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ultralytics 8.4.0 YOLO26 Models Release (#23176)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
1a50655c75
commit
f2d3aed634
323 changed files with 11710 additions and 7454 deletions
8
.github/workflows/ci.yml
vendored
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.github/workflows/ci.yml
vendored
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@ -210,7 +210,9 @@ jobs:
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run: uv cache prune --ci
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SlowTests:
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if: (github.event_name == 'workflow_dispatch' && github.event.inputs.tests == 'true') || github.event_name == 'schedule'
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# TODO: Tests disabled to debug YOLO26 compatiblility
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# if: (github.event_name == 'workflow_dispatch' && github.event.inputs.tests == 'true') || github.event_name == 'schedule'
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if: false
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timeout-minutes: 360
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runs-on: ${{ matrix.os }}
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strategy:
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@ -343,7 +345,9 @@ jobs:
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CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
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RaspberryPi:
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if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event.inputs.raspberrypi == 'true')
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# TODO: Tests disabled to debug YOLO26 compatiblility
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# if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event.inputs.raspberrypi == 'true')
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if: false
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timeout-minutes: 120
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runs-on: raspberry-pi
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steps:
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68
README.md
68
README.md
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@ -9,7 +9,6 @@
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<div>
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<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg" alt="Ultralytics CI"></a>
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<a href="https://clickpy.clickhouse.com/dashboard/ultralytics"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></a>
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<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLO Citation"></a>
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<a href="https://discord.com/invite/ultralytics"><img alt="Ultralytics Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
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<a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>
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<a href="https://www.reddit.com/r/ultralytics/"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
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@ -77,8 +76,8 @@ For alternative installation methods, including [Conda](https://anaconda.org/con
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You can use Ultralytics YOLO directly from the Command Line Interface (CLI) with the `yolo` command:
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```bash
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# Predict using a pretrained YOLO model (e.g., YOLO11n) on an image
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yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
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# Predict using a pretrained YOLO model (e.g., YOLO26n) on an image
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yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'
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```
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The `yolo` command supports various tasks and modes, accepting additional arguments like `imgsz=640`. Explore the YOLO [CLI Docs](https://docs.ultralytics.com/usage/cli/) for more examples.
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@ -90,8 +89,8 @@ Ultralytics YOLO can also be integrated directly into your Python projects. It a
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```python
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from ultralytics import YOLO
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# Load a pretrained YOLO11n model
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model = YOLO("yolo11n.pt")
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# Load a pretrained YOLO26n model
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model = YOLO("yolo26n.pt")
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# Train the model on the COCO8 dataset for 100 epochs
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train_results = model.train(
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@ -118,7 +117,7 @@ Discover more examples in the YOLO [Python Docs](https://docs.ultralytics.com/us
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## ✨ Models
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Ultralytics supports a wide range of YOLO models, from early versions like [YOLOv3](https://docs.ultralytics.com/models/yolov3/) to the latest [YOLO11](https://docs.ultralytics.com/models/yolo11/). The tables below showcase YOLO11 models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset for [Detection](https://docs.ultralytics.com/tasks/detect/), [Segmentation](https://docs.ultralytics.com/tasks/segment/), and [Pose Estimation](https://docs.ultralytics.com/tasks/pose/). Additionally, [Classification](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset are available. [Tracking](https://docs.ultralytics.com/modes/track/) mode is compatible with all Detection, Segmentation, and Pose models. All [Models](https://docs.ultralytics.com/models/) are automatically downloaded from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) upon first use.
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Ultralytics supports a wide range of YOLO models, from early versions like [YOLOv3](https://docs.ultralytics.com/models/yolov3/) to the latest [YOLO26](https://docs.ultralytics.com/models/yolo26/). The tables below showcase YOLO26 models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset for [Detection](https://docs.ultralytics.com/tasks/detect/), [Segmentation](https://docs.ultralytics.com/tasks/segment/), and [Pose Estimation](https://docs.ultralytics.com/tasks/pose/). Additionally, [Classification](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset are available. [Tracking](https://docs.ultralytics.com/modes/track/) mode is compatible with all Detection, Segmentation, and Pose models. All [Models](https://docs.ultralytics.com/models/) are automatically downloaded from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) upon first use.
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<a href="https://docs.ultralytics.com/tasks/" target="_blank">
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<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-tasks-banner.avif" alt="Ultralytics YOLO supported tasks">
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@ -132,11 +131,11 @@ Explore the [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usa
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| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt) | 640 | 39.5 | 56.1 ± 0.8 | 1.5 ± 0.0 | 2.6 | 6.5 |
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| [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | 640 | 47.0 | 90.0 ± 1.2 | 2.5 ± 0.0 | 9.4 | 21.5 |
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| [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt) | 640 | 51.5 | 183.2 ± 2.0 | 4.7 ± 0.1 | 20.1 | 68.0 |
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| [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt) | 640 | 53.4 | 238.6 ± 1.4 | 6.2 ± 0.1 | 25.3 | 86.9 |
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| [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt) | 640 | 54.7 | 462.8 ± 6.7 | 11.3 ± 0.2 | 56.9 | 194.9 |
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| [YOLO26n](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt) | 640 | 40.9 | 38.9 ± 0.7 | 1.7 ± 0.0 | 2.4 | 5.4 |
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| [YOLO26s](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s.pt) | 640 | 48.6 | 87.2 ± 0.9 | 2.5 ± 0.0 | 9.5 | 20.7 |
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| [YOLO26m](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m.pt) | 640 | 53.1 | 220.0 ± 1.4 | 4.7 ± 0.1 | 20.4 | 68.2 |
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| [YOLO26l](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l.pt) | 640 | 55.0 | 286.2 ± 2.0 | 6.2 ± 0.2 | 24.8 | 86.4 |
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| [YOLO26x](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x.pt) | 640 | 57.5 | 525.8 ± 4.0 | 11.8 ± 0.2 | 55.7 | 193.9 |
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- **mAP<sup>val</sup>** values refer to single-model single-scale performance on the [COCO val2017](https://cocodataset.org/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details. <br>Reproduce with `yolo val detect data=coco.yaml device=0`
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- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val detect data=coco.yaml batch=1 device=0|cpu`
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@ -149,11 +148,11 @@ Refer to the [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) fo
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| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLO11n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt) | 640 | 38.9 | 32.0 | 65.9 ± 1.1 | 1.8 ± 0.0 | 2.9 | 9.7 |
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| [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640 | 46.6 | 37.8 | 117.6 ± 4.9 | 2.9 ± 0.0 | 10.1 | 33.0 |
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| [YOLO11m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt) | 640 | 51.5 | 41.5 | 281.6 ± 1.2 | 6.3 ± 0.1 | 22.4 | 113.2 |
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| [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640 | 53.4 | 42.9 | 344.2 ± 3.2 | 7.8 ± 0.2 | 27.6 | 132.2 |
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| [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640 | 54.7 | 43.8 | 664.5 ± 3.2 | 15.8 ± 0.7 | 62.1 | 296.4 |
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| [YOLO26n-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-seg.pt) | 640 | 39.6 | 33.9 | 53.3 ± 0.5 | 2.1 ± 0.0 | 2.8 | 9.1 |
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| [YOLO26s-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-seg.pt) | 640 | 47.3 | 40.0 | 118.4 ± 0.9 | 3.3 ± 0.0 | 10.7 | 34.2 |
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| [YOLO26m-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-seg.pt) | 640 | 52.5 | 44.1 | 328.2 ± 2.4 | 6.7 ± 0.1 | 24.8 | 121.5 |
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| [YOLO26l-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-seg.pt) | 640 | 54.4 | 45.5 | 387.0 ± 3.7 | 8.0 ± 0.1 | 29.2 | 139.8 |
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| [YOLO26x-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-seg.pt) | 640 | 56.5 | 47.0 | 787.0 ± 6.8 | 16.4 ± 0.1 | 65.5 | 313.5 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on the [COCO val2017](https://cocodataset.org/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details. <br>Reproduce with `yolo val segment data=coco.yaml device=0`
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- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val segment data=coco.yaml batch=1 device=0|cpu`
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@ -166,11 +165,11 @@ Consult the [Classification Docs](https://docs.ultralytics.com/tasks/classify/)
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| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 224 |
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| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
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| [YOLO11n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt) | 224 | 70.0 | 89.4 | 5.0 ± 0.3 | 1.1 ± 0.0 | 2.8 | 0.5 |
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| [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224 | 75.4 | 92.7 | 7.9 ± 0.2 | 1.3 ± 0.0 | 6.7 | 1.6 |
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| [YOLO11m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-cls.pt) | 224 | 77.3 | 93.9 | 17.2 ± 0.4 | 2.0 ± 0.0 | 11.6 | 4.9 |
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| [YOLO11l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-cls.pt) | 224 | 78.3 | 94.3 | 23.2 ± 0.3 | 2.8 ± 0.0 | 14.1 | 6.2 |
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| [YOLO11x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-cls.pt) | 224 | 79.5 | 94.9 | 41.4 ± 0.9 | 3.8 ± 0.0 | 29.6 | 13.6 |
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| [YOLO26n-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-cls.pt) | 224 | 71.4 | 90.1 | 5.0 ± 0.3 | 1.1 ± 0.0 | 2.8 | 0.5 |
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| [YOLO26s-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-cls.pt) | 224 | 76.0 | 92.9 | 7.9 ± 0.2 | 1.3 ± 0.0 | 6.7 | 1.6 |
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| [YOLO26m-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-cls.pt) | 224 | 78.1 | 94.2 | 17.2 ± 0.4 | 2.0 ± 0.0 | 11.6 | 4.9 |
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| [YOLO26l-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-cls.pt) | 224 | 79.0 | 94.6 | 23.2 ± 0.3 | 2.8 ± 0.0 | 14.1 | 6.2 |
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| [YOLO26x-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-cls.pt) | 224 | 79.9 | 95.0 | 41.4 ± 0.9 | 3.8 ± 0.0 | 29.6 | 13.6 |
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- **acc** values represent model accuracy on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce with `yolo val classify data=path/to/ImageNet device=0`
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- **Speed** metrics are averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
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@ -183,11 +182,11 @@ See the [Pose Estimation Docs](https://docs.ultralytics.com/tasks/pose/) for usa
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| Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ---------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLO11n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt) | 640 | 50.0 | 81.0 | 52.4 ± 0.5 | 1.7 ± 0.0 | 2.9 | 7.4 |
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| [YOLO11s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt) | 640 | 58.9 | 86.3 | 90.5 ± 0.6 | 2.6 ± 0.0 | 9.9 | 23.1 |
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| [YOLO11m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-pose.pt) | 640 | 64.9 | 89.4 | 187.3 ± 0.8 | 4.9 ± 0.1 | 20.9 | 71.4 |
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| [YOLO11l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt) | 640 | 66.1 | 89.9 | 247.7 ± 1.1 | 6.4 ± 0.1 | 26.1 | 90.3 |
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| [YOLO11x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose.pt) | 640 | 69.5 | 91.1 | 488.0 ± 13.9 | 12.1 ± 0.2 | 58.8 | 202.8 |
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| [YOLO26n-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-pose.pt) | 640 | 56.9 | 83.0 | 40.3 ± 0.5 | 1.8 ± 0.0 | 2.9 | 7.5 |
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| [YOLO26s-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-pose.pt) | 640 | 63.1 | 86.8 | 85.3 ± 0.9 | 2.7 ± 0.0 | 10.4 | 23.9 |
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| [YOLO26m-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-pose.pt) | 640 | 68.8 | 89.9 | 218.0 ± 1.5 | 5.0 ± 0.1 | 21.5 | 73.1 |
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| [YOLO26l-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-pose.pt) | 640 | 70.4 | 90.8 | 275.4 ± 2.4 | 6.5 ± 0.1 | 25.9 | 91.3 |
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| [YOLO26x-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-pose.pt) | 640 | 71.7 | 91.6 | 565.4 ± 3.0 | 12.2 ± 0.2 | 57.6 | 201.7 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on the [COCO Keypoints val2017](https://docs.ultralytics.com/datasets/pose/coco/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details. <br>Reproduce with `yolo val pose data=coco-pose.yaml device=0`
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- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
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@ -200,11 +199,11 @@ Check the [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples
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| Model | size<br><sup>(pixels) | mAP<sup>test<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| -------------------------------------------------------------------------------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLO11n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-obb.pt) | 1024 | 78.4 | 117.6 ± 0.8 | 4.4 ± 0.0 | 2.7 | 16.8 |
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| [YOLO11s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-obb.pt) | 1024 | 79.5 | 219.4 ± 4.0 | 5.1 ± 0.0 | 9.7 | 57.1 |
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| [YOLO11m-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-obb.pt) | 1024 | 80.9 | 562.8 ± 2.9 | 10.1 ± 0.4 | 20.9 | 182.8 |
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| [YOLO11l-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-obb.pt) | 1024 | 81.0 | 712.5 ± 5.0 | 13.5 ± 0.6 | 26.1 | 231.2 |
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| [YOLO11x-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-obb.pt) | 1024 | 81.3 | 1408.6 ± 7.7 | 28.6 ± 1.0 | 58.8 | 519.1 |
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| [YOLO26n-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-obb.pt) | 1024 | 78.9 | 97.7 ± 0.9 | 2.8 ± 0.0 | 2.5 | 14.0 |
|
||||
| [YOLO26s-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-obb.pt) | 1024 | 79.8 | 218.0 ± 1.4 | 4.9 ± 0.1 | 9.8 | 55.1 |
|
||||
| [YOLO26m-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-obb.pt) | 1024 | 81.0 | 579.2 ± 3.8 | 10.2 ± 0.3 | 21.2 | 183.3 |
|
||||
| [YOLO26l-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-obb.pt) | 1024 | 81.4 | 735.6 ± 3.1 | 13.0 ± 0.2 | 25.6 | 230.0 |
|
||||
| [YOLO26x-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-obb.pt) | 1024 | 82.1 | 1485.7 ± 11.5 | 30.5 ± 0.9 | 57.6 | 516.5 |
|
||||
|
||||
- **mAP<sup>test</sup>** values are for single-model multiscale performance on the [DOTAv1 test set](https://captain-whu.github.io/DOTA/dataset.html). <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to the [DOTA evaluation server](https://captain-whu.github.io/DOTA/evaluation.html).
|
||||
- **Speed** metrics are averaged over [DOTAv1 val images](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10) using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
|
||||
|
|
@ -239,13 +238,6 @@ Our key integrations with leading AI platforms extend the functionality of Ultra
|
|||
| :-----------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: |
|
||||
| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://hub.ultralytics.com/). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/). | Free forever, [Comet ML](https://docs.ultralytics.com/integrations/comet/) lets you save YOLO models, resume training, and interactively visualize predictions. | Run YOLO inference up to 6x faster with [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/). |
|
||||
|
||||
## 🌟 Ultralytics HUB
|
||||
|
||||
Experience seamless AI with [Ultralytics HUB](https://hub.ultralytics.com/), the all-in-one platform for data visualization, training YOLO models, and deployment—no coding required. Transform images into actionable insights and bring your AI visions to life effortlessly using our cutting-edge platform and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** today!
|
||||
|
||||
<a href="https://www.ultralytics.com/hub" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
|
||||
|
||||
## 🤝 Contribute
|
||||
|
||||
We thrive on community collaboration! Ultralytics YOLO wouldn't be the SOTA framework it is without contributions from developers like you. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. We also welcome your feedback—share your experience by completing our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A huge **Thank You** 🙏 to everyone who contributes!
|
||||
|
|
|
|||
|
|
@ -9,7 +9,6 @@
|
|||
<div>
|
||||
<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg" alt="Ultralytics CI"></a>
|
||||
<a href="https://clickpy.clickhouse.com/dashboard/ultralytics"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></a>
|
||||
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLO Citation"></a>
|
||||
<a href="https://discord.com/invite/ultralytics"><img alt="Ultralytics Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
|
||||
<a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>
|
||||
<a href="https://www.reddit.com/r/ultralytics/"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
|
||||
|
|
@ -77,8 +76,8 @@ pip install ultralytics
|
|||
您可以直接通过命令行界面 (CLI) 使用 `yolo` 命令来运行 Ultralytics YOLO：
|
||||
|
||||
```bash
|
||||
# 使用预训练的 YOLO 模型 (例如 YOLO11n) 对图像进行预测
|
||||
yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
|
||||
# 使用预训练的 YOLO 模型 (例如 YOLO26n) 对图像进行预测
|
||||
yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'
|
||||
```
|
||||
|
||||
`yolo` 命令支持各种任务和模式，并接受额外的参数，如 `imgsz=640`。浏览 YOLO [CLI 文档](https://docs.ultralytics.com/usage/cli/)获取更多示例。
|
||||
|
|
@ -90,8 +89,8 @@ Ultralytics YOLO 也可以直接集成到您的 Python 项目中。它接受与
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# 加载一个预训练的 YOLO11n 模型
|
||||
model = YOLO("yolo11n.pt")
|
||||
# 加载一个预训练的 YOLO26n 模型
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# 在 COCO8 数据集上训练模型 100 个周期
|
||||
train_results = model.train(
|
||||
|
|
@ -118,7 +117,7 @@ path = model.export(format="onnx") # 返回导出模型的路径
|
|||
|
||||
## ✨ 模型
|
||||
|
||||
Ultralytics 支持广泛的 YOLO 模型，从早期的版本如 [YOLOv3](https://docs.ultralytics.com/models/yolov3/) 到最新的 [YOLO11](https://docs.ultralytics.com/models/yolo11/)。下表展示了在 [COCO](https://docs.ultralytics.com/datasets/detect/coco/) 数据集上预训练的 YOLO11 模型，用于[检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://docs.ultralytics.com/tasks/segment/)和[姿态估计](https://docs.ultralytics.com/tasks/pose/)任务。此外，还提供了在 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 数据集上预训练的[分类](https://docs.ultralytics.com/tasks/classify/)模型。[跟踪](https://docs.ultralytics.com/modes/track/)模式与所有检测、分割和姿态模型兼容。所有[模型](https://docs.ultralytics.com/models/)在首次使用时都会自动从最新的 Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)下载。
|
||||
Ultralytics 支持广泛的 YOLO 模型，从早期的版本如 [YOLOv3](https://docs.ultralytics.com/models/yolov3/) 到最新的 [YOLO26](https://docs.ultralytics.com/models/yolo26/)。下表展示了在 [COCO](https://docs.ultralytics.com/datasets/detect/coco/) 数据集上预训练的 YOLO26 模型，用于[检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://docs.ultralytics.com/tasks/segment/)和[姿态估计](https://docs.ultralytics.com/tasks/pose/)任务。此外，还提供了在 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 数据集上预训练的[分类](https://docs.ultralytics.com/tasks/classify/)模型。[跟踪](https://docs.ultralytics.com/modes/track/)模式与所有检测、分割和姿态模型兼容。所有[模型](https://docs.ultralytics.com/models/)在首次使用时都会自动从最新的 Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)下载。
|
||||
|
||||
<a href="https://docs.ultralytics.com/tasks/" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-tasks-banner.avif" alt="Ultralytics YOLO supported tasks">
|
||||
|
|
@ -132,11 +131,11 @@ Ultralytics 支持广泛的 YOLO 模型，从早期的版本如 [YOLOv3](https:/
|
|||
|
||||
| 模型 | 尺寸<br><sup>(像素) | mAP<sup>val<br>50-95 | 速度<br><sup>CPU ONNX<br>(毫秒) | 速度<br><sup>T4 TensorRT10<br>(毫秒) | 参数<br><sup>(百万) | FLOPs<br><sup>(十亿) |
|
||||
| ------------------------------------------------------------------------------------ | ------------------- | -------------------- | ------------------------------- | ------------------------------------ | ------------------- | -------------------- |
|
||||
| [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt) | 640 | 39.5 | 56.1 ± 0.8 | 1.5 ± 0.0 | 2.6 | 6.5 |
|
||||
| [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | 640 | 47.0 | 90.0 ± 1.2 | 2.5 ± 0.0 | 9.4 | 21.5 |
|
||||
| [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt) | 640 | 51.5 | 183.2 ± 2.0 | 4.7 ± 0.1 | 20.1 | 68.0 |
|
||||
| [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt) | 640 | 53.4 | 238.6 ± 1.4 | 6.2 ± 0.1 | 25.3 | 86.9 |
|
||||
| [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt) | 640 | 54.7 | 462.8 ± 6.7 | 11.3 ± 0.2 | 56.9 | 194.9 |
|
||||
| [YOLO26n](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt) | 640 | 40.9 | 38.9 ± 0.7 | 1.7 ± 0.0 | 2.4 | 5.4 |
|
||||
| [YOLO26s](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s.pt) | 640 | 48.6 | 87.2 ± 0.9 | 2.5 ± 0.0 | 9.5 | 20.7 |
|
||||
| [YOLO26m](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m.pt) | 640 | 53.1 | 220.0 ± 1.4 | 4.7 ± 0.1 | 20.4 | 68.2 |
|
||||
| [YOLO26l](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l.pt) | 640 | 55.0 | 286.2 ± 2.0 | 6.2 ± 0.2 | 24.8 | 86.4 |
|
||||
| [YOLO26x](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x.pt) | 640 | 57.5 | 525.8 ± 4.0 | 11.8 ± 0.2 | 55.7 | 193.9 |
|
||||
|
||||
- **mAP<sup>val</sup>** 值指的是在 [COCO val2017](https://cocodataset.org/) 数据集上的单模型单尺度性能。详见 [YOLO 性能指标](https://docs.ultralytics.com/guides/yolo-performance-metrics/)。<br>使用 `yolo val detect data=coco.yaml device=0` 复现结果。
|
||||
- **速度** 指标是在 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例上对 COCO val 图像进行平均测量的。CPU 速度使用 [ONNX](https://onnx.ai/) 导出进行测量。GPU 速度使用 [TensorRT](https://developer.nvidia.com/tensorrt) 导出进行测量。<br>使用 `yolo val detect data=coco.yaml batch=1 device=0|cpu` 复现结果。
|
||||
|
|
@ -149,11 +148,11 @@ Ultralytics 支持广泛的 YOLO 模型，从早期的版本如 [YOLOv3](https:/
|
|||
|
||||
| 模型 | 尺寸<br><sup>(像素) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 速度<br><sup>CPU ONNX<br>(毫秒) | 速度<br><sup>T4 TensorRT10<br>(毫秒) | 参数<br><sup>(百万) | FLOPs<br><sup>(十亿) |
|
||||
| -------------------------------------------------------------------------------------------- | ------------------- | -------------------- | --------------------- | ------------------------------- | ------------------------------------ | ------------------- | -------------------- |
|
||||
| [YOLO11n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt) | 640 | 38.9 | 32.0 | 65.9 ± 1.1 | 1.8 ± 0.0 | 2.9 | 9.7 |
|
||||
| [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640 | 46.6 | 37.8 | 117.6 ± 4.9 | 2.9 ± 0.0 | 10.1 | 33.0 |
|
||||
| [YOLO11m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt) | 640 | 51.5 | 41.5 | 281.6 ± 1.2 | 6.3 ± 0.1 | 22.4 | 113.2 |
|
||||
| [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640 | 53.4 | 42.9 | 344.2 ± 3.2 | 7.8 ± 0.2 | 27.6 | 132.2 |
|
||||
| [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640 | 54.7 | 43.8 | 664.5 ± 3.2 | 15.8 ± 0.7 | 62.1 | 296.4 |
|
||||
| [YOLO26n-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-seg.pt) | 640 | 39.6 | 33.9 | 53.3 ± 0.5 | 2.1 ± 0.0 | 2.8 | 9.1 |
|
||||
| [YOLO26s-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-seg.pt) | 640 | 47.3 | 40.0 | 118.4 ± 0.9 | 3.3 ± 0.0 | 10.7 | 34.2 |
|
||||
| [YOLO26m-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-seg.pt) | 640 | 52.5 | 44.1 | 328.2 ± 2.4 | 6.7 ± 0.1 | 24.8 | 121.5 |
|
||||
| [YOLO26l-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-seg.pt) | 640 | 54.4 | 45.5 | 387.0 ± 3.7 | 8.0 ± 0.1 | 29.2 | 139.8 |
|
||||
| [YOLO26x-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-seg.pt) | 640 | 56.5 | 47.0 | 787.0 ± 6.8 | 16.4 ± 0.1 | 65.5 | 313.5 |
|
||||
|
||||
- **mAP<sup>val</sup>** 值指的是在 [COCO val2017](https://cocodataset.org/) 数据集上的单模型单尺度性能。详见 [YOLO 性能指标](https://docs.ultralytics.com/guides/yolo-performance-metrics/)。<br>使用 `yolo val segment data=coco.yaml device=0` 复现结果。
|
||||
- **速度** 指标是在 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例上对 COCO val 图像进行平均测量的。CPU 速度使用 [ONNX](https://onnx.ai/) 导出进行测量。GPU 速度使用 [TensorRT](https://developer.nvidia.com/tensorrt) 导出进行测量。<br>使用 `yolo val segment data=coco.yaml batch=1 device=0|cpu` 复现结果。
|
||||
|
|
@ -166,11 +165,11 @@ Ultralytics 支持广泛的 YOLO 模型，从早期的版本如 [YOLOv3](https:/
|
|||
|
||||
| 模型 | 尺寸<br><sup>(像素) | acc<br><sup>top1 | acc<br><sup>top5 | 速度<br><sup>CPU ONNX<br>(毫秒) | 速度<br><sup>T4 TensorRT10<br>(毫秒) | 参数<br><sup>(百万) | FLOPs<br><sup>(十亿) @ 224 |
|
||||
| -------------------------------------------------------------------------------------------- | ------------------- | ---------------- | ---------------- | ------------------------------- | ------------------------------------ | ------------------- | -------------------------- |
|
||||
| [YOLO11n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt) | 224 | 70.0 | 89.4 | 5.0 ± 0.3 | 1.1 ± 0.0 | 2.8 | 0.5 |
|
||||
| [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224 | 75.4 | 92.7 | 7.9 ± 0.2 | 1.3 ± 0.0 | 6.7 | 1.6 |
|
||||
| [YOLO11m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-cls.pt) | 224 | 77.3 | 93.9 | 17.2 ± 0.4 | 2.0 ± 0.0 | 11.6 | 4.9 |
|
||||
| [YOLO11l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-cls.pt) | 224 | 78.3 | 94.3 | 23.2 ± 0.3 | 2.8 ± 0.0 | 14.1 | 6.2 |
|
||||
| [YOLO11x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-cls.pt) | 224 | 79.5 | 94.9 | 41.4 ± 0.9 | 3.8 ± 0.0 | 29.6 | 13.6 |
|
||||
| [YOLO26n-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-cls.pt) | 224 | 71.4 | 90.1 | 5.0 ± 0.3 | 1.1 ± 0.0 | 2.8 | 0.5 |
|
||||
| [YOLO26s-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-cls.pt) | 224 | 76.0 | 92.9 | 7.9 ± 0.2 | 1.3 ± 0.0 | 6.7 | 1.6 |
|
||||
| [YOLO26m-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-cls.pt) | 224 | 78.1 | 94.2 | 17.2 ± 0.4 | 2.0 ± 0.0 | 11.6 | 4.9 |
|
||||
| [YOLO26l-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-cls.pt) | 224 | 79.0 | 94.6 | 23.2 ± 0.3 | 2.8 ± 0.0 | 14.1 | 6.2 |
|
||||
| [YOLO26x-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-cls.pt) | 224 | 79.9 | 95.0 | 41.4 ± 0.9 | 3.8 ± 0.0 | 29.6 | 13.6 |
|
||||
|
||||
- **acc** 值表示模型在 [ImageNet](https://www.image-net.org/) 数据集验证集上的准确率。<br>使用 `yolo val classify data=path/to/ImageNet device=0` 复现结果。
|
||||
- **速度** 指标是在 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例上对 ImageNet val 图像进行平均测量的。CPU 速度使用 [ONNX](https://onnx.ai/) 导出进行测量。GPU 速度使用 [TensorRT](https://developer.nvidia.com/tensorrt) 导出进行测量。<br>使用 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu` 复现结果。
|
||||
|
|
@ -183,11 +182,11 @@ Ultralytics 支持广泛的 YOLO 模型，从早期的版本如 [YOLOv3](https:/
|
|||
|
||||
| 模型 | 尺寸<br><sup>(像素) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | 速度<br><sup>CPU ONNX<br>(毫秒) | 速度<br><sup>T4 TensorRT10<br>(毫秒) | 参数<br><sup>(百万) | FLOPs<br><sup>(十亿) |
|
||||
| ---------------------------------------------------------------------------------------------- | ------------------- | --------------------- | ------------------ | ------------------------------- | ------------------------------------ | ------------------- | -------------------- |
|
||||
| [YOLO11n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt) | 640 | 50.0 | 81.0 | 52.4 ± 0.5 | 1.7 ± 0.0 | 2.9 | 7.4 |
|
||||
| [YOLO11s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt) | 640 | 58.9 | 86.3 | 90.5 ± 0.6 | 2.6 ± 0.0 | 9.9 | 23.1 |
|
||||
| [YOLO11m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-pose.pt) | 640 | 64.9 | 89.4 | 187.3 ± 0.8 | 4.9 ± 0.1 | 20.9 | 71.4 |
|
||||
| [YOLO11l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt) | 640 | 66.1 | 89.9 | 247.7 ± 1.1 | 6.4 ± 0.1 | 26.1 | 90.3 |
|
||||
| [YOLO11x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose.pt) | 640 | 69.5 | 91.1 | 488.0 ± 13.9 | 12.1 ± 0.2 | 58.8 | 202.8 |
|
||||
| [YOLO26n-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-pose.pt) | 640 | 56.9 | 83.0 | 40.3 ± 0.5 | 1.8 ± 0.0 | 2.9 | 7.5 |
|
||||
| [YOLO26s-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-pose.pt) | 640 | 63.1 | 86.8 | 85.3 ± 0.9 | 2.7 ± 0.0 | 10.4 | 23.9 |
|
||||
| [YOLO26m-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-pose.pt) | 640 | 68.8 | 89.9 | 218.0 ± 1.5 | 5.0 ± 0.1 | 21.5 | 73.1 |
|
||||
| [YOLO26l-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-pose.pt) | 640 | 70.4 | 90.8 | 275.4 ± 2.4 | 6.5 ± 0.1 | 25.9 | 91.3 |
|
||||
| [YOLO26x-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-pose.pt) | 640 | 71.7 | 91.6 | 565.4 ± 3.0 | 12.2 ± 0.2 | 57.6 | 201.7 |
|
||||
|
||||
- **mAP<sup>val</sup>** 值指的是在 [COCO Keypoints val2017](https://docs.ultralytics.com/datasets/pose/coco/) 数据集上的单模型单尺度性能。详见 [YOLO 性能指标](https://docs.ultralytics.com/guides/yolo-performance-metrics/)。<br>使用 `yolo val pose data=coco-pose.yaml device=0` 复现结果。
|
||||
- **速度** 指标是在 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例上对 COCO val 图像进行平均测量的。CPU 速度使用 [ONNX](https://onnx.ai/) 导出进行测量。GPU 速度使用 [TensorRT](https://developer.nvidia.com/tensorrt) 导出进行测量。<br>使用 `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu` 复现结果。
|
||||
|
|
@ -200,11 +199,11 @@ Ultralytics 支持广泛的 YOLO 模型，从早期的版本如 [YOLOv3](https:/
|
|||
|
||||
| 模型 | 尺寸<br><sup>(像素) | mAP<sup>test<br>50 | 速度<br><sup>CPU ONNX<br>(毫秒) | 速度<br><sup>T4 TensorRT10<br>(毫秒) | 参数<br><sup>(百万) | FLOPs<br><sup>(十亿) |
|
||||
| -------------------------------------------------------------------------------------------- | ------------------- | ------------------ | ------------------------------- | ------------------------------------ | ------------------- | -------------------- |
|
||||
| [YOLO11n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-obb.pt) | 1024 | 78.4 | 117.6 ± 0.8 | 4.4 ± 0.0 | 2.7 | 16.8 |
|
||||
| [YOLO11s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-obb.pt) | 1024 | 79.5 | 219.4 ± 4.0 | 5.1 ± 0.0 | 9.7 | 57.1 |
|
||||
| [YOLO11m-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-obb.pt) | 1024 | 80.9 | 562.8 ± 2.9 | 10.1 ± 0.4 | 20.9 | 182.8 |
|
||||
| [YOLO11l-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-obb.pt) | 1024 | 81.0 | 712.5 ± 5.0 | 13.5 ± 0.6 | 26.1 | 231.2 |
|
||||
| [YOLO11x-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-obb.pt) | 1024 | 81.3 | 1408.6 ± 7.7 | 28.6 ± 1.0 | 58.8 | 519.1 |
|
||||
| [YOLO26n-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-obb.pt) | 1024 | 78.9 | 97.7 ± 0.9 | 2.8 ± 0.0 | 2.5 | 14.0 |
|
||||
| [YOLO26s-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-obb.pt) | 1024 | 79.8 | 218.0 ± 1.4 | 4.9 ± 0.1 | 9.8 | 55.1 |
|
||||
| [YOLO26m-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-obb.pt) | 1024 | 81.0 | 579.2 ± 3.8 | 10.2 ± 0.3 | 21.2 | 183.3 |
|
||||
| [YOLO26l-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-obb.pt) | 1024 | 81.4 | 735.6 ± 3.1 | 13.0 ± 0.2 | 25.6 | 230.0 |
|
||||
| [YOLO26x-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-obb.pt) | 1024 | 82.1 | 1485.7 ± 11.5 | 30.5 ± 0.9 | 57.6 | 516.5 |
|
||||
|
||||
- **mAP<sup>test</sup>** 值指的是在 [DOTAv1 测试集](https://captain-whu.github.io/DOTA/dataset.html)上的单模型多尺度性能。<br>通过 `yolo val obb data=DOTAv1.yaml device=0 split=test` 复现结果，并将合并后的结果提交到 [DOTA 评估服务器](https://captain-whu.github.io/DOTA/evaluation.html)。
|
||||
- **速度** 指标是在 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例上对 [DOTAv1 val 图像](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10)进行平均测量的。CPU 速度使用 [ONNX](https://onnx.ai/) 导出进行测量。GPU 速度使用 [TensorRT](https://developer.nvidia.com/tensorrt) 导出进行测量。<br>通过 `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu` 复现结果。
|
||||
|
|
@ -239,13 +238,6 @@ Ultralytics 支持广泛的 YOLO 模型，从早期的版本如 [YOLOv3](https:/
|
|||
| :-----------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------: |
|
||||
| 简化 YOLO 工作流程：使用 [Ultralytics HUB](https://hub.ultralytics.com/) 轻松进行标注、训练和部署。立即试用！ | 使用 [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/) 跟踪实验、超参数和结果。 | 永久免费的 [Comet ML](https://docs.ultralytics.com/integrations/comet/) 让您能够保存 YOLO 模型、恢复训练并交互式地可视化预测结果。 | 使用 [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/)，将 YOLO 推理速度提高多达 6 倍。 |
|
||||
|
||||
## 🌟 Ultralytics HUB
|
||||
|
||||
通过 [Ultralytics HUB](https://hub.ultralytics.com/) 体验无缝 AI，这是一个集数据可视化、训练 YOLO 模型和部署于一体的平台——无需编码。使用我们尖端的平台和用户友好的 [Ultralytics App](https://www.ultralytics.com/app-install)，轻松将图像转化为可操作的见解，并将您的 AI 愿景变为现实。立即**免费**开始您的旅程！
|
||||
|
||||
<a href="https://www.ultralytics.com/hub" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
|
||||
|
||||
## 🤝 贡献
|
||||
|
||||
我们依靠社区协作蓬勃发展！没有像您这样的开发者的贡献，Ultralytics YOLO 就不会成为如今最先进的框架。请参阅我们的[贡献指南](https://docs.ultralytics.com/help/contributing/)开始贡献。我们也欢迎您的反馈——通过完成我们的[调查问卷](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey)分享您的体验。非常**感谢** 🙏 每一位贡献者！
|
||||
|
|
|
|||
|
|
@ -39,7 +39,7 @@ WORKDIR /ultralytics
|
|||
COPY . .
|
||||
RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
|
||||
sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt .
|
||||
|
||||
# Install pip packages (uv already installed in base image)
|
||||
RUN uv pip install --system -e "." albumentations faster-coco-eval wandb && \
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@ WORKDIR /ultralytics
|
|||
COPY . .
|
||||
RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
|
||||
sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt .
|
||||
|
||||
# Install pip packages, create python symlink, and remove build files
|
||||
RUN python3 -m pip install uv && \
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@ RUN apt-get update && \
|
|||
rm -rf /var/lib/apt/lists/* /root/.config/Ultralytics/persistent_cache.json
|
||||
|
||||
# Copy model
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt .
|
||||
|
||||
# Usage --------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
|
|
|||
|
|
@ -11,8 +11,8 @@ FROM ultralytics/ultralytics:latest
|
|||
# Note tensorrt installed on-demand as depends on runtime environment CUDA version
|
||||
RUN uv pip install --system -e ".[export]" "onnxruntime-gpu" paddlepaddle x2paddle numpy==1.26.4 && \
|
||||
# Run exports to AutoInstall packages \
|
||||
yolo export model=tmp/yolo11n.pt format=edgetpu imgsz=32 && \
|
||||
yolo export model=tmp/yolo11n.pt format=ncnn imgsz=32 && \
|
||||
yolo export model=tmp/yolo26n.pt format=edgetpu imgsz=32 && \
|
||||
yolo export model=tmp/yolo26n.pt format=ncnn imgsz=32 && \
|
||||
# Remove temporary files \
|
||||
rm -rf tmp /root/.config/Ultralytics/persistent_cache.json
|
||||
|
||||
|
|
|
|||
|
|
@ -40,7 +40,7 @@ WORKDIR /ultralytics
|
|||
COPY . .
|
||||
RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
|
||||
sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt .
|
||||
|
||||
# Replace pyproject.toml TF.js version with 'tensorflowjs>=3.9.0' for JetPack4 compatibility
|
||||
RUN sed -i 's/^\( *"tensorflowjs\)>=.*\(".*\)/\1>=3.9.0\2/' pyproject.toml
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@ WORKDIR /ultralytics
|
|||
COPY . .
|
||||
RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
|
||||
sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt .
|
||||
|
||||
# Replace pyproject.toml TF.js version with 'tensorflowjs>=3.9.0' for JetPack5 compatibility and install packages
|
||||
RUN sed -i 's/^\( *"tensorflowjs\)>=.*\(".*\)/\1>=3.9.0\2/' pyproject.toml && \
|
||||
|
|
|
|||
|
|
@ -33,7 +33,7 @@ WORKDIR /ultralytics
|
|||
COPY . .
|
||||
RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
|
||||
sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt .
|
||||
|
||||
# Pip install onnxruntime-gpu, torch, torchvision and ultralytics, then remove build files
|
||||
RUN python3 -m pip install --upgrade pip uv && \
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ WORKDIR /ultralytics
|
|||
COPY . .
|
||||
RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
|
||||
sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt .
|
||||
|
||||
# Install pip packages (uv already installed in base image)
|
||||
RUN uv pip install --system \
|
||||
|
|
@ -48,4 +48,4 @@ RUN uv pip install --system \
|
|||
# Example (push): docker push $t
|
||||
# Example (pull): t=ultralytics/ultralytics:latest-nvidia-arm64 && docker pull $t
|
||||
# Example (run): docker run -it --ipc=host --runtime=nvidia $t
|
||||
# Example (run-with-volume): docker run -it --ipc=host --runtime=nvidia -v "$PWD/shared/datasets:/datasets" $t && docker push $tnew
|
||||
# Example (run-with-volume): docker run -it --ipc=host --runtime=nvidia -v "$PWD/shared/datasets:/datasets" $t && docker push $tnew
|
||||
|
|
|
|||
|
|
@ -32,7 +32,7 @@ WORKDIR /ultralytics
|
|||
COPY . .
|
||||
RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
|
||||
sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt .
|
||||
|
||||
# Install pip packages
|
||||
RUN pip install uv && \
|
||||
|
|
|
|||
|
|
@ -16,9 +16,9 @@ RUN apt-get update && \
|
|||
|
||||
# Install export dependencies and run exports to AutoInstall packages
|
||||
RUN uv pip install --system -e ".[export]" && \
|
||||
# Run exports to AutoInstall packages
|
||||
yolo export model=tmp/yolo11n.pt format=edgetpu imgsz=32 && \
|
||||
yolo export model=tmp/yolo11n.pt format=ncnn imgsz=32 && \
|
||||
# Run exports to AutoInstall packages (IMX can only export to YOLO11)
|
||||
yolo export model=tmp/yolo26n.pt format=edgetpu imgsz=32 && \
|
||||
yolo export model=tmp/yolo26n.pt format=ncnn imgsz=32 && \
|
||||
yolo export model=tmp/yolo11n.pt format=imx imgsz=32 && \
|
||||
uv pip install --system paddlepaddle x2paddle && \
|
||||
# Remove extra build files
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
# 📚 Ultralytics Docs
|
||||
|
||||
Welcome to Ultralytics Docs, your comprehensive resource for understanding and utilizing our state-of-the-art [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) tools and models, including [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/). These documents are actively maintained and deployed to [https://docs.ultralytics.com](https://docs.ultralytics.com/) for easy access.
|
||||
Welcome to Ultralytics Docs, your comprehensive resource for understanding and utilizing our state-of-the-art [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) tools and models, including [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo26/). These documents are actively maintained and deployed to [https://docs.ultralytics.com](https://docs.ultralytics.com/) for easy access.
|
||||
|
||||
[](https://github.com/ultralytics/docs/actions/workflows/pages/pages-build-deployment)
|
||||
[](https://github.com/ultralytics/docs/actions/workflows/links.yml)
|
||||
|
|
|
|||
|
|
@ -39,7 +39,7 @@ try:
|
|||
except ImportError:
|
||||
postprocess_site = None
|
||||
|
||||
from build_reference import build_reference_docs, build_reference_for
|
||||
from build_reference import build_reference_docs
|
||||
|
||||
from ultralytics.utils import LINUX, LOGGER, MACOS
|
||||
from ultralytics.utils.tqdm import TQDM
|
||||
|
|
@ -66,16 +66,6 @@ def prepare_docs_markdown(clone_repos: bool = True):
|
|||
shutil.rmtree(DOCS / "repos", ignore_errors=True)
|
||||
|
||||
if clone_repos:
|
||||
# Get hub-sdk repo
|
||||
repo = "https://github.com/ultralytics/hub-sdk"
|
||||
local_dir = DOCS / "repos" / Path(repo).name
|
||||
subprocess.run(
|
||||
["git", "clone", "-q", "--depth=1", "--single-branch", "-b", "main", repo, str(local_dir)], check=True
|
||||
)
|
||||
shutil.rmtree(DOCS / "en/hub/sdk", ignore_errors=True) # delete if exists
|
||||
shutil.copytree(local_dir / "docs", DOCS / "en/hub/sdk") # for docs
|
||||
LOGGER.info(f"Cloned/Updated {repo} in {local_dir}")
|
||||
|
||||
# Get docs repo
|
||||
repo = "https://github.com/ultralytics/docs"
|
||||
local_dir = DOCS / "repos" / Path(repo).name
|
||||
|
|
@ -160,8 +150,8 @@ def _process_html_file(html_file: Path) -> bool:
|
|||
except ValueError:
|
||||
rel_path = html_file.name
|
||||
|
||||
# For pages sourced from external repos (hub-sdk, compare), drop edit/copy buttons to avoid wrong links
|
||||
if rel_path.startswith(("hub/sdk/", "compare/")):
|
||||
# For pages sourced from external repos (compare), drop edit/copy buttons to avoid wrong links
|
||||
if rel_path.startswith("compare/"):
|
||||
before = content
|
||||
content = re.sub(
|
||||
r'<a[^>]*class="[^"]*md-content__button[^"]*"[^>]*>.*?</a>',
|
||||
|
|
@ -609,17 +599,6 @@ def main():
|
|||
backup_root, docs_backups = backup_docs_sources()
|
||||
prepare_docs_markdown()
|
||||
build_reference_docs(update_nav=False)
|
||||
# Render reference docs for any extra packages present (e.g., hub-sdk)
|
||||
extra_refs = [
|
||||
{
|
||||
"package": DOCS / "repos" / "hub-sdk" / "hub_sdk",
|
||||
"reference_dir": DOCS / "en" / "hub" / "sdk" / "reference",
|
||||
"repo": "ultralytics/hub-sdk",
|
||||
},
|
||||
]
|
||||
for ref in extra_refs:
|
||||
if ref["package"].exists():
|
||||
build_reference_for(ref["package"], ref["reference_dir"], ref["repo"], update_nav=False)
|
||||
render_jinja_macros()
|
||||
|
||||
# Remove cloned repos before serving/building to keep the tree lean during mkdocs processing
|
||||
|
|
@ -686,7 +665,6 @@ def main():
|
|||
finally:
|
||||
if not restored:
|
||||
restore_all()
|
||||
shutil.rmtree(DOCS.parent / "hub_sdk", ignore_errors=True)
|
||||
shutil.rmtree(DOCS / "repos", ignore_errors=True)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ Welcome to the [Ultralytics](https://www.ultralytics.com/) "Under Construction"
|
|||
|
||||
- **Innovative Breakthroughs:** Get ready for [advanced features](https://docs.ultralytics.com/) and services designed to [transform your AI and ML experience](https://www.ultralytics.com/solutions).
|
||||
- **New Horizons:** Anticipate novel products that [redefine AI and ML capabilities](https://docs.ultralytics.com/tasks/).
|
||||
- **Enhanced Services:** We're upgrading our [services](https://www.ultralytics.com/hub) for greater [efficiency](https://docs.ultralytics.com/modes/benchmark/) and user-friendliness.
|
||||
- **Enhanced Services:** We're upgrading our [services](https://platform.ultralytics.com) for greater [efficiency](https://docs.ultralytics.com/modes/benchmark/) and user-friendliness.
|
||||
|
||||
## Stay Updated 🚧
|
||||
|
||||
|
|
@ -23,7 +23,7 @@ This page is your go-to resource for the latest integration updates and feature
|
|||
|
||||
## We Value Your Input 🗣️
|
||||
|
||||
Help shape the future of Ultralytics HUB by sharing your ideas, feedback, and integration requests through our [official contact form](https://www.ultralytics.com/contact).
|
||||
Help shape the future of Ultralytics Platform by sharing your ideas, feedback, and integration requests through our [official contact form](https://www.ultralytics.com/contact).
|
||||
|
||||
## Thank You, Community! 🌍
|
||||
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ The [Caltech-101](https://data.caltech.edu/records/mzrjq-6wc02) dataset is a wid
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model using Caltech-256 Dataset with Ultralytics HUB
|
||||
<strong>Watch:</strong> How to Train <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model using Caltech-256 Dataset with Ultralytics Platform
|
||||
</p>
|
||||
|
||||
!!! note "Automatic Data Splitting"
|
||||
|
|
@ -51,7 +51,7 @@ To train a YOLO model on the Caltech-101 dataset for 100 [epochs](https://www.ul
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="caltech101", epochs=100, imgsz=416)
|
||||
|
|
@ -61,7 +61,7 @@ To train a YOLO model on the Caltech-101 dataset for 100 [epochs](https://www.ul
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=caltech101 model=yolo11n-cls.pt epochs=100 imgsz=416
|
||||
yolo classify train data=caltech101 model=yolo26n-cls.pt epochs=100 imgsz=416
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -113,7 +113,7 @@ To train an Ultralytics YOLO model on the Caltech-101 dataset, you can use the p
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="caltech101", epochs=100, imgsz=416)
|
||||
|
|
@ -123,7 +123,7 @@ To train an Ultralytics YOLO model on the Caltech-101 dataset, you can use the p
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=caltech101 model=yolo11n-cls.pt epochs=100 imgsz=416
|
||||
yolo classify train data=caltech101 model=yolo26n-cls.pt epochs=100 imgsz=416
|
||||
```
|
||||
|
||||
For more detailed arguments and options, refer to the model [Training](../../modes/train.md) page.
|
||||
|
|
@ -162,6 +162,6 @@ Citing the Caltech-101 dataset in your research acknowledges the creators' contr
|
|||
|
||||
Citing helps in maintaining the integrity of academic work and assists peers in locating the original resource.
|
||||
|
||||
### Can I use Ultralytics HUB for training models on the Caltech-101 dataset?
|
||||
### Can I use Ultralytics Platform for training models on the Caltech-101 dataset?
|
||||
|
||||
Yes, you can use [Ultralytics HUB](https://www.ultralytics.com/hub) for training models on the Caltech-101 dataset. Ultralytics HUB provides an intuitive platform for managing datasets, training models, and deploying them without extensive coding. For a detailed guide, refer to the [how to train your custom models with Ultralytics HUB](https://www.ultralytics.com/blog/how-to-train-your-custom-models-with-ultralytics-hub) blog post.
|
||||
Yes, you can use [Ultralytics Platform](https://platform.ultralytics.com) for training models on the Caltech-101 dataset. Ultralytics Platform provides an intuitive platform for managing datasets, training models, and deploying them without extensive coding. For a detailed guide, refer to the [how to train your custom models with Ultralytics Platform](https://www.ultralytics.com/blog/how-to-train-your-custom-models-with-ultralytics-hub) blog post.
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ The [Caltech-256](https://data.caltech.edu/records/nyy15-4j048) dataset is an ex
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model using Caltech-256 Dataset with Ultralytics HUB
|
||||
<strong>Watch:</strong> How to Train <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model using Caltech-256 Dataset with Ultralytics Platform
|
||||
</p>
|
||||
|
||||
!!! note "Automatic Data Splitting"
|
||||
|
|
@ -51,7 +51,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 [epochs](https://www.ul
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="caltech256", epochs=100, imgsz=416)
|
||||
|
|
@ -61,7 +61,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 [epochs](https://www.ul
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=caltech256 model=yolo11n-cls.pt epochs=100 imgsz=416
|
||||
yolo classify train data=caltech256 model=yolo26n-cls.pt epochs=100 imgsz=416
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -108,7 +108,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 [epochs](https://www.ul
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="caltech256", epochs=100, imgsz=416)
|
||||
|
|
@ -118,7 +118,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 [epochs](https://www.ul
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=caltech256 model=yolo11n-cls.pt epochs=100 imgsz=416
|
||||
yolo classify train data=caltech256 model=yolo26n-cls.pt epochs=100 imgsz=416
|
||||
```
|
||||
|
||||
### What are the most common use cases for the Caltech-256 dataset?
|
||||
|
|
@ -142,7 +142,7 @@ Ultralytics YOLO models offer several advantages for training on the Caltech-256
|
|||
|
||||
- **High Accuracy**: YOLO models are known for their state-of-the-art performance in object detection tasks.
|
||||
- **Speed**: They provide real-time inference capabilities, making them suitable for applications requiring quick predictions.
|
||||
- **Ease of Use**: With [Ultralytics HUB](https://www.ultralytics.com/hub), users can train, validate, and deploy models without extensive coding.
|
||||
- **Pretrained Models**: Starting from pretrained models, like `yolo11n-cls.pt`, can significantly reduce training time and improve model [accuracy](https://www.ultralytics.com/glossary/accuracy).
|
||||
- **Ease of Use**: With [Ultralytics Platform](https://platform.ultralytics.com), users can train, validate, and deploy models without extensive coding.
|
||||
- **Pretrained Models**: Starting from pretrained models, like `yolo26n-cls.pt`, can significantly reduce training time and improve model [accuracy](https://www.ultralytics.com/glossary/accuracy).
|
||||
|
||||
For more details, explore our [comprehensive training guide](../../modes/train.md) and learn about [image classification](../../tasks/classify.md) with Ultralytics YOLO.
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ The [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train an <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model with CIFAR-10 Dataset using Ultralytics YOLO11
|
||||
<strong>Watch:</strong> How to Train an <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model with CIFAR-10 Dataset using Ultralytics YOLO26
|
||||
</p>
|
||||
|
||||
## Key Features
|
||||
|
|
@ -50,7 +50,7 @@ To train a YOLO model on the CIFAR-10 dataset for 100 [epochs](https://www.ultra
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="cifar10", epochs=100, imgsz=32)
|
||||
|
|
@ -60,7 +60,7 @@ To train a YOLO model on the CIFAR-10 dataset for 100 [epochs](https://www.ultra
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=cifar10 model=yolo11n-cls.pt epochs=100 imgsz=32
|
||||
yolo classify train data=cifar10 model=yolo26n-cls.pt epochs=100 imgsz=32
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -104,7 +104,7 @@ To train a YOLO model on the CIFAR-10 dataset using Ultralytics, you can follow
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="cifar10", epochs=100, imgsz=32)
|
||||
|
|
@ -114,7 +114,7 @@ To train a YOLO model on the CIFAR-10 dataset using Ultralytics, you can follow
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=cifar10 model=yolo11n-cls.pt epochs=100 imgsz=32
|
||||
yolo classify train data=cifar10 model=yolo26n-cls.pt epochs=100 imgsz=32
|
||||
```
|
||||
|
||||
For more details, refer to the model [Training](../../modes/train.md) page.
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ The [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO11 on CIFAR-100 | Step-by-Step Image Classification Tutorial 🚀
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on CIFAR-100 | Step-by-Step Image Classification Tutorial 🚀
|
||||
</p>
|
||||
|
||||
## Key Features
|
||||
|
|
@ -50,7 +50,7 @@ To train a YOLO model on the CIFAR-100 dataset for 100 [epochs](https://www.ultr
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="cifar100", epochs=100, imgsz=32)
|
||||
|
|
@ -60,7 +60,7 @@ To train a YOLO model on the CIFAR-100 dataset for 100 [epochs](https://www.ultr
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=cifar100 model=yolo11n-cls.pt epochs=100 imgsz=32
|
||||
yolo classify train data=cifar100 model=yolo26n-cls.pt epochs=100 imgsz=32
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -94,7 +94,7 @@ We would like to acknowledge Alex Krizhevsky for creating and maintaining the CI
|
|||
|
||||
### What is the CIFAR-100 dataset and why is it significant?
|
||||
|
||||
The [CIFAR-100 dataset](https://www.cs.toronto.edu/~kriz/cifar.html) is a large collection of 60,000 32x32 color images classified into 100 classes. Developed by the Canadian Institute For Advanced Research (CIFAR), it provides a challenging dataset ideal for complex machine learning and computer vision tasks. Its significance lies in the diversity of classes and the small size of the images, making it a valuable resource for training and testing [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models, like Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs), using frameworks such as [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/).
|
||||
The [CIFAR-100 dataset](https://www.cs.toronto.edu/~kriz/cifar.html) is a large collection of 60,000 32x32 color images classified into 100 classes. Developed by the Canadian Institute For Advanced Research (CIFAR), it provides a challenging dataset ideal for complex machine learning and computer vision tasks. Its significance lies in the diversity of classes and the small size of the images, making it a valuable resource for training and testing [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models, like Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs), using frameworks such as [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo26/).
|
||||
|
||||
### How do I train a YOLO model on the CIFAR-100 dataset?
|
||||
|
||||
|
|
@ -108,7 +108,7 @@ You can train a YOLO model on the CIFAR-100 dataset using either Python or CLI c
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="cifar100", epochs=100, imgsz=32)
|
||||
|
|
@ -118,7 +118,7 @@ You can train a YOLO model on the CIFAR-100 dataset using either Python or CLI c
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=cifar100 model=yolo11n-cls.pt epochs=100 imgsz=32
|
||||
yolo classify train data=cifar100 model=yolo26n-cls.pt epochs=100 imgsz=32
|
||||
```
|
||||
|
||||
For a comprehensive list of available arguments, please refer to the model [Training](../../modes/train.md) page.
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ The [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset is
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to do <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> on Fashion MNIST Dataset using Ultralytics YOLO11
|
||||
<strong>Watch:</strong> How to do <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> on Fashion MNIST Dataset using Ultralytics YOLO26
|
||||
</p>
|
||||
|
||||
## Key Features
|
||||
|
|
@ -66,7 +66,7 @@ To train a CNN model on the Fashion-MNIST dataset for 100 [epochs](https://www.u
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="fashion-mnist", epochs=100, imgsz=28)
|
||||
|
|
@ -76,7 +76,7 @@ To train a CNN model on the Fashion-MNIST dataset for 100 [epochs](https://www.u
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=fashion-mnist model=yolo11n-cls.pt epochs=100 imgsz=28
|
||||
yolo classify train data=fashion-mnist model=yolo26n-cls.pt epochs=100 imgsz=28
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -109,7 +109,7 @@ To train an Ultralytics YOLO model on the Fashion-MNIST dataset, you can use bot
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained model
|
||||
model = YOLO("yolo11n-cls.pt")
|
||||
model = YOLO("yolo26n-cls.pt")
|
||||
|
||||
# Train the model on Fashion-MNIST
|
||||
results = model.train(data="fashion-mnist", epochs=100, imgsz=28)
|
||||
|
|
@ -119,7 +119,7 @@ To train an Ultralytics YOLO model on the Fashion-MNIST dataset, you can use bot
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo classify train data=fashion-mnist model=yolo11n-cls.pt epochs=100 imgsz=28
|
||||
yolo classify train data=fashion-mnist model=yolo26n-cls.pt epochs=100 imgsz=28
|
||||
```
|
||||
|
||||
For more detailed training parameters, refer to the [Training page](../../modes/train.md).
|
||||
|
|
@ -130,7 +130,7 @@ The [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset is
|
|||
|
||||
### Can I use Ultralytics YOLO for image classification tasks like Fashion-MNIST?
|
||||
|
||||
Yes, Ultralytics YOLO models can be used for image classification tasks, including those involving the Fashion-MNIST dataset. YOLO11, for example, supports various vision tasks such as detection, segmentation, and classification. To get started with image classification tasks, refer to the [Classification page](https://docs.ultralytics.com/tasks/classify/).
|
||||
Yes, Ultralytics YOLO models can be used for image classification tasks, including those involving the Fashion-MNIST dataset. YOLO26, for example, supports various vision tasks such as detection, segmentation, and classification. To get started with image classification tasks, refer to the [Classification page](https://docs.ultralytics.com/tasks/classify/).
|
||||
|
||||
### What are the key features and structure of the Fashion-MNIST dataset?
|
||||
|
||||
|
|
|
|||
|
|
@ -43,7 +43,7 @@ To train a deep learning model on the ImageNet dataset for 100 [epochs](https://
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="imagenet", epochs=100, imgsz=224)
|
||||
|
|
@ -53,7 +53,7 @@ To train a deep learning model on the ImageNet dataset for 100 [epochs](https://
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=imagenet model=yolo11n-cls.pt epochs=100 imgsz=224
|
||||
yolo classify train data=imagenet model=yolo26n-cls.pt epochs=100 imgsz=224
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -104,7 +104,7 @@ To use a pretrained Ultralytics YOLO model for image classification on the Image
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="imagenet", epochs=100, imgsz=224)
|
||||
|
|
@ -114,14 +114,14 @@ To use a pretrained Ultralytics YOLO model for image classification on the Image
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=imagenet model=yolo11n-cls.pt epochs=100 imgsz=224
|
||||
yolo classify train data=imagenet model=yolo26n-cls.pt epochs=100 imgsz=224
|
||||
```
|
||||
|
||||
For more in-depth training instruction, refer to our [Training page](../../modes/train.md).
|
||||
|
||||
### Why should I use the Ultralytics YOLO11 pretrained models for my ImageNet dataset projects?
|
||||
### Why should I use the Ultralytics YOLO26 pretrained models for my ImageNet dataset projects?
|
||||
|
||||
Ultralytics YOLO11 pretrained models offer state-of-the-art performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) for various computer vision tasks. For example, the YOLO11n-cls model, with a top-1 accuracy of 70.0% and a top-5 accuracy of 89.4%, is optimized for real-time applications. Pretrained models reduce the computational resources required for training from scratch and accelerate development cycles. Learn more about the performance metrics of YOLO11 models in the [ImageNet Pretrained Models section](#imagenet-pretrained-models).
|
||||
Ultralytics YOLO26 pretrained models offer state-of-the-art performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) for various computer vision tasks. For example, the YOLO26n-cls model, with a top-1 accuracy of 70.0% and a top-5 accuracy of 89.4%, is optimized for real-time applications. Pretrained models reduce the computational resources required for training from scratch and accelerate development cycles. Learn more about the performance metrics of YOLO26 models in the [ImageNet Pretrained Models section](#imagenet-pretrained-models).
|
||||
|
||||
### How is the ImageNet dataset structured, and why is it important?
|
||||
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@ To test a deep learning model on the ImageNet10 dataset with an image size of 22
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="imagenet10", epochs=5, imgsz=224)
|
||||
|
|
@ -45,7 +45,7 @@ To test a deep learning model on the ImageNet10 dataset with an image size of 22
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=imagenet10 model=yolo11n-cls.pt epochs=5 imgsz=224
|
||||
yolo classify train data=imagenet10 model=yolo26n-cls.pt epochs=5 imgsz=224
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -96,7 +96,7 @@ To test your deep learning model on the ImageNet10 dataset with an image size of
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="imagenet10", epochs=5, imgsz=224)
|
||||
|
|
@ -106,7 +106,7 @@ To test your deep learning model on the ImageNet10 dataset with an image size of
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=imagenet10 model=yolo11n-cls.pt epochs=5 imgsz=224
|
||||
yolo classify train data=imagenet10 model=yolo26n-cls.pt epochs=5 imgsz=224
|
||||
```
|
||||
|
||||
Refer to the [Training](../../modes/train.md) page for a comprehensive list of available arguments.
|
||||
|
|
|
|||
|
|
@ -37,7 +37,7 @@ To train a model on the ImageNette dataset for 100 epochs with a standard image
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="imagenette", epochs=100, imgsz=224)
|
||||
|
|
@ -47,7 +47,7 @@ To train a model on the ImageNette dataset for 100 epochs with a standard image
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=imagenette model=yolo11n-cls.pt epochs=100 imgsz=224
|
||||
yolo classify train data=imagenette model=yolo26n-cls.pt epochs=100 imgsz=224
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -72,7 +72,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model with ImageNette160
|
||||
results = model.train(data="imagenette160", epochs=100, imgsz=160)
|
||||
|
|
@ -82,7 +82,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model with ImageNette160
|
||||
yolo classify train data=imagenette160 model=yolo11n-cls.pt epochs=100 imgsz=160
|
||||
yolo classify train data=imagenette160 model=yolo26n-cls.pt epochs=100 imgsz=160
|
||||
```
|
||||
|
||||
!!! example "Train Example with ImageNette320"
|
||||
|
|
@ -93,7 +93,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model with ImageNette320
|
||||
results = model.train(data="imagenette320", epochs=100, imgsz=320)
|
||||
|
|
@ -103,7 +103,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model with ImageNette320
|
||||
yolo classify train data=imagenette320 model=yolo11n-cls.pt epochs=100 imgsz=320
|
||||
yolo classify train data=imagenette320 model=yolo26n-cls.pt epochs=100 imgsz=320
|
||||
```
|
||||
|
||||
These smaller versions of the dataset allow for rapid iterations during the development process while still providing valuable and realistic image classification tasks.
|
||||
|
|
@ -130,7 +130,7 @@ To train a YOLO model on the ImageNette dataset for 100 [epochs](https://www.ult
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="imagenette", epochs=100, imgsz=224)
|
||||
|
|
@ -140,7 +140,7 @@ To train a YOLO model on the ImageNette dataset for 100 [epochs](https://www.ult
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=imagenette model=yolo11n-cls.pt epochs=100 imgsz=224
|
||||
yolo classify train data=imagenette model=yolo26n-cls.pt epochs=100 imgsz=224
|
||||
```
|
||||
|
||||
For more details, see the [Training](../../modes/train.md) documentation page.
|
||||
|
|
@ -167,7 +167,7 @@ Yes, the ImageNette dataset is also available in two resized versions: ImageNett
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt")
|
||||
model = YOLO("yolo26n-cls.pt")
|
||||
|
||||
# Train the model with ImageNette160
|
||||
results = model.train(data="imagenette160", epochs=100, imgsz=160)
|
||||
|
|
@ -177,7 +177,7 @@ Yes, the ImageNette dataset is also available in two resized versions: ImageNett
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model with ImageNette160
|
||||
yolo classify train data=imagenette160 model=yolo11n-cls.pt epochs=100 imgsz=160
|
||||
yolo classify train data=imagenette160 model=yolo26n-cls.pt epochs=100 imgsz=160
|
||||
```
|
||||
|
||||
For more information, refer to [Training with ImageNette160 and ImageNette320](#imagenette160-and-imagenette320).
|
||||
|
|
|
|||
|
|
@ -39,7 +39,7 @@ To train a CNN model on the ImageWoof dataset for 100 [epochs](https://www.ultra
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="imagewoof", epochs=100, imgsz=224)
|
||||
|
|
@ -49,7 +49,7 @@ To train a CNN model on the ImageWoof dataset for 100 [epochs](https://www.ultra
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=imagewoof model=yolo11n-cls.pt epochs=100 imgsz=224
|
||||
yolo classify train data=imagewoof model=yolo26n-cls.pt epochs=100 imgsz=224
|
||||
```
|
||||
|
||||
## Dataset Variants
|
||||
|
|
@ -72,7 +72,7 @@ To use these variants in your training, simply replace 'imagewoof' in the datase
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# For medium-sized dataset
|
||||
model.train(data="imagewoof320", epochs=100, imgsz=224)
|
||||
|
|
@ -85,7 +85,7 @@ To use these variants in your training, simply replace 'imagewoof' in the datase
|
|||
|
||||
```bash
|
||||
# Load a pretrained model and train on the medium-sized dataset
|
||||
yolo classify train model=yolo11n-cls.pt data=imagewoof320 epochs=100 imgsz=224
|
||||
yolo classify train model=yolo26n-cls.pt data=imagewoof320 epochs=100 imgsz=224
|
||||
```
|
||||
|
||||
It's important to note that using smaller images will likely yield lower performance in terms of classification accuracy. However, it's an excellent way to iterate quickly in the early stages of model development and prototyping.
|
||||
|
|
@ -121,7 +121,7 @@ To train a [Convolutional Neural Network](https://www.ultralytics.com/glossary/c
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo11n-cls.pt") # Load a pretrained model
|
||||
model = YOLO("yolo26n-cls.pt") # Load a pretrained model
|
||||
results = model.train(data="imagewoof", epochs=100, imgsz=224)
|
||||
```
|
||||
|
||||
|
|
@ -129,7 +129,7 @@ To train a [Convolutional Neural Network](https://www.ultralytics.com/glossary/c
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo classify train data=imagewoof model=yolo11n-cls.pt epochs=100 imgsz=224
|
||||
yolo classify train data=imagewoof model=yolo26n-cls.pt epochs=100 imgsz=224
|
||||
```
|
||||
|
||||
For more details on available training arguments, refer to the [Training](../../modes/train.md) page.
|
||||
|
|
|
|||
|
|
@ -86,7 +86,7 @@ This structured approach ensures that the model can effectively learn from well-
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="path/to/dataset", epochs=100, imgsz=640)
|
||||
|
|
@ -96,7 +96,7 @@ This structured approach ensures that the model can effectively learn from well-
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=path/to/data model=yolo11n-cls.pt epochs=100 imgsz=640
|
||||
yolo classify train data=path/to/data model=yolo26n-cls.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
!!! tip
|
||||
|
|
@ -162,7 +162,7 @@ To use your own dataset with Ultralytics YOLO, ensure it follows the specified d
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="path/to/your/dataset", epochs=100, imgsz=640)
|
||||
|
|
@ -174,7 +174,7 @@ More details can be found in the [Adding your own dataset](#adding-your-own-data
|
|||
|
||||
Ultralytics YOLO offers several benefits for image classification, including:
|
||||
|
||||
- **Pretrained Models**: Load pretrained models like `yolo11n-cls.pt` to jump-start your training process.
|
||||
- **Pretrained Models**: Load pretrained models like `yolo26n-cls.pt` to jump-start your training process.
|
||||
- **Ease of Use**: Simple API and CLI commands for training and evaluation.
|
||||
- **High Performance**: State-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed, ideal for real-time applications.
|
||||
- **Support for Multiple Datasets**: Seamless integration with various popular datasets like [CIFAR-10](cifar10.md), [ImageNet](imagenet.md), and more.
|
||||
|
|
@ -194,7 +194,7 @@ Training a model using Ultralytics YOLO can be done easily in both Python and CL
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="path/to/dataset", epochs=100, imgsz=640)
|
||||
|
|
@ -204,7 +204,7 @@ Training a model using Ultralytics YOLO can be done easily in both Python and CL
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=path/to/data model=yolo11n-cls.pt epochs=100 imgsz=640
|
||||
yolo classify train data=path/to/data model=yolo26n-cls.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
These examples demonstrate the straightforward process of training a YOLO model using either approach. For more information, visit the [Usage](#usage) section and the [Train](https://docs.ultralytics.com/tasks/classify/#train) page for classification tasks.
|
||||
|
|
|
|||
|
|
@ -56,7 +56,7 @@ To train a CNN model on the MNIST dataset for 100 [epochs](https://www.ultralyti
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="mnist", epochs=100, imgsz=28)
|
||||
|
|
@ -66,7 +66,7 @@ To train a CNN model on the MNIST dataset for 100 [epochs](https://www.ultralyti
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=mnist model=yolo11n-cls.pt epochs=100 imgsz=28
|
||||
yolo classify train data=mnist model=yolo26n-cls.pt epochs=100 imgsz=28
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -106,7 +106,7 @@ Need a lightning-fast regression test? Ultralytics also exposes `data="mnist160"
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo classify train data=mnist160 model=yolo11n-cls.pt epochs=5 imgsz=28
|
||||
yolo classify train data=mnist160 model=yolo26n-cls.pt epochs=5 imgsz=28
|
||||
```
|
||||
|
||||
Use this subset for CI pipelines or sanity checks before committing to the full 70,000-image dataset.
|
||||
|
|
@ -129,7 +129,7 @@ To train a model on the MNIST dataset using Ultralytics YOLO, you can follow the
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="mnist", epochs=100, imgsz=28)
|
||||
|
|
@ -139,7 +139,7 @@ To train a model on the MNIST dataset using Ultralytics YOLO, you can follow the
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=mnist model=yolo11n-cls.pt epochs=100 imgsz=28
|
||||
yolo classify train data=mnist model=yolo26n-cls.pt epochs=100 imgsz=28
|
||||
```
|
||||
|
||||
For a detailed list of available training arguments, refer to the [Training](../../modes/train.md) page.
|
||||
|
|
@ -148,9 +148,9 @@ For a detailed list of available training arguments, refer to the [Training](../
|
|||
|
||||
The MNIST dataset contains only handwritten digits, whereas the Extended MNIST (EMNIST) dataset includes both digits and uppercase and lowercase letters. EMNIST was developed as a successor to MNIST and utilizes the same 28×28 pixel format for the images, making it compatible with tools and models designed for the original MNIST dataset. This broader range of characters in EMNIST makes it useful for a wider variety of machine learning applications.
|
||||
|
||||
### Can I use Ultralytics HUB to train models on custom datasets like MNIST?
|
||||
### Can I use Ultralytics Platform to train models on custom datasets like MNIST?
|
||||
|
||||
Yes, you can use [Ultralytics HUB](https://docs.ultralytics.com/hub/) to train models on custom datasets like MNIST. Ultralytics HUB offers a user-friendly interface for uploading datasets, training models, and managing projects without needing extensive coding knowledge. For more details on how to get started, check out the [Ultralytics HUB Quickstart](https://docs.ultralytics.com/hub/quickstart/) page.
|
||||
Yes, you can use [Ultralytics Platform](https://docs.ultralytics.com/platform/) to train models on custom datasets like MNIST. Ultralytics Platform offers a user-friendly interface for uploading datasets, training models, and managing projects without needing extensive coding knowledge. For more details on how to get started, check out the [Ultralytics Platform Quickstart](https://docs.ultralytics.com/platform/quickstart/) page.
|
||||
|
||||
### How does MNIST compare to other image classification datasets?
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore our African Wildlife Dataset featuring images of buffalo, elephant, rhino, and zebra for training computer vision models. Ideal for research and conservation.
|
||||
keywords: African Wildlife Dataset, South African animals, object detection, computer vision, YOLO11, wildlife research, conservation, dataset
|
||||
keywords: African Wildlife Dataset, South African animals, object detection, computer vision, YOLO26, wildlife research, conservation, dataset
|
||||
---
|
||||
|
||||
# African Wildlife Dataset
|
||||
|
|
@ -16,7 +16,7 @@ This dataset showcases four common animal classes typically found in South Afric
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> African Wildlife Animals Detection using Ultralytics YOLO11
|
||||
<strong>Watch:</strong> African Wildlife Animals Detection using Ultralytics YOLO26
|
||||
</p>
|
||||
|
||||
## Dataset Structure
|
||||
|
|
@ -43,7 +43,7 @@ A YAML (Yet Another Markup Language) file defines the dataset configuration, inc
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the African wildlife dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n model on the African wildlife dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -53,7 +53,7 @@ To train a YOLO11n model on the African wildlife dataset for 100 [epochs](https:
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -63,7 +63,7 @@ To train a YOLO11n model on the African wildlife dataset for 100 [epochs](https:
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=african-wildlife.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=african-wildlife.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
!!! example "Inference Example"
|
||||
|
|
@ -126,9 +126,9 @@ If you use this dataset in your research, please cite it using the mentioned det
|
|||
|
||||
The African Wildlife Dataset includes images of four common animal species found in South African nature reserves: buffalo, elephant, rhino, and zebra. It is a valuable resource for training computer vision algorithms in object detection and animal identification. The dataset supports various tasks like object tracking, research, and conservation efforts. For more information on its structure and applications, refer to the [Dataset Structure](#dataset-structure) section and [Applications](#applications) of the dataset.
|
||||
|
||||
### How do I train a YOLO11 model using the African Wildlife Dataset?
|
||||
### How do I train a YOLO26 model using the African Wildlife Dataset?
|
||||
|
||||
You can train a YOLO11 model on the African Wildlife Dataset by using the `african-wildlife.yaml` configuration file. Below is an example of how to train the YOLO11n model for 100 epochs with an image size of 640:
|
||||
You can train a YOLO26 model on the African Wildlife Dataset by using the `african-wildlife.yaml` configuration file. Below is an example of how to train the YOLO26n model for 100 epochs with an image size of 640:
|
||||
|
||||
!!! example
|
||||
|
||||
|
|
@ -138,7 +138,7 @@ You can train a YOLO11 model on the African Wildlife Dataset by using the `afric
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -148,7 +148,7 @@ You can train a YOLO11 model on the African Wildlife Dataset by using the `afric
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=african-wildlife.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=african-wildlife.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For additional training parameters and options, refer to the [Training](../../modes/train.md) documentation.
|
||||
|
|
|
|||
|
|
@ -43,7 +43,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the Argoverse dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n model on the Argoverse dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -53,7 +53,7 @@ To train a YOLO11n model on the Argoverse dataset for 100 [epochs](https://www.u
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="Argoverse.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -63,7 +63,7 @@ To train a YOLO11n model on the Argoverse dataset for 100 [epochs](https://www.u
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=Argoverse.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=Argoverse.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
|
@ -104,7 +104,7 @@ The [Argoverse](https://www.argoverse.org/) dataset, developed by Argo AI, suppo
|
|||
|
||||
### How can I train an Ultralytics YOLO model using the Argoverse dataset?
|
||||
|
||||
To train a YOLO11 model with the Argoverse dataset, use the provided YAML configuration file and the following code:
|
||||
To train a YOLO26 model with the Argoverse dataset, use the provided YAML configuration file and the following code:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -114,7 +114,7 @@ To train a YOLO11 model with the Argoverse dataset, use the provided YAML config
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="Argoverse.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -125,7 +125,7 @@ To train a YOLO11 model with the Argoverse dataset, use the provided YAML config
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=Argoverse.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=Argoverse.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a detailed explanation of the arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ A brain tumor detection dataset consists of medical images from MRI or CT scans,
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Brain Tumor Detection using Ultralytics HUB
|
||||
<strong>Watch:</strong> Brain Tumor Detection using Ultralytics Platform
|
||||
</p>
|
||||
|
||||
## Dataset Structure
|
||||
|
|
@ -56,7 +56,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a [YOLO11](https://docs.ultralytics.com/models/yolo11/) model on the brain tumor dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, utilize the provided code snippets. For a detailed list of available arguments, consult the model's [Training](../../modes/train.md) page.
|
||||
To train a [YOLO26](https://docs.ultralytics.com/models/yolo26/) model on the brain tumor dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, utilize the provided code snippets. For a detailed list of available arguments, consult the model's [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -66,7 +66,7 @@ To train a [YOLO11](https://docs.ultralytics.com/models/yolo11/) model on the br
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="brain-tumor.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -76,7 +76,7 @@ To train a [YOLO11](https://docs.ultralytics.com/models/yolo11/) model on the br
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=brain-tumor.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=brain-tumor.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
!!! example "Inference Example"
|
||||
|
|
@ -108,7 +108,7 @@ The brain tumor dataset encompasses a wide array of medical images featuring bra
|
|||
|
||||
- **Mosaiced Image**: Displayed here is a training batch comprising mosaiced dataset images. Mosaicing, a training technique, consolidates multiple images into one, enhancing batch diversity. This approach aids in improving the model's capacity to generalize across various tumor sizes, shapes, and locations within brain scans.
|
||||
|
||||
This example highlights the diversity and intricacy of images within the brain tumor dataset, underscoring the advantages of incorporating mosaicing during the training phase for [medical image analysis](https://www.ultralytics.com/blog/using-yolo11-for-tumor-detection-in-medical-imaging).
|
||||
This example highlights the diversity and intricacy of images within the brain tumor dataset, underscoring the advantages of incorporating mosaicing during the training phase for [medical image analysis](https://www.ultralytics.com/blog/using-yolo26-for-tumor-detection-in-medical-imaging).
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
|
|
@ -136,9 +136,9 @@ If you use this dataset in your research or development work, please cite it app
|
|||
|
||||
The brain tumor dataset is divided into two subsets: the **training set** consists of 893 images with corresponding annotations, while the **testing set** comprises 223 images with paired annotations. This structured division aids in developing robust and accurate computer vision models for detecting brain tumors. For more information on the dataset structure, visit the [Dataset Structure](#dataset-structure) section.
|
||||
|
||||
### How can I train a YOLO11 model on the brain tumor dataset using Ultralytics?
|
||||
### How can I train a YOLO26 model on the brain tumor dataset using Ultralytics?
|
||||
|
||||
You can train a YOLO11 model on the brain tumor dataset for 100 epochs with an image size of 640px using both Python and CLI methods. Below are the examples for both:
|
||||
You can train a YOLO26 model on the brain tumor dataset for 100 epochs with an image size of 640px using both Python and CLI methods. Below are the examples for both:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -148,7 +148,7 @@ You can train a YOLO11 model on the brain tumor dataset for 100 epochs with an i
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="brain-tumor.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -159,7 +159,7 @@ You can train a YOLO11 model on the brain tumor dataset for 100 epochs with an i
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=brain-tumor.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=brain-tumor.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a detailed list of available arguments, refer to the [Training](../../modes/train.md) page.
|
||||
|
|
@ -168,9 +168,9 @@ For a detailed list of available arguments, refer to the [Training](../../modes/
|
|||
|
||||
Using the brain tumor dataset in AI projects enables early diagnosis and treatment planning for brain tumors. It helps in automating brain tumor identification through computer vision, facilitating accurate and timely medical interventions, and supporting personalized treatment strategies. This application holds significant potential in improving patient outcomes and medical efficiencies. For more insights on AI applications in healthcare, see [Ultralytics' healthcare solutions](https://www.ultralytics.com/solutions/ai-in-healthcare).
|
||||
|
||||
### How do I perform inference using a fine-tuned YOLO11 model on the brain tumor dataset?
|
||||
### How do I perform inference using a fine-tuned YOLO26 model on the brain tumor dataset?
|
||||
|
||||
Inference using a fine-tuned YOLO11 model can be performed with either Python or CLI approaches. Here are the examples:
|
||||
Inference using a fine-tuned YOLO26 model can be performed with either Python or CLI approaches. Here are the examples:
|
||||
|
||||
!!! example "Inference Example"
|
||||
|
||||
|
|
|
|||
|
|
@ -40,7 +40,7 @@ The COCO dataset is split into three subsets:
|
|||
|
||||
## Applications
|
||||
|
||||
The COCO dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object detection (such as [Ultralytics YOLO](../../models/yolo11.md), [Faster R-CNN](https://arxiv.org/abs/1506.01497), and [SSD](https://arxiv.org/abs/1512.02325)), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) (such as [Mask R-CNN](https://arxiv.org/abs/1703.06870)), and keypoint detection (such as [OpenPose](https://arxiv.org/abs/1812.08008)). The dataset's diverse set of object categories, large number of annotated images, and standardized evaluation metrics make it an essential resource for computer vision researchers and practitioners.
|
||||
The COCO dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object detection (such as [Ultralytics YOLO](../../models/yolo26.md), [Faster R-CNN](https://arxiv.org/abs/1506.01497), and [SSD](https://arxiv.org/abs/1512.02325)), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) (such as [Mask R-CNN](https://arxiv.org/abs/1703.06870)), and keypoint detection (such as [OpenPose](https://arxiv.org/abs/1812.08008)). The dataset's diverse set of object categories, large number of annotated images, and standardized evaluation metrics make it an essential resource for computer vision researchers and practitioners.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
|
|
@ -54,7 +54,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the COCO dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n model on the COCO dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -64,7 +64,7 @@ To train a YOLO11n model on the COCO dataset for 100 [epochs](https://www.ultral
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -74,7 +74,7 @@ To train a YOLO11n model on the COCO dataset for 100 [epochs](https://www.ultral
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=coco.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=coco.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -116,7 +116,7 @@ The [COCO dataset](https://cocodataset.org/#home) (Common Objects in Context) is
|
|||
|
||||
### How can I train a YOLO model using the COCO dataset?
|
||||
|
||||
To train a YOLO11 model using the COCO dataset, you can use the following code snippets:
|
||||
To train a YOLO26 model using the COCO dataset, you can use the following code snippets:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -126,7 +126,7 @@ To train a YOLO11 model using the COCO dataset, you can use the following code s
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -136,7 +136,7 @@ To train a YOLO11 model using the COCO dataset, you can use the following code s
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=coco.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=coco.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
Refer to the [Training page](../../modes/train.md) for more details on available arguments.
|
||||
|
|
@ -150,15 +150,15 @@ The COCO dataset includes:
|
|||
- Standardized evaluation metrics for object detection (mAP) and segmentation (mean Average Recall, mAR).
|
||||
- **Mosaicing** technique in training batches to enhance model generalization across various object sizes and contexts.
|
||||
|
||||
### Where can I find pretrained YOLO11 models trained on the COCO dataset?
|
||||
### Where can I find pretrained YOLO26 models trained on the COCO dataset?
|
||||
|
||||
Pretrained YOLO11 models on the COCO dataset can be downloaded from the links provided in the documentation. Examples include:
|
||||
Pretrained YOLO26 models on the COCO dataset can be downloaded from the links provided in the documentation. Examples include:
|
||||
|
||||
- [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt)
|
||||
- [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt)
|
||||
- [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt)
|
||||
- [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt)
|
||||
- [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt)
|
||||
- [YOLO26n](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt)
|
||||
- [YOLO26s](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s.pt)
|
||||
- [YOLO26m](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m.pt)
|
||||
- [YOLO26l](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l.pt)
|
||||
- [YOLO26x](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x.pt)
|
||||
|
||||
These models vary in size, mAP, and inference speed, providing options for different performance and resource requirements.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore the Ultralytics COCO128 dataset, a versatile and manageable set of 128 images perfect for testing object detection models and training pipelines.
|
||||
keywords: COCO128, Ultralytics, dataset, object detection, YOLO11, training, validation, machine learning, computer vision
|
||||
keywords: COCO128, Ultralytics, dataset, object detection, YOLO26, training, validation, machine learning, computer vision
|
||||
---
|
||||
|
||||
# COCO128 Dataset
|
||||
|
|
@ -21,7 +21,7 @@ keywords: COCO128, Ultralytics, dataset, object detection, YOLO11, training, val
|
|||
<strong>Watch:</strong> Ultralytics COCO Dataset Overview
|
||||
</p>
|
||||
|
||||
This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
|
||||
This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
|
|
@ -35,7 +35,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the COCO128 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n model on the COCO128 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -45,7 +45,7 @@ To train a YOLO11n model on the COCO128 dataset for 100 [epochs](https://www.ult
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -55,7 +55,7 @@ To train a YOLO11n model on the COCO128 dataset for 100 [epochs](https://www.ult
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=coco128.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=coco128.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -95,9 +95,9 @@ We would like to acknowledge the COCO Consortium for creating and maintaining th
|
|||
|
||||
The Ultralytics COCO128 dataset is a compact subset containing the first 128 images from the COCO train 2017 dataset. It's primarily used for testing and debugging [object detection](https://www.ultralytics.com/glossary/object-detection) models, experimenting with new detection approaches, and validating training pipelines before scaling to larger datasets. Its manageable size makes it perfect for quick iterations while still providing enough diversity to be a meaningful test case.
|
||||
|
||||
### How do I train a YOLO11 model using the COCO128 dataset?
|
||||
### How do I train a YOLO26 model using the COCO128 dataset?
|
||||
|
||||
To train a YOLO11 model on the COCO128 dataset, you can use either Python or CLI commands. Here's how:
|
||||
To train a YOLO26 model on the COCO128 dataset, you can use either Python or CLI commands. Here's how:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -107,7 +107,7 @@ To train a YOLO11 model on the COCO128 dataset, you can use either Python or CLI
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained model
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -117,7 +117,7 @@ To train a YOLO11 model on the COCO128 dataset, you can use either Python or CLI
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=coco128.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=coco128.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For more training options and parameters, refer to the [Training](../../modes/train.md) documentation.
|
||||
|
|
|
|||
|
|
@ -1,14 +1,14 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore the Ultralytics COCO8-Grayscale dataset, a versatile and manageable set of 8 images perfect for testing object detection models and training pipelines.
|
||||
keywords: COCO8-Grayscale, Ultralytics, dataset, object detection, YOLO11, training, validation, machine learning, computer vision
|
||||
keywords: COCO8-Grayscale, Ultralytics, dataset, object detection, YOLO26, training, validation, machine learning, computer vision
|
||||
---
|
||||
|
||||
# COCO8-Grayscale Dataset
|
||||
|
||||
## Introduction
|
||||
|
||||
The [Ultralytics](https://www.ultralytics.com/) COCO8-Grayscale dataset is a compact yet powerful [object detection](https://www.ultralytics.com/glossary/object-detection) dataset, consisting of the first 8 images from the COCO train 2017 set and converted to grayscale format—4 for training and 4 for validation. This dataset is specifically designed for rapid testing, debugging, and experimentation with [YOLO](https://docs.ultralytics.com/models/yolo11/) grayscale models and training pipelines. Its small size makes it highly manageable, while its diversity ensures it serves as an effective sanity check before scaling up to larger datasets.
|
||||
The [Ultralytics](https://www.ultralytics.com/) COCO8-Grayscale dataset is a compact yet powerful [object detection](https://www.ultralytics.com/glossary/object-detection) dataset, consisting of the first 8 images from the COCO train 2017 set and converted to grayscale format—4 for training and 4 for validation. This dataset is specifically designed for rapid testing, debugging, and experimentation with [YOLO](https://docs.ultralytics.com/models/yolo26/) grayscale models and training pipelines. Its small size makes it highly manageable, while its diversity ensures it serves as an effective sanity check before scaling up to larger datasets.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
|
|
@ -18,10 +18,10 @@ The [Ultralytics](https://www.ultralytics.com/) COCO8-Grayscale dataset is a com
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO11 on Grayscale Datasets 🚀
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on Grayscale Datasets 🚀
|
||||
</p>
|
||||
|
||||
COCO8-Grayscale is fully compatible with [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLO11](../../models/yolo11.md), enabling seamless integration into your computer vision workflows.
|
||||
COCO8-Grayscale is fully compatible with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](../../models/yolo26.md), enabling seamless integration into your computer vision workflows.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
|
|
@ -39,7 +39,7 @@ The COCO8-Grayscale dataset configuration is defined in a YAML (Yet Another Mark
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the COCO8-Grayscale dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following examples. For a full list of training options, see the [YOLO Training documentation](../../modes/train.md).
|
||||
To train a YOLO26n model on the COCO8-Grayscale dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following examples. For a full list of training options, see the [YOLO Training documentation](../../modes/train.md).
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -48,8 +48,8 @@ To train a YOLO11n model on the COCO8-Grayscale dataset for 100 [epochs](https:/
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained YOLO11n model
|
||||
model = YOLO("yolo11n.pt")
|
||||
# Load a pretrained YOLO26n model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on COCO8-Grayscale
|
||||
results = model.train(data="coco8-grayscale.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -58,8 +58,8 @@ To train a YOLO11n model on the COCO8-Grayscale dataset for 100 [epochs](https:/
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train YOLO11n on COCO8-Grayscale using the command line
|
||||
yolo detect train data=coco8-grayscale.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
# Train YOLO26n on COCO8-Grayscale using the command line
|
||||
yolo detect train data=coco8-grayscale.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -97,11 +97,11 @@ Special thanks to the [COCO Consortium](https://cocodataset.org/#home) for their
|
|||
|
||||
### What Is the Ultralytics COCO8-Grayscale Dataset Used For?
|
||||
|
||||
The Ultralytics COCO8-Grayscale dataset is designed for rapid testing and debugging of [object detection](https://www.ultralytics.com/glossary/object-detection) models. With only 8 images (4 for training, 4 for validation), it is ideal for verifying your [YOLO](https://docs.ultralytics.com/models/yolo11/) training pipelines and ensuring everything works as expected before scaling to larger datasets. Explore the [COCO8-Grayscale YAML configuration](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-grayscale.yaml) for more details.
|
||||
The Ultralytics COCO8-Grayscale dataset is designed for rapid testing and debugging of [object detection](https://www.ultralytics.com/glossary/object-detection) models. With only 8 images (4 for training, 4 for validation), it is ideal for verifying your [YOLO](https://docs.ultralytics.com/models/yolo26/) training pipelines and ensuring everything works as expected before scaling to larger datasets. Explore the [COCO8-Grayscale YAML configuration](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-grayscale.yaml) for more details.
|
||||
|
||||
### How Do I Train a YOLO11 Model Using the COCO8-Grayscale Dataset?
|
||||
### How Do I Train a YOLO26 Model Using the COCO8-Grayscale Dataset?
|
||||
|
||||
You can train a YOLO11 model on COCO8-Grayscale using either Python or the CLI:
|
||||
You can train a YOLO26 model on COCO8-Grayscale using either Python or the CLI:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -110,8 +110,8 @@ You can train a YOLO11 model on COCO8-Grayscale using either Python or the CLI:
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained YOLO11n model
|
||||
model = YOLO("yolo11n.pt")
|
||||
# Load a pretrained YOLO26n model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on COCO8-Grayscale
|
||||
results = model.train(data="coco8-grayscale.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -120,19 +120,19 @@ You can train a YOLO11 model on COCO8-Grayscale using either Python or the CLI:
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=coco8-grayscale.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=coco8-grayscale.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For additional training options, refer to the [YOLO Training documentation](../../modes/train.md).
|
||||
|
||||
### Why Should I Use Ultralytics HUB for Managing My COCO8-Grayscale Training?
|
||||
### Why Should I Use Ultralytics Platform for Managing My COCO8-Grayscale Training?
|
||||
|
||||
[Ultralytics HUB](https://hub.ultralytics.com/) streamlines dataset management, training, and deployment for [YOLO](https://docs.ultralytics.com/models/yolo11/) models—including COCO8-Grayscale. With features like cloud training, real-time monitoring, and intuitive dataset handling, HUB enables you to launch experiments with a single click and eliminates manual setup hassles. Learn more about [Ultralytics HUB](https://hub.ultralytics.com/) and how it can accelerate your computer vision projects.
|
||||
[Ultralytics Platform](https://platform.ultralytics.com/) streamlines dataset management, training, and deployment for [YOLO](https://docs.ultralytics.com/models/yolo26/) models—including COCO8-Grayscale. With features like cloud training, real-time monitoring, and intuitive dataset handling, HUB enables you to launch experiments with a single click and eliminates manual setup hassles. Learn more about [Ultralytics Platform](https://platform.ultralytics.com/) and how it can accelerate your computer vision projects.
|
||||
|
||||
### What Are the Benefits of Using Mosaic Augmentation in Training With the COCO8-Grayscale Dataset?
|
||||
|
||||
Mosaic augmentation, as used in COCO8-Grayscale training, combines multiple images into one during each batch. This increases the diversity of objects and backgrounds, helping your [YOLO](https://docs.ultralytics.com/models/yolo11/) model generalize better to new scenarios. Mosaic augmentation is especially valuable for small datasets, as it maximizes the information available in each training step. For more on this, see the [training guide](#usage).
|
||||
Mosaic augmentation, as used in COCO8-Grayscale training, combines multiple images into one during each batch. This increases the diversity of objects and backgrounds, helping your [YOLO](https://docs.ultralytics.com/models/yolo26/) model generalize better to new scenarios. Mosaic augmentation is especially valuable for small datasets, as it maximizes the information available in each training step. For more on this, see the [training guide](#usage).
|
||||
|
||||
### How Can I Validate My YOLO11 Model Trained on the COCO8-Grayscale Dataset?
|
||||
### How Can I Validate My YOLO26 Model Trained on the COCO8-Grayscale Dataset?
|
||||
|
||||
To validate your YOLO11 model after training on COCO8-Grayscale, use the model's validation commands in either Python or CLI. This evaluates your model's performance using standard metrics. For step-by-step instructions, visit the [YOLO Validation documentation](../../modes/val.md).
|
||||
To validate your YOLO26 model after training on COCO8-Grayscale, use the model's validation commands in either Python or CLI. This evaluates your model's performance using standard metrics. For step-by-step instructions, visit the [YOLO Validation documentation](../../modes/val.md).
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore the Ultralytics COCO8-Multispectral dataset, an enhanced version of COCO8 with interpolated spectral channels, ideal for testing multispectral object detection models and training pipelines.
|
||||
keywords: COCO8-Multispectral, Ultralytics, dataset, multispectral, object detection, YOLO11, training, validation, machine learning, computer vision
|
||||
keywords: COCO8-Multispectral, Ultralytics, dataset, multispectral, object detection, YOLO26, training, validation, machine learning, computer vision
|
||||
---
|
||||
|
||||
# COCO8-Multispectral Dataset
|
||||
|
|
@ -14,7 +14,7 @@ The [Ultralytics](https://www.ultralytics.com/) COCO8-Multispectral dataset is a
|
|||
<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/coco8-multispectral-overview.avif" alt="Multispectral Imagery Overview">
|
||||
</p>
|
||||
|
||||
COCO8-Multispectral is fully compatible with [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLO11](../../models/yolo11.md), ensuring seamless integration into your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) workflows.
|
||||
COCO8-Multispectral is fully compatible with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](../../models/yolo26.md), ensuring seamless integration into your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) workflows.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
|
|
@ -24,7 +24,7 @@ COCO8-Multispectral is fully compatible with [Ultralytics HUB](https://hub.ultra
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO11 on Multispectral Datasets | Multi-Channel VisionAI 🚀
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on Multispectral Datasets | Multi-Channel VisionAI 🚀
|
||||
</p>
|
||||
|
||||
## Dataset Generation
|
||||
|
|
@ -67,7 +67,7 @@ The COCO8-Multispectral dataset is configured using a YAML file, which defines d
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the COCO8-Multispectral dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following examples. For a comprehensive list of training options, refer to the [YOLO Training documentation](../../modes/train.md).
|
||||
To train a YOLO26n model on the COCO8-Multispectral dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following examples. For a comprehensive list of training options, refer to the [YOLO Training documentation](../../modes/train.md).
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -76,8 +76,8 @@ To train a YOLO11n model on the COCO8-Multispectral dataset for 100 [epochs](htt
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained YOLO11n model
|
||||
model = YOLO("yolo11n.pt")
|
||||
# Load a pretrained YOLO26n model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on COCO8-Multispectral
|
||||
results = model.train(data="coco8-multispectral.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -86,11 +86,11 @@ To train a YOLO11n model on the COCO8-Multispectral dataset for 100 [epochs](htt
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train YOLO11n on COCO8-Multispectral using the command line
|
||||
yolo detect train data=coco8-multispectral.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
# Train YOLO26n on COCO8-Multispectral using the command line
|
||||
yolo detect train data=coco8-multispectral.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For more details on model selection and best practices, explore the [Ultralytics YOLO model documentation](../../models/yolo11.md) and the [YOLO Model Training Tips guide](https://docs.ultralytics.com/guides/model-training-tips/).
|
||||
For more details on model selection and best practices, explore the [Ultralytics YOLO model documentation](../../models/yolo26.md) and the [YOLO Model Training Tips guide](https://docs.ultralytics.com/guides/model-training-tips/).
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
|
|
@ -127,15 +127,15 @@ Special thanks to the [COCO Consortium](https://cocodataset.org/#home) for their
|
|||
|
||||
### What Is the Ultralytics COCO8-Multispectral Dataset Used For?
|
||||
|
||||
The Ultralytics COCO8-Multispectral dataset is designed for rapid testing and debugging of [multispectral object detection](https://www.ultralytics.com/glossary/object-detection) models. With only 8 images (4 for training, 4 for validation), it is ideal for verifying your [YOLO](../../models/yolo11.md) training pipelines and ensuring everything works as expected before scaling to larger datasets. For more datasets to experiment with, visit the [Ultralytics Datasets Catalog](https://docs.ultralytics.com/datasets/).
|
||||
The Ultralytics COCO8-Multispectral dataset is designed for rapid testing and debugging of [multispectral object detection](https://www.ultralytics.com/glossary/object-detection) models. With only 8 images (4 for training, 4 for validation), it is ideal for verifying your [YOLO26](../../models/yolo26.md) training pipelines and ensuring everything works as expected before scaling to larger datasets. For more datasets to experiment with, visit the [Ultralytics Datasets Catalog](https://docs.ultralytics.com/datasets/).
|
||||
|
||||
### How Does Multispectral Data Improve Object Detection?
|
||||
|
||||
Multispectral data provides additional spectral information beyond standard RGB, enabling models to distinguish objects based on subtle differences in reflectance across wavelengths. This can enhance detection accuracy, especially in challenging scenarios. Learn more about [multispectral imaging](https://en.wikipedia.org/wiki/Multispectral_imaging) and its applications in [advanced computer vision](https://www.ultralytics.com/blog/ai-in-aviation-a-runway-to-smarter-airports).
|
||||
|
||||
### Is COCO8-Multispectral Compatible With Ultralytics HUB and YOLO Models?
|
||||
### Is COCO8-Multispectral Compatible With Ultralytics Platform and YOLO Models?
|
||||
|
||||
Yes, COCO8-Multispectral is fully compatible with [Ultralytics HUB](https://hub.ultralytics.com/) and all [YOLO models](../../models/yolo11.md), including the latest YOLO11. This allows you to easily integrate the dataset into your training and validation workflows.
|
||||
Yes, COCO8-Multispectral is fully compatible with [Ultralytics Platform](https://platform.ultralytics.com/) and all [YOLO models](../../models/yolo26.md), including the latest YOLO26. This allows you to easily integrate the dataset into your training and validation workflows.
|
||||
|
||||
### Where Can I Find More Information on Data Augmentation Techniques?
|
||||
|
||||
|
|
|
|||
|
|
@ -1,14 +1,14 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore the Ultralytics COCO8 dataset, a versatile and manageable set of 8 images perfect for testing object detection models and training pipelines.
|
||||
keywords: COCO8, Ultralytics, dataset, object detection, YOLO11, training, validation, machine learning, computer vision
|
||||
keywords: COCO8, Ultralytics, dataset, object detection, YOLO26, training, validation, machine learning, computer vision
|
||||
---
|
||||
|
||||
# COCO8 Dataset
|
||||
|
||||
## Introduction
|
||||
|
||||
The [Ultralytics](https://www.ultralytics.com/) COCO8 dataset is a compact yet powerful [object detection](https://www.ultralytics.com/glossary/object-detection) dataset, consisting of the first 8 images from the COCO train 2017 set—4 for training and 4 for validation. This dataset is specifically designed for rapid testing, debugging, and experimentation with [YOLO](https://docs.ultralytics.com/models/yolo11/) models and training pipelines. Its small size makes it highly manageable, while its diversity ensures it serves as an effective sanity check before scaling up to larger datasets.
|
||||
The [Ultralytics](https://www.ultralytics.com/) COCO8 dataset is a compact yet powerful [object detection](https://www.ultralytics.com/glossary/object-detection) dataset, consisting of the first 8 images from the COCO train 2017 set—4 for training and 4 for validation. This dataset is specifically designed for rapid testing, debugging, and experimentation with [YOLO](https://docs.ultralytics.com/models/yolo26/) models and training pipelines. Its small size makes it highly manageable, while its diversity ensures it serves as an effective sanity check before scaling up to larger datasets.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
|
|
@ -21,7 +21,7 @@ The [Ultralytics](https://www.ultralytics.com/) COCO8 dataset is a compact yet p
|
|||
<strong>Watch:</strong> Ultralytics COCO Dataset Overview
|
||||
</p>
|
||||
|
||||
COCO8 is fully compatible with [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLO11](../../models/yolo11.md), enabling seamless integration into your computer vision workflows.
|
||||
COCO8 is fully compatible with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](../../models/yolo26.md), enabling seamless integration into your computer vision workflows.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
|
|
@ -35,7 +35,7 @@ The COCO8 dataset configuration is defined in a YAML (Yet Another Markup Languag
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the COCO8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following examples. For a full list of training options, see the [YOLO Training documentation](../../modes/train.md).
|
||||
To train a YOLO26n model on the COCO8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following examples. For a full list of training options, see the [YOLO Training documentation](../../modes/train.md).
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -44,8 +44,8 @@ To train a YOLO11n model on the COCO8 dataset for 100 [epochs](https://www.ultra
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained YOLO11n model
|
||||
model = YOLO("yolo11n.pt")
|
||||
# Load a pretrained YOLO26n model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on COCO8
|
||||
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -54,8 +54,8 @@ To train a YOLO11n model on the COCO8 dataset for 100 [epochs](https://www.ultra
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train YOLO11n on COCO8 using the command line
|
||||
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
# Train YOLO26n on COCO8 using the command line
|
||||
yolo detect train data=coco8.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -93,11 +93,11 @@ Special thanks to the [COCO Consortium](https://cocodataset.org/#home) for their
|
|||
|
||||
### What Is the Ultralytics COCO8 Dataset Used For?
|
||||
|
||||
The Ultralytics COCO8 dataset is designed for rapid testing and debugging of [object detection](https://www.ultralytics.com/glossary/object-detection) models. With only 8 images (4 for training, 4 for validation), it is ideal for verifying your [YOLO](https://docs.ultralytics.com/models/yolo11/) training pipelines and ensuring everything works as expected before scaling to larger datasets. Explore the [COCO8 YAML configuration](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8.yaml) for more details.
|
||||
The Ultralytics COCO8 dataset is designed for rapid testing and debugging of [object detection](https://www.ultralytics.com/glossary/object-detection) models. With only 8 images (4 for training, 4 for validation), it is ideal for verifying your [YOLO](https://docs.ultralytics.com/models/yolo26/) training pipelines and ensuring everything works as expected before scaling to larger datasets. Explore the [COCO8 YAML configuration](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8.yaml) for more details.
|
||||
|
||||
### How Do I Train a YOLO11 Model Using the COCO8 Dataset?
|
||||
### How Do I Train a YOLO26 Model Using the COCO8 Dataset?
|
||||
|
||||
You can train a YOLO11 model on COCO8 using either Python or the CLI:
|
||||
You can train a YOLO26 model on COCO8 using either Python or the CLI:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -106,8 +106,8 @@ You can train a YOLO11 model on COCO8 using either Python or the CLI:
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained YOLO11n model
|
||||
model = YOLO("yolo11n.pt")
|
||||
# Load a pretrained YOLO26n model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on COCO8
|
||||
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -116,19 +116,19 @@ You can train a YOLO11 model on COCO8 using either Python or the CLI:
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=coco8.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For additional training options, refer to the [YOLO Training documentation](../../modes/train.md).
|
||||
|
||||
### Why Should I Use Ultralytics HUB for Managing My COCO8 Training?
|
||||
### Why Should I Use Ultralytics Platform for Managing My COCO8 Training?
|
||||
|
||||
[Ultralytics HUB](https://hub.ultralytics.com/) streamlines dataset management, training, and deployment for [YOLO](https://docs.ultralytics.com/models/yolo11/) models—including COCO8. With features like cloud training, real-time monitoring, and intuitive dataset handling, HUB enables you to launch experiments with a single click and eliminates manual setup hassles. Learn more about [Ultralytics HUB](https://hub.ultralytics.com/) and how it can accelerate your computer vision projects.
|
||||
[Ultralytics Platform](https://platform.ultralytics.com/) streamlines dataset management, training, and deployment for [YOLO](https://docs.ultralytics.com/models/yolo26/) models—including COCO8. With features like cloud training, real-time monitoring, and intuitive dataset handling, HUB enables you to launch experiments with a single click and eliminates manual setup hassles. Learn more about [Ultralytics Platform](https://platform.ultralytics.com/) and how it can accelerate your computer vision projects.
|
||||
|
||||
### What Are the Benefits of Using Mosaic Augmentation in Training With the COCO8 Dataset?
|
||||
|
||||
Mosaic augmentation, as used in COCO8 training, combines multiple images into one during each batch. This increases the diversity of objects and backgrounds, helping your [YOLO](https://docs.ultralytics.com/models/yolo11/) model generalize better to new scenarios. Mosaic augmentation is especially valuable for small datasets, as it maximizes the information available in each training step. For more on this, see the [training guide](#usage).
|
||||
Mosaic augmentation, as used in COCO8 training, combines multiple images into one during each batch. This increases the diversity of objects and backgrounds, helping your [YOLO](https://docs.ultralytics.com/models/yolo26/) model generalize better to new scenarios. Mosaic augmentation is especially valuable for small datasets, as it maximizes the information available in each training step. For more on this, see the [training guide](#usage).
|
||||
|
||||
### How Can I Validate My YOLO11 Model Trained on the COCO8 Dataset?
|
||||
### How Can I Validate My YOLO26 Model Trained on the COCO8 Dataset?
|
||||
|
||||
To validate your YOLO11 model after training on COCO8, use the model's validation commands in either Python or CLI. This evaluates your model's performance using standard metrics. For step-by-step instructions, visit the [YOLO Validation documentation](../../modes/val.md).
|
||||
To validate your YOLO26 model after training on COCO8, use the model's validation commands in either Python or CLI. This evaluates your model's performance using standard metrics. For step-by-step instructions, visit the [YOLO Validation documentation](../../modes/val.md).
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Discover Construction-PPE, a specialized dataset for detecting helmets, vests, gloves, boots, and goggles in real-world construction sites. Includes compliant and non-compliant scenarios for AI-powered safety monitoring.
|
||||
keywords: Construction-PPE, PPE dataset, safety compliance, construction workers, object detection, YOLO11, workplace safety, computer vision
|
||||
keywords: Construction-PPE, PPE dataset, safety compliance, construction workers, object detection, YOLO26, workplace safety, computer vision
|
||||
---
|
||||
|
||||
# Construction-PPE Dataset
|
||||
|
|
@ -62,7 +62,7 @@ The Construction-PPE dataset includes a YAML configuration file that defines the
|
|||
|
||||
## Usage
|
||||
|
||||
You can train a YOLO11n model on the Construction-PPE dataset for 100 epochs with an image size of 640. The following examples show how to get started quickly. For more options and advanced configurations, see the [Training guide](../../modes/train.md).
|
||||
You can train a YOLO26n model on the Construction-PPE dataset for 100 epochs with an image size of 640. The following examples show how to get started quickly. For more options and advanced configurations, see the [Training guide](../../modes/train.md).
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -72,7 +72,7 @@ You can train a YOLO11n model on the Construction-PPE dataset for 100 epochs wit
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load pretrained model
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on Construction-PPE dataset
|
||||
model.train(data="construction-ppe.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -81,7 +81,7 @@ You can train a YOLO11n model on the Construction-PPE dataset for 100 epochs wit
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=construction-ppe.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=construction-ppe.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -125,7 +125,7 @@ The dataset covers helmets, vests, gloves, boots, goggles, and workers, along wi
|
|||
|
||||
### How can I train a YOLO model using the Construction-PPE dataset?
|
||||
|
||||
To train a YOLO11 model using the Construction-PPE dataset, you can use the following code snippets:
|
||||
To train a YOLO26 model using the Construction-PPE dataset, you can use the following code snippets:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -135,7 +135,7 @@ To train a YOLO11 model using the Construction-PPE dataset, you can use the foll
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="construction-ppe.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -145,7 +145,7 @@ To train a YOLO11 model using the Construction-PPE dataset, you can use the foll
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=construction-ppe.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=construction-ppe.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### Is this dataset suitable for real-world applications?
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the Global Wheat Head Dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n model on the Global Wheat Head Dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -48,7 +48,7 @@ To train a YOLO11n model on the Global Wheat Head Dataset for 100 [epochs](https
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="GlobalWheat2020.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -58,7 +58,7 @@ To train a YOLO11n model on the Global Wheat Head Dataset for 100 [epochs](https
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=GlobalWheat2020.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=GlobalWheat2020.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
|
@ -96,9 +96,9 @@ We would like to acknowledge the researchers and institutions that contributed t
|
|||
|
||||
The Global Wheat Head Dataset is primarily used for developing and training deep learning models aimed at wheat head detection. This is crucial for applications in [wheat phenotyping](https://www.ultralytics.com/blog/from-farm-to-table-how-ai-drives-innovation-in-agriculture) and crop management, allowing for more accurate estimations of wheat head density, size, and overall crop yield potential. Accurate detection methods help in assessing crop health and maturity, essential for efficient crop management.
|
||||
|
||||
### How do I train a YOLO11n model on the Global Wheat Head Dataset?
|
||||
### How do I train a YOLO26n model on the Global Wheat Head Dataset?
|
||||
|
||||
To train a YOLO11n model on the Global Wheat Head Dataset, you can use the following code snippets. Make sure you have the `GlobalWheat2020.yaml` configuration file specifying dataset paths and classes:
|
||||
To train a YOLO26n model on the Global Wheat Head Dataset, you can use the following code snippets. Make sure you have the `GlobalWheat2020.yaml` configuration file specifying dataset paths and classes:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -108,7 +108,7 @@ To train a YOLO11n model on the Global Wheat Head Dataset, you can use the follo
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="GlobalWheat2020.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -118,7 +118,7 @@ To train a YOLO11n model on the Global Wheat Head Dataset, you can use the follo
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=GlobalWheat2020.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=GlobalWheat2020.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
|
@ -136,10 +136,10 @@ These features facilitate the development of robust models capable of generaliza
|
|||
|
||||
### Where can I find the configuration YAML file for the Global Wheat Head Dataset?
|
||||
|
||||
The configuration YAML file for the Global Wheat Head Dataset, named `GlobalWheat2020.yaml`, is available on GitHub. You can access it at <https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/GlobalWheat2020.yaml>. This file contains necessary information about dataset paths, classes, and other configuration details needed for model training in [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/).
|
||||
The configuration YAML file for the Global Wheat Head Dataset, named `GlobalWheat2020.yaml`, is available on GitHub. You can access it at <https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/GlobalWheat2020.yaml>. This file contains necessary information about dataset paths, classes, and other configuration details needed for model training in [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo26/).
|
||||
|
||||
### Why is wheat head detection important in crop management?
|
||||
|
||||
Wheat head detection is critical in crop management because it enables accurate estimation of wheat head density and size, which are essential for evaluating crop health, maturity, and yield potential. By leveraging [deep learning models](https://docs.ultralytics.com/models/) trained on datasets like the Global Wheat Head Dataset, farmers and researchers can better monitor and manage crops, leading to improved productivity and optimized resource use in agricultural practices. This technological advancement supports [sustainable agriculture](https://www.ultralytics.com/blog/real-time-crop-health-monitoring-with-ultralytics-yolo11) and food security initiatives.
|
||||
Wheat head detection is critical in crop management because it enables accurate estimation of wheat head density and size, which are essential for evaluating crop health, maturity, and yield potential. By leveraging [deep learning models](https://docs.ultralytics.com/models/) trained on datasets like the Global Wheat Head Dataset, farmers and researchers can better monitor and manage crops, leading to improved productivity and optimized resource use in agricultural practices. This technological advancement supports [sustainable agriculture](https://www.ultralytics.com/blog/real-time-crop-health-monitoring-with-ultralytics-yolo26) and food security initiatives.
|
||||
|
||||
For more information on applications of AI in agriculture, visit [AI in Agriculture](https://www.ultralytics.com/solutions/ai-in-agriculture).
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Discover HomeObjects-3K, a rich indoor object detection dataset with 12 classes like bed, sofa, TV, and laptop. Ideal for computer vision in smart homes, robotics, and AR.
|
||||
keywords: HomeObjects-3K, indoor dataset, household items, object detection, computer vision, YOLO11, smart home AI, robotics dataset
|
||||
keywords: HomeObjects-3K, indoor dataset, household items, object detection, computer vision, YOLO26, smart home AI, robotics dataset
|
||||
---
|
||||
|
||||
# HomeObjects-3K Dataset
|
||||
|
|
@ -18,7 +18,7 @@ The HomeObjects-3K dataset is a curated collection of common household object im
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO11 on HomeObjects-3K Dataset | Detection, Validation & ONNX Export 🚀
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on HomeObjects-3K Dataset | Detection, Validation & ONNX Export 🚀
|
||||
</p>
|
||||
|
||||
## Dataset Structure
|
||||
|
|
@ -53,7 +53,7 @@ The dataset supports 12 everyday object categories, covering furniture, electron
|
|||
|
||||
HomeObjects-3K enables a wide spectrum of applications in indoor computer vision, spanning both research and real-world product development:
|
||||
|
||||
- **Indoor object detection**: Use models like [Ultralytics YOLO11](../../models/yolo11.md) to find and locate common home items like beds, chairs, lamps, and laptops in images. This helps with real-time understanding of indoor scenes.
|
||||
- **Indoor object detection**: Use models like [Ultralytics YOLO26](../../models/yolo26.md) to find and locate common home items like beds, chairs, lamps, and laptops in images. This helps with real-time understanding of indoor scenes.
|
||||
|
||||
- **Scene layout parsing**: In robotics and smart home systems, this helps devices understand how rooms are arranged, where objects like doors, windows, and furniture are, so they can navigate safely and interact with their environment properly.
|
||||
|
||||
|
|
@ -76,7 +76,7 @@ You can access the `HomeObjects-3K.yaml` file directly from the Ultralytics repo
|
|||
|
||||
## Usage
|
||||
|
||||
You can train a YOLO11n model on the HomeObjects-3K dataset for 100 epochs using an image size of 640. The examples below show how to get started. For more training options and detailed settings, check the [Training](../../modes/train.md) guide.
|
||||
You can train a YOLO26n model on the HomeObjects-3K dataset for 100 epochs using an image size of 640. The examples below show how to get started. For more training options and detailed settings, check the [Training](../../modes/train.md) guide.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -86,7 +86,7 @@ You can train a YOLO11n model on the HomeObjects-3K dataset for 100 epochs using
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load pretrained model
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on HomeObjects-3K dataset
|
||||
model.train(data="HomeObjects-3K.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -95,7 +95,7 @@ You can train a YOLO11n model on the HomeObjects-3K dataset for 100 epochs using
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=HomeObjects-3K.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=HomeObjects-3K.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -138,7 +138,7 @@ The dataset includes 12 of the most commonly encountered household items: bed, s
|
|||
|
||||
### How can I train a YOLO model using the HomeObjects-3K dataset?
|
||||
|
||||
To train a YOLO model like YOLO11n, you'll just need the `HomeObjects-3K.yaml` configuration file and the [pretrained model](../../models/index.md) weights. Whether you're using Python or the CLI, training can be launched with a single command. You can customize parameters such as epochs, image size, and batch size depending on your target performance and hardware setup.
|
||||
To train a YOLO model like YOLO26n, you'll just need the `HomeObjects-3K.yaml` configuration file and the [pretrained model](../../models/index.md) weights. Whether you're using Python or the CLI, training can be launched with a single command. You can customize parameters such as epochs, image size, and batch size depending on your target performance and hardware setup.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -148,7 +148,7 @@ To train a YOLO model like YOLO11n, you'll just need the `HomeObjects-3K.yaml` c
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load pretrained model
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on HomeObjects-3K dataset
|
||||
model.train(data="HomeObjects-3K.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -157,7 +157,7 @@ To train a YOLO model like YOLO11n, you'll just need the `HomeObjects-3K.yaml` c
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=HomeObjects-3K.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=HomeObjects-3K.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### Is this dataset suitable for beginner-level projects?
|
||||
|
|
|
|||
|
|
@ -44,7 +44,7 @@ Here's how you can use YOLO format datasets to train your model:
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -54,12 +54,12 @@ Here's how you can use YOLO format datasets to train your model:
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=coco8.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### Ultralytics NDJSON format
|
||||
|
||||
The NDJSON (Newline Delimited JSON) format provides an alternative way to define datasets for Ultralytics YOLO11 models. This format stores dataset metadata and annotations in a single file where each line contains a separate JSON object.
|
||||
The NDJSON (Newline Delimited JSON) format provides an alternative way to define datasets for Ultralytics YOLO models. This format stores dataset metadata and annotations in a single file where each line contains a separate JSON object.
|
||||
|
||||
An NDJSON dataset file contains:
|
||||
|
||||
|
|
@ -114,7 +114,7 @@ An NDJSON dataset file contains:
|
|||
|
||||
#### Usage Example
|
||||
|
||||
To use an NDJSON dataset with YOLO11, simply specify the path to the `.ndjson` file:
|
||||
To use an NDJSON dataset with YOLO26, simply specify the path to the `.ndjson` file:
|
||||
|
||||
!!! example
|
||||
|
||||
|
|
@ -124,7 +124,7 @@ To use an NDJSON dataset with YOLO11, simply specify the path to the `.ndjson` f
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train using NDJSON dataset
|
||||
results = model.train(data="path/to/dataset.ndjson", epochs=100, imgsz=640)
|
||||
|
|
@ -134,7 +134,7 @@ To use an NDJSON dataset with YOLO11, simply specify the path to the `.ndjson` f
|
|||
|
||||
```bash
|
||||
# Start training with NDJSON dataset
|
||||
yolo detect train data=path/to/dataset.ndjson model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=path/to/dataset.ndjson model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
#### Advantages of NDJSON format
|
||||
|
|
@ -193,7 +193,7 @@ You can easily convert labels from the popular [COCO dataset](coco.md) format to
|
|||
convert_coco(labels_dir="path/to/coco/annotations/")
|
||||
```
|
||||
|
||||
This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The process transforms the JSON-based COCO annotations into the simpler text-based YOLO format, making it compatible with [Ultralytics YOLO models](../../models/yolo11.md).
|
||||
This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The process transforms the JSON-based COCO annotations into the simpler text-based YOLO format, making it compatible with [Ultralytics YOLO models](../../models/yolo26.md).
|
||||
|
||||
Remember to double-check if the dataset you want to use is compatible with your model and follows the necessary format conventions. Properly formatted datasets are crucial for training successful object detection models.
|
||||
|
||||
|
|
@ -233,11 +233,11 @@ Ultralytics YOLO supports a wide range of datasets, including:
|
|||
- [Objects365](objects365.md)
|
||||
- [OpenImagesV7](open-images-v7.md)
|
||||
|
||||
Each dataset page provides detailed information on the structure and usage tailored for efficient YOLO11 training. Explore the full list in the [Supported Datasets](#supported-datasets) section.
|
||||
Each dataset page provides detailed information on the structure and usage tailored for efficient YOLO26 training. Explore the full list in the [Supported Datasets](#supported-datasets) section.
|
||||
|
||||
### How do I start training a YOLO11 model using my dataset?
|
||||
### How do I start training a YOLO26 model using my dataset?
|
||||
|
||||
To start training a YOLO11 model, ensure your dataset is formatted correctly and the paths are defined in a YAML file. Use the following script to begin training:
|
||||
To start training a YOLO26 model, ensure your dataset is formatted correctly and the paths are defined in a YAML file. Use the following script to begin training:
|
||||
|
||||
!!! example
|
||||
|
||||
|
|
@ -246,18 +246,18 @@ To start training a YOLO11 model, ensure your dataset is formatted correctly and
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo11n.pt") # Load a pretrained model
|
||||
model = YOLO("yolo26n.pt") # Load a pretrained model
|
||||
results = model.train(data="path/to/your_dataset.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=path/to/your_dataset.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=path/to/your_dataset.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
Refer to the [Usage](#usage-example) section for more details on utilizing different modes, including CLI commands.
|
||||
|
||||
### Where can I find practical examples of using Ultralytics YOLO for object detection?
|
||||
|
||||
Ultralytics provides numerous examples and practical guides for using YOLO11 in diverse applications. For a comprehensive overview, visit the [Ultralytics Blog](https://www.ultralytics.com/blog) where you can find case studies, detailed tutorials, and community stories showcasing object detection, segmentation, and more with YOLO11. For specific examples, check the [Usage](../../modes/predict.md) section in the documentation.
|
||||
Ultralytics provides numerous examples and practical guides for using YOLO26 in diverse applications. For a comprehensive overview, visit the [Ultralytics Blog](https://www.ultralytics.com/blog) where you can find case studies, detailed tutorials, and community stories showcasing object detection, segmentation, and more with YOLO26. For specific examples, check the [Usage](../../modes/predict.md) section in the documentation.
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore the Ultralytics kitti dataset, a benchmark dataset for computer vision tasks such as 3D object detection, depth estimation, and autonomous driving perception.
|
||||
keywords: kitti, Ultralytics, dataset, object detection, 3D vision, YOLO11, training, validation, self-driving cars, computer vision
|
||||
keywords: kitti, Ultralytics, dataset, object detection, 3D vision, YOLO26, training, validation, self-driving cars, computer vision
|
||||
---
|
||||
|
||||
# KITTI Dataset
|
||||
|
|
@ -18,10 +18,10 @@ The kitti dataset is one of the most influential benchmark datasets for autonomo
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO11 on the KITTI Dataset 🚀
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on the KITTI Dataset 🚀
|
||||
</p>
|
||||
|
||||
It is widely used for evaluating algorithms in object detection, depth estimation, optical flow, and visual odometry. The dataset is fully compatible with Ultralytics YOLO11 for 2D object detection tasks and can be easily integrated into the Ultralytics platform for training and evaluation.
|
||||
It is widely used for evaluating algorithms in object detection, depth estimation, optical flow, and visual odometry. The dataset is fully compatible with Ultralytics YOLO26 for 2D object detection tasks and can be easily integrated into the Ultralytics platform for training and evaluation.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
|
|
@ -55,7 +55,7 @@ Ultralytics defines the kitti dataset configuration using a YAML file. This file
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the kitti dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following commands. For more details, refer to the [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n model on the kitti dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following commands. For more details, refer to the [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -64,8 +64,8 @@ To train a YOLO11n model on the kitti dataset for 100 [epochs](https://www.ultra
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained YOLO11 model
|
||||
model = YOLO("yolo11n.pt")
|
||||
# Load a pretrained YOLO26 model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train on kitti dataset
|
||||
results = model.train(data="kitti.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -74,7 +74,7 @@ To train a YOLO11n model on the kitti dataset for 100 [epochs](https://www.ultra
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=kitti.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=kitti.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
You can also perform evaluation, [inference](../../modes/predict.md), and [export](../../modes/export.md) tasks directly from the command line or Python API using the same configuration file.
|
||||
|
|
@ -118,9 +118,9 @@ The dataset includes 5,985 labeled training images and 1,496 validation images c
|
|||
|
||||
kitti includes annotations for objects such as cars, pedestrians, cyclists, trucks, trams, and miscellaneous road users.
|
||||
|
||||
### Can I train Ultralytics YOLO11 models using the kitti dataset?
|
||||
### Can I train Ultralytics YOLO26 models using the kitti dataset?
|
||||
|
||||
Yes, kitti is fully compatible with Ultralytics YOLO11. You can [train](../../modes/train.md) and [validate](../../modes/val.md), models directly using the provided YAML configuration file.
|
||||
Yes, kitti is fully compatible with Ultralytics YOLO26. You can [train](../../modes/train.md) and [validate](../../modes/val.md), models directly using the provided YAML configuration file.
|
||||
|
||||
### Where can I find the kitti dataset configuration file?
|
||||
|
||||
|
|
|
|||
|
|
@ -42,7 +42,7 @@ The LVIS dataset is split into three subsets:
|
|||
|
||||
## Applications
|
||||
|
||||
The LVIS dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object detection (such as [YOLO](../../models/yolo11.md), [Faster R-CNN](https://arxiv.org/abs/1506.01497), and [SSD](https://arxiv.org/abs/1512.02325)), instance segmentation (such as [Mask R-CNN](https://arxiv.org/abs/1703.06870)). The dataset's diverse set of object categories, large number of annotated images, and standardized evaluation metrics make it an essential resource for computer vision researchers and practitioners.
|
||||
The LVIS dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object detection (such as [YOLO](../../models/yolo26.md), [Faster R-CNN](https://arxiv.org/abs/1506.01497), and [SSD](https://arxiv.org/abs/1512.02325)), instance segmentation (such as [Mask R-CNN](https://arxiv.org/abs/1703.06870)). The dataset's diverse set of object categories, large number of annotated images, and standardized evaluation metrics make it an essential resource for computer vision researchers and practitioners.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
|
|
@ -56,7 +56,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the LVIS dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n model on the LVIS dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -66,7 +66,7 @@ To train a YOLO11n model on the LVIS dataset for 100 [epochs](https://www.ultral
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="lvis.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -76,7 +76,7 @@ To train a YOLO11n model on the LVIS dataset for 100 [epochs](https://www.ultral
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=lvis.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=lvis.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -114,9 +114,9 @@ We would like to acknowledge the LVIS Consortium for creating and maintaining th
|
|||
|
||||
The [LVIS dataset](https://www.lvisdataset.org/) is a large-scale dataset with fine-grained vocabulary-level annotations developed by Facebook AI Research (FAIR). It is primarily used for object detection and instance segmentation, featuring over 1203 object categories and 2 million instance annotations. Researchers and practitioners use it to train and benchmark models like Ultralytics YOLO for advanced computer vision tasks. The dataset's extensive size and diversity make it an essential resource for pushing the boundaries of model performance in detection and segmentation.
|
||||
|
||||
### How can I train a YOLO11n model using the LVIS dataset?
|
||||
### How can I train a YOLO26n model using the LVIS dataset?
|
||||
|
||||
To train a YOLO11n model on the LVIS dataset for 100 epochs with an image size of 640, follow the example below. This process utilizes Ultralytics' framework, which offers comprehensive training features.
|
||||
To train a YOLO26n model on the LVIS dataset for 100 epochs with an image size of 640, follow the example below. This process utilizes Ultralytics' framework, which offers comprehensive training features.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -126,7 +126,7 @@ To train a YOLO11n model on the LVIS dataset for 100 epochs with an image size o
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="lvis.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -137,7 +137,7 @@ To train a YOLO11n model on the LVIS dataset for 100 epochs with an image size o
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=lvis.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=lvis.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For detailed training configurations, refer to the [Training](../../modes/train.md) documentation.
|
||||
|
|
@ -148,7 +148,7 @@ The images in the LVIS dataset are the same as those in the [COCO dataset](./coc
|
|||
|
||||
### Why should I use Ultralytics YOLO for training on the LVIS dataset?
|
||||
|
||||
Ultralytics YOLO models, including the latest YOLO11, are optimized for real-time object detection with state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed. They support a wide range of annotations, such as the fine-grained ones provided by the LVIS dataset, making them ideal for advanced computer vision applications. Moreover, Ultralytics offers seamless integration with various [training](../../modes/train.md), [validation](../../modes/val.md), and [prediction](../../modes/predict.md) modes, ensuring efficient model development and deployment.
|
||||
Ultralytics YOLO models, including the latest YOLO26, are optimized for real-time object detection with state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed. They support a wide range of annotations, such as the fine-grained ones provided by the LVIS dataset, making them ideal for advanced computer vision applications. Moreover, Ultralytics offers seamless integration with various [training](../../modes/train.md), [validation](../../modes/val.md), and [prediction](../../modes/predict.md) modes, ensuring efficient model development and deployment.
|
||||
|
||||
### Can I see some sample annotations from the LVIS dataset?
|
||||
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ The medical-pills detection dataset is a proof-of-concept (POC) dataset, careful
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to train Ultralytics YOLO11 Model on Medical Pills Detection Dataset in <a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-medical-pills-dataset.ipynb">Google Colab</a>
|
||||
<strong>Watch:</strong> How to train Ultralytics YOLO26 Model on Medical Pills Detection Dataset in <a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-medical-pills-dataset.ipynb">Google Colab</a>
|
||||
</p>
|
||||
|
||||
This dataset serves as a foundational resource for automating essential [tasks](https://docs.ultralytics.com/tasks/) such as quality control, packaging automation, and efficient sorting in pharmaceutical workflows. By integrating this dataset into projects, researchers and developers can explore innovative [solutions](https://docs.ultralytics.com/solutions/) that enhance [accuracy](https://www.ultralytics.com/glossary/accuracy), streamline operations, and ultimately contribute to improved healthcare outcomes.
|
||||
|
|
@ -52,7 +52,7 @@ A YAML configuration file is provided to define the dataset's structure, includi
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the medical-pills dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following examples. For detailed arguments, refer to the model's [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n model on the medical-pills dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following examples. For detailed arguments, refer to the model's [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -62,7 +62,7 @@ To train a YOLO11n model on the medical-pills dataset for 100 [epochs](https://w
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="medical-pills.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -72,7 +72,7 @@ To train a YOLO11n model on the medical-pills dataset for 100 [epochs](https://w
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=medical-pills.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=medical-pills.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
!!! example "Inference Example"
|
||||
|
|
@ -136,9 +136,9 @@ If you use the Medical-pills dataset in your research or development work, pleas
|
|||
|
||||
The dataset includes 92 images for training and 23 images for validation. Each image is annotated with the class `pill`, enabling effective training and evaluation of models for pharmaceutical applications.
|
||||
|
||||
### How can I train a YOLO11 model on the medical-pills dataset?
|
||||
### How can I train a YOLO26 model on the medical-pills dataset?
|
||||
|
||||
You can train a YOLO11 model for 100 epochs with an image size of 640px using the Python or CLI methods provided. Refer to the [Training Example](#usage) section for detailed instructions and check the [YOLO11 documentation](../../models/yolo11.md) for more information on model capabilities.
|
||||
You can train a YOLO26 model for 100 epochs with an image size of 640px using the Python or CLI methods provided. Refer to the [Training Example](#usage) section for detailed instructions and check the [YOLO26 documentation](../../models/yolo26.md) for more information on model capabilities.
|
||||
|
||||
### What are the benefits of using the medical-pills dataset in AI projects?
|
||||
|
||||
|
|
@ -146,7 +146,7 @@ The dataset enables automation in pill detection, contributing to counterfeit pr
|
|||
|
||||
### How do I perform inference on the medical-pills dataset?
|
||||
|
||||
Inference can be done using Python or CLI methods with a fine-tuned YOLO11 model. Refer to the [Inference Example](#usage) section for code snippets and the [Predict mode documentation](../../modes/predict.md) for additional options.
|
||||
Inference can be done using Python or CLI methods with a fine-tuned YOLO26 model. Refer to the [Inference Example](#usage) section for code snippets and the [Predict mode documentation](../../modes/predict.md) for additional options.
|
||||
|
||||
### Where can I find the YAML configuration file for the medical-pills dataset?
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore the Objects365 Dataset with 2M images and 30M bounding boxes across 365 categories. Enhance your object detection models with diverse, high-quality data.
|
||||
keywords: Objects365 dataset, object detection, machine learning, deep learning, computer vision, annotated images, bounding boxes, YOLO11, high-resolution images, dataset configuration
|
||||
keywords: Objects365 dataset, object detection, machine learning, deep learning, computer vision, annotated images, bounding boxes, YOLO26, high-resolution images, dataset configuration
|
||||
---
|
||||
|
||||
# Objects365 Dataset
|
||||
|
|
@ -16,7 +16,7 @@ The [Objects365](https://www.objects365.org/) dataset is a large-scale, high-qua
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO11 on the Objects365 Dataset with Ultralytics | 2M Annotations 🚀
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on the Objects365 Dataset with Ultralytics | 2M Annotations 🚀
|
||||
</p>
|
||||
|
||||
## Key Features
|
||||
|
|
@ -49,7 +49,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the Objects365 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n model on the Objects365 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -59,7 +59,7 @@ To train a YOLO11n model on the Objects365 dataset for 100 [epochs](https://www.
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -69,7 +69,7 @@ To train a YOLO11n model on the Objects365 dataset for 100 [epochs](https://www.
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=Objects365.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=Objects365.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
|
@ -108,9 +108,9 @@ We would like to acknowledge the team of researchers who created and maintain th
|
|||
|
||||
The [Objects365 dataset](https://www.objects365.org/) is designed for object detection tasks in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision. It provides a large-scale, high-quality dataset with 2 million annotated images and 30 million bounding boxes across 365 categories. Leveraging such a diverse dataset helps improve the performance and generalization of object detection models, making it invaluable for research and development in the field.
|
||||
|
||||
### How can I train a YOLO11 model on the Objects365 dataset?
|
||||
### How can I train a YOLO26 model on the Objects365 dataset?
|
||||
|
||||
To train a YOLO11n model using the Objects365 dataset for 100 epochs with an image size of 640, follow these instructions:
|
||||
To train a YOLO26n model using the Objects365 dataset for 100 epochs with an image size of 640, follow these instructions:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -120,7 +120,7 @@ To train a YOLO11n model using the Objects365 dataset for 100 epochs with an ima
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -130,7 +130,7 @@ To train a YOLO11n model using the Objects365 dataset for 100 epochs with an ima
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=Objects365.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=Objects365.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
Refer to the [Training](../../modes/train.md) page for a comprehensive list of available arguments.
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore the comprehensive Open Images V7 dataset by Google. Learn about its annotations, applications, and use YOLO11 pretrained models for computer vision tasks.
|
||||
keywords: Open Images V7, Google dataset, computer vision, YOLO11 models, object detection, image segmentation, visual relationships, AI research, Ultralytics
|
||||
description: Explore the comprehensive Open Images V7 dataset by Google. Learn about its annotations, applications, and use YOLO26 pretrained models for computer vision tasks.
|
||||
keywords: Open Images V7, Google dataset, computer vision, YOLO26 models, object detection, image segmentation, visual relationships, AI research, Ultralytics
|
||||
---
|
||||
|
||||
# Open Images V7 Dataset
|
||||
|
|
@ -23,11 +23,11 @@ keywords: Open Images V7, Google dataset, computer vision, YOLO11 models, object
|
|||
|
||||
| Model | size<br><sup>(pixels)</sup> | mAP<sup>val<br>50-95</sup> | Speed<br><sup>CPU ONNX<br>(ms)</sup> | Speed<br><sup>A100 TensorRT<br>(ms)</sup> | params<br><sup>(M)</sup> | FLOPs<br><sup>(B)</sup> |
|
||||
| ----------------------------------------------------------------------------------------- | --------------------------- | -------------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ----------------------- |
|
||||
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
|
||||
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
|
||||
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
|
||||
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
|
||||
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
|
||||
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
|
||||
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
|
||||
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
|
||||
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
|
||||
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
|
||||
|
||||
You can use these pretrained models for inference or fine-tuning as follows.
|
||||
|
||||
|
|
@ -106,7 +106,7 @@ Ultralytics maintains an `open-images-v7.yaml` file that specifies the dataset p
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the Open Images V7 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n model on the Open Images V7 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! warning
|
||||
|
||||
|
|
@ -124,8 +124,8 @@ To train a YOLO11n model on the Open Images V7 dataset for 100 [epochs](https://
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a COCO-pretrained YOLO11n model
|
||||
model = YOLO("yolo11n.pt")
|
||||
# Load a COCO-pretrained YOLO26n model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on the Open Images V7 dataset
|
||||
results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -134,8 +134,8 @@ To train a YOLO11n model on the Open Images V7 dataset for 100 [epochs](https://
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train a COCO-pretrained YOLO11n model on the Open Images V7 dataset
|
||||
yolo detect train data=open-images-v7.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
# Train a COCO-pretrained YOLO26n model on the Open Images V7 dataset
|
||||
yolo detect train data=open-images-v7.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
|
@ -173,9 +173,9 @@ A heartfelt acknowledgment goes out to the Google AI team for creating and maint
|
|||
|
||||
Open Images V7 is an extensive and versatile dataset created by Google, designed to advance research in computer vision. It includes image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives, making it ideal for various computer vision tasks such as object detection, segmentation, and relationship detection.
|
||||
|
||||
### How do I train a YOLO11 model on the Open Images V7 dataset?
|
||||
### How do I train a YOLO26 model on the Open Images V7 dataset?
|
||||
|
||||
To train a YOLO11 model on the Open Images V7 dataset, you can use both Python and CLI commands. Here's an example of training the YOLO11n model for 100 epochs with an image size of 640:
|
||||
To train a YOLO26 model on the Open Images V7 dataset, you can use both Python and CLI commands. Here's an example of training the YOLO26n model for 100 epochs with an image size of 640:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -184,8 +184,8 @@ To train a YOLO11 model on the Open Images V7 dataset, you can use both Python a
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a COCO-pretrained YOLO11n model
|
||||
model = YOLO("yolo11n.pt")
|
||||
# Load a COCO-pretrained YOLO26n model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on the Open Images V7 dataset
|
||||
results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -195,8 +195,8 @@ To train a YOLO11 model on the Open Images V7 dataset, you can use both Python a
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train a COCO-pretrained YOLO11n model on the Open Images V7 dataset
|
||||
yolo detect train data=open-images-v7.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
# Train a COCO-pretrained YOLO26n model on the Open Images V7 dataset
|
||||
yolo detect train data=open-images-v7.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For more details on arguments and settings, refer to the [Training](../../modes/train.md) page.
|
||||
|
|
@ -218,11 +218,11 @@ Ultralytics provides several YOLOv8 pretrained models for the Open Images V7 dat
|
|||
|
||||
| Model | size<br><sup>(pixels)</sup> | mAP<sup>val<br>50-95</sup> | Speed<br><sup>CPU ONNX<br>(ms)</sup> | Speed<br><sup>A100 TensorRT<br>(ms)</sup> | params<br><sup>(M)</sup> | FLOPs<br><sup>(B)</sup> |
|
||||
| ----------------------------------------------------------------------------------------- | --------------------------- | -------------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ----------------------- |
|
||||
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
|
||||
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
|
||||
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
|
||||
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
|
||||
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
|
||||
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
|
||||
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
|
||||
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
|
||||
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
|
||||
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
|
||||
|
||||
### What applications can the Open Images V7 dataset be used for?
|
||||
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ keywords: Roboflow 100, Ultralytics, object detection, dataset, benchmarking, ma
|
|||
|
||||
# Roboflow 100 Dataset
|
||||
|
||||
Roboflow 100, sponsored by [Intel](https://www.intel.com/), is a groundbreaking [object detection](../../tasks/detect.md) benchmark dataset. It includes 100 diverse datasets sampled from over 90,000 public datasets available on Roboflow Universe. This benchmark is specifically designed to test the adaptability of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models, like [Ultralytics YOLO models](../../models/yolo11.md), to various domains, including healthcare, aerial imagery, and video games.
|
||||
Roboflow 100, sponsored by [Intel](https://www.intel.com/), is a groundbreaking [object detection](../../tasks/detect.md) benchmark dataset. It includes 100 diverse datasets sampled from over 90,000 public datasets available on Roboflow Universe. This benchmark is specifically designed to test the adaptability of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models, like [Ultralytics YOLO models](../../models/yolo26.md), to various domains, including healthcare, aerial imagery, and video games.
|
||||
|
||||
!!! question "Licensing"
|
||||
|
||||
|
|
@ -51,7 +51,7 @@ Dataset [benchmarking](../../modes/benchmark.md) involves evaluating the perform
|
|||
|
||||
!!! example "Benchmarking Example"
|
||||
|
||||
The following script demonstrates how to programmatically benchmark an Ultralytics YOLO model (e.g., YOLO11n) on all 100 datasets within the Roboflow 100 benchmark using the `RF100Benchmark` class.
|
||||
The following script demonstrates how to programmatically benchmark an Ultralytics YOLO model (e.g., YOLO26n) on all 100 datasets within the Roboflow 100 benchmark using the `RF100Benchmark` class.
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -77,7 +77,7 @@ Dataset [benchmarking](../../modes/benchmark.md) involves evaluating the perform
|
|||
if path.exists():
|
||||
# Fix YAML file and run training
|
||||
benchmark.fix_yaml(str(path))
|
||||
os.system(f"yolo detect train data={path} model=yolo11s.pt epochs=1 batch=16")
|
||||
os.system(f"yolo detect train data={path} model=yolo26s.pt epochs=1 batch=16")
|
||||
|
||||
# Run validation and evaluate
|
||||
os.system(f"yolo detect val data={path} model=runs/detect/train/weights/best.pt > {val_log_file} 2>&1")
|
||||
|
|
@ -103,7 +103,7 @@ Roboflow 100 is invaluable for various applications related to [computer vision]
|
|||
- Compare model performance across different [neural network](https://www.ultralytics.com/glossary/neural-network-nn) architectures and [optimization](https://www.ultralytics.com/glossary/optimization-algorithm) techniques.
|
||||
- Identify domain-specific challenges that may require specialized [model training tips](../../guides/model-training-tips.md) or [fine-tuning](https://www.ultralytics.com/glossary/fine-tuning) approaches like [transfer learning](https://www.ultralytics.com/glossary/transfer-learning).
|
||||
|
||||
For more ideas and inspiration on real-world applications, explore [our guides on practical projects](../../guides/index.md) or check out [Ultralytics HUB](https://www.ultralytics.com/hub) for streamlined [model training](../../modes/train.md) and [deployment](../../guides/model-deployment-options.md).
|
||||
For more ideas and inspiration on real-world applications, explore [our guides on practical projects](../../guides/index.md) or check out [Ultralytics Platform](https://platform.ultralytics.com) for streamlined [model training](../../modes/train.md) and [deployment](../../guides/model-deployment-options.md).
|
||||
|
||||
## Usage
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Discover the Signature Detection Dataset for training models to identify and verify human signatures in various documents. Perfect for document verification and fraud prevention.
|
||||
keywords: Signature Detection Dataset, document verification, fraud detection, computer vision, YOLO11, Ultralytics, annotated signatures, training dataset
|
||||
keywords: Signature Detection Dataset, document verification, fraud detection, computer vision, YOLO26, Ultralytics, annotated signatures, training dataset
|
||||
---
|
||||
|
||||
# Signature Detection Dataset
|
||||
|
|
@ -39,7 +39,7 @@ A YAML (Yet Another Markup Language) file defines the dataset configuration, inc
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the signature detection dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n model on the signature detection dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -49,7 +49,7 @@ To train a YOLO11n model on the signature detection dataset for 100 [epochs](htt
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="signature.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -59,7 +59,7 @@ To train a YOLO11n model on the signature detection dataset for 100 [epochs](htt
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=signature.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=signature.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
!!! example "Inference Example"
|
||||
|
|
@ -101,11 +101,11 @@ The dataset has been released available under the [AGPL-3.0 License](https://git
|
|||
|
||||
### What is the Signature Detection Dataset, and how can it be used?
|
||||
|
||||
The Signature Detection Dataset is a collection of annotated images aimed at detecting human signatures within various document types. It can be applied in computer vision tasks such as [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking, primarily for document verification, fraud detection, and archival research. This dataset helps train models to recognize signatures in different contexts, making it valuable for both research and practical applications in [smart document analysis](https://www.ultralytics.com/blog/using-ultralytics-yolo11-for-smart-document-analysis).
|
||||
The Signature Detection Dataset is a collection of annotated images aimed at detecting human signatures within various document types. It can be applied in computer vision tasks such as [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking, primarily for document verification, fraud detection, and archival research. This dataset helps train models to recognize signatures in different contexts, making it valuable for both research and practical applications in [smart document analysis](https://www.ultralytics.com/blog/using-ultralytics-yolo26-for-smart-document-analysis).
|
||||
|
||||
### How do I train a YOLO11n model on the Signature Detection Dataset?
|
||||
### How do I train a YOLO26n model on the Signature Detection Dataset?
|
||||
|
||||
To train a YOLO11n model on the Signature Detection Dataset, follow these steps:
|
||||
To train a YOLO26n model on the Signature Detection Dataset, follow these steps:
|
||||
|
||||
1. Download the `signature.yaml` dataset configuration file from [signature.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml).
|
||||
2. Use the following Python script or CLI command to start training:
|
||||
|
|
@ -118,7 +118,7 @@ To train a YOLO11n model on the Signature Detection Dataset, follow these steps:
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained model
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="signature.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -127,7 +127,7 @@ To train a YOLO11n model on the Signature Detection Dataset, follow these steps:
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=signature.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=signature.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For more details, refer to the [Training](../../modes/train.md) page.
|
||||
|
|
|
|||
|
|
@ -59,7 +59,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the SKU-110K dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n model on the SKU-110K dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -69,7 +69,7 @@ To train a YOLO11n model on the SKU-110K dataset for 100 [epochs](https://www.ul
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="SKU-110K.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -79,7 +79,7 @@ To train a YOLO11n model on the SKU-110K dataset for 100 [epochs](https://www.ul
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=SKU-110K.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=SKU-110K.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
|
@ -117,9 +117,9 @@ We would like to acknowledge Eran Goldman et al. for creating and maintaining th
|
|||
|
||||
The SKU-110k dataset consists of densely packed retail shelf images designed to aid research in object detection tasks. Developed by Eran Goldman et al., it includes over 110,000 unique SKU categories. Its importance lies in its ability to challenge state-of-the-art object detectors with diverse object appearances and proximity, making it an invaluable resource for researchers and practitioners in computer vision. Learn more about the dataset's structure and applications in our [SKU-110k Dataset](#sku-110k-dataset) section.
|
||||
|
||||
### How do I train a YOLO11 model using the SKU-110k dataset?
|
||||
### How do I train a YOLO26 model using the SKU-110k dataset?
|
||||
|
||||
Training a YOLO11 model on the SKU-110k dataset is straightforward. Here's an example to train a YOLO11n model for 100 epochs with an image size of 640:
|
||||
Training a YOLO26 model on the SKU-110k dataset is straightforward. Here's an example to train a YOLO26n model for 100 epochs with an image size of 640:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -129,7 +129,7 @@ Training a YOLO11 model on the SKU-110k dataset is straightforward. Here's an ex
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="SKU-110K.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -140,7 +140,7 @@ Training a YOLO11 model on the SKU-110k dataset is straightforward. Here's an ex
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=SKU-110K.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=SKU-110K.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore the Tsinghua-Tencent 100K (TT100K) traffic sign dataset with 100,000 street view images and 30,000+ annotated traffic signs for robust detection and classification.
|
||||
keywords: TT100K, Tsinghua-Tencent 100K, traffic sign detection, YOLO11, dataset, object detection, street view, traffic signs, Chinese traffic signs
|
||||
keywords: TT100K, Tsinghua-Tencent 100K, traffic sign detection, YOLO26, dataset, object detection, street view, traffic signs, Chinese traffic signs
|
||||
---
|
||||
|
||||
# TT100K Dataset
|
||||
|
|
@ -82,7 +82,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11 model on the TT100K dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. The dataset will be automatically downloaded and converted to YOLO format on first use.
|
||||
To train a YOLO26 model on the TT100K dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. The dataset will be automatically downloaded and converted to YOLO format on first use.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -92,7 +92,7 @@ To train a YOLO11 model on the TT100K dataset for 100 [epochs](https://www.ultra
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model - dataset will auto-download on first run
|
||||
results = model.train(data="TT100K.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -103,7 +103,7 @@ To train a YOLO11 model on the TT100K dataset for 100 [epochs](https://www.ultra
|
|||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
# Dataset will auto-download and convert on first run
|
||||
yolo detect train data=TT100K.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=TT100K.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -168,9 +168,9 @@ The TT100K dataset contains **221 different traffic sign categories**, including
|
|||
|
||||
This comprehensive coverage includes most traffic signs found in Chinese road networks.
|
||||
|
||||
### How can I train a YOLO11n model using the TT100K dataset?
|
||||
### How can I train a YOLO26n model using the TT100K dataset?
|
||||
|
||||
To train a YOLO11n model on the TT100K dataset for 100 epochs with an image size of 640, use the example below.
|
||||
To train a YOLO26n model on the TT100K dataset for 100 epochs with an image size of 640, use the example below.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -180,7 +180,7 @@ To train a YOLO11n model on the TT100K dataset for 100 epochs with an image size
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="TT100K.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -191,7 +191,7 @@ To train a YOLO11n model on the TT100K dataset for 100 epochs with an image size
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=TT100K.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=TT100K.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For detailed training configurations, refer to the [Training](../../modes/train.md) documentation.
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ The [VisDrone Dataset](https://github.com/VisDrone/VisDrone-Dataset) is a large-
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO11 on the VisDrone Dataset | Aerial Detection | Complete Tutorial 🚀
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on the VisDrone Dataset | Aerial Detection | Complete Tutorial 🚀
|
||||
</p>
|
||||
|
||||
VisDrone is composed of 288 video clips with 261,908 frames and 10,209 static images, captured by various drone-mounted cameras. The dataset covers a wide range of aspects, including location (14 different cities across China), environment (urban and rural), objects (pedestrians, vehicles, bicycles, etc.), and density (sparse and crowded scenes). The dataset was collected using various drone platforms under different scenarios and weather and lighting conditions. These frames are manually annotated with over 2.6 million bounding boxes of targets such as pedestrians, cars, bicycles, and tricycles. Attributes like scene visibility, object class, and occlusion are also provided for better data utilization.
|
||||
|
|
@ -47,7 +47,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the VisDrone dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n model on the VisDrone dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -57,7 +57,7 @@ To train a YOLO11n model on the VisDrone dataset for 100 [epochs](https://www.ul
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="VisDrone.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -67,7 +67,7 @@ To train a YOLO11n model on the VisDrone dataset for 100 [epochs](https://www.ul
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=VisDrone.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=VisDrone.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
|
@ -113,9 +113,9 @@ The [VisDrone Dataset](https://github.com/VisDrone/VisDrone-Dataset) is a large-
|
|||
- **Diversity**: Collected across 14 cities, in urban and rural settings, under different weather and lighting conditions.
|
||||
- **Tasks**: Split into five main tasks—object detection in images and videos, single-object and multi-object tracking, and crowd counting.
|
||||
|
||||
### How can I use the VisDrone Dataset to train a YOLO11 model with Ultralytics?
|
||||
### How can I use the VisDrone Dataset to train a YOLO26 model with Ultralytics?
|
||||
|
||||
To train a YOLO11 model on the VisDrone dataset for 100 epochs with an image size of 640, you can follow these steps:
|
||||
To train a YOLO26 model on the VisDrone dataset for 100 epochs with an image size of 640, you can follow these steps:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -125,7 +125,7 @@ To train a YOLO11 model on the VisDrone dataset for 100 epochs with an image siz
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained model
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="VisDrone.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -135,7 +135,7 @@ To train a YOLO11 model on the VisDrone dataset for 100 epochs with an image siz
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=VisDrone.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=VisDrone.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For additional configuration options, please refer to the model [Training](../../modes/train.md) page.
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ The [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) (Visual Object Classes
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO11 on the Pascal VOC Dataset | Object Detection 🚀
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on the Pascal VOC Dataset | Object Detection 🚀
|
||||
</p>
|
||||
|
||||
## Key Features
|
||||
|
|
@ -36,7 +36,7 @@ The VOC dataset is split into three subsets:
|
|||
|
||||
## Applications
|
||||
|
||||
The VOC dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object detection (such as [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/), [Faster R-CNN](https://arxiv.org/abs/1506.01497), and [SSD](https://arxiv.org/abs/1512.02325)), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) (such as [Mask R-CNN](https://arxiv.org/abs/1703.06870)), and [image classification](https://www.ultralytics.com/glossary/image-classification). The dataset's diverse set of object categories, large number of annotated images, and standardized evaluation metrics make it an essential resource for [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) researchers and practitioners.
|
||||
The VOC dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object detection (such as [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo26/), [Faster R-CNN](https://arxiv.org/abs/1506.01497), and [SSD](https://arxiv.org/abs/1512.02325)), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) (such as [Mask R-CNN](https://arxiv.org/abs/1703.06870)), and [image classification](https://www.ultralytics.com/glossary/image-classification). The dataset's diverse set of object categories, large number of annotated images, and standardized evaluation metrics make it an essential resource for [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) researchers and practitioners.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
|
|
@ -50,7 +50,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n model on the VOC dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n model on the VOC dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -60,7 +60,7 @@ To train a YOLO11n model on the VOC dataset for 100 [epochs](https://www.ultraly
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="VOC.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -70,7 +70,7 @@ To train a YOLO11n model on the VOC dataset for 100 [epochs](https://www.ultraly
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=VOC.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=VOC.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -110,9 +110,9 @@ We would like to acknowledge the PASCAL VOC Consortium for creating and maintain
|
|||
|
||||
The [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) (Visual Object Classes) dataset is a renowned benchmark for [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification in computer vision. It includes comprehensive annotations like bounding boxes, class labels, and segmentation masks across 20 different object categories. Researchers use it widely to evaluate the performance of models like Faster R-CNN, YOLO, and Mask R-CNN due to its standardized evaluation metrics such as mean Average Precision (mAP).
|
||||
|
||||
### How do I train a YOLO11 model using the VOC dataset?
|
||||
### How do I train a YOLO26 model using the VOC dataset?
|
||||
|
||||
To train a YOLO11 model with the VOC dataset, you need the dataset configuration in a YAML file. Here's an example to start training a YOLO11n model for 100 epochs with an image size of 640:
|
||||
To train a YOLO26 model with the VOC dataset, you need the dataset configuration in a YAML file. Here's an example to start training a YOLO26n model for 100 epochs with an image size of 640:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -122,7 +122,7 @@ To train a YOLO11 model with the VOC dataset, you need the dataset configuration
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="VOC.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -132,7 +132,7 @@ To train a YOLO11 model with the VOC dataset, you need the dataset configuration
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=VOC.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=VOC.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### What are the primary challenges included in the VOC dataset?
|
||||
|
|
@ -145,4 +145,4 @@ The PASCAL VOC dataset enhances model benchmarking and evaluation through its de
|
|||
|
||||
### How do I use the VOC dataset for [semantic segmentation](https://www.ultralytics.com/glossary/semantic-segmentation) in YOLO models?
|
||||
|
||||
To use the VOC dataset for semantic segmentation tasks with YOLO models, you need to configure the dataset properly in a YAML file. The YAML file defines paths and classes needed for training segmentation models. Check the VOC dataset YAML configuration file at [VOC.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VOC.yaml) for detailed setups. For segmentation tasks, you would use a segmentation-specific model like `yolo11n-seg.pt` instead of the detection model.
|
||||
To use the VOC dataset for semantic segmentation tasks with YOLO models, you need to configure the dataset properly in a YAML file. The YAML file defines paths and classes needed for training segmentation models. Check the VOC dataset YAML configuration file at [VOC.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VOC.yaml) for detailed setups. For segmentation tasks, you would use a segmentation-specific model like `yolo26n-seg.pt` instead of the detection model.
|
||||
|
|
|
|||
|
|
@ -67,7 +67,7 @@ To train a model on the xView dataset for 100 [epochs](https://www.ultralytics.c
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="xView.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -77,7 +77,7 @@ To train a model on the xView dataset for 100 [epochs](https://www.ultralytics.c
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=xView.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=xView.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
|
@ -127,7 +127,7 @@ The [xView](http://xviewdataset.org/) dataset is one of the largest publicly ava
|
|||
|
||||
### How can I use Ultralytics YOLO to train a model on the xView dataset?
|
||||
|
||||
To train a model on the xView dataset using [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/), follow these steps:
|
||||
To train a model on the xView dataset using [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo26/), follow these steps:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -137,7 +137,7 @@ To train a model on the xView dataset using [Ultralytics YOLO](https://docs.ultr
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="xView.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -148,7 +148,7 @@ To train a model on the xView dataset using [Ultralytics YOLO](https://docs.ultr
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=xView.yaml model=yolo11n.pt epochs=100 imgsz=640
|
||||
yolo detect train data=xView.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For detailed arguments and settings, refer to the model [Training](../../modes/train.md) page.
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ keywords: Ultralytics, Explorer API, dataset exploration, SQL queries, similarit
|
|||
|
||||
!!! warning "Community Note ⚠️"
|
||||
|
||||
As of **`ultralytics>=8.3.10`**, Ultralytics Explorer support is deprecated. Similar (and expanded) dataset exploration features are available in [Ultralytics HUB](https://hub.ultralytics.com/).
|
||||
As of **`ultralytics>=8.3.10`**, Ultralytics Explorer support is deprecated. Similar (and expanded) dataset exploration features are available in [Ultralytics Platform](https://platform.ultralytics.com/).
|
||||
|
||||
## Introduction
|
||||
|
||||
|
|
@ -40,7 +40,7 @@ pip install ultralytics[explorer]
|
|||
from ultralytics import Explorer
|
||||
|
||||
# Create an Explorer object
|
||||
explorer = Explorer(data="coco128.yaml", model="yolo11n.pt")
|
||||
explorer = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
|
||||
# Create embeddings for your dataset
|
||||
explorer.create_embeddings_table()
|
||||
|
|
@ -79,7 +79,7 @@ You get a pandas DataFrame with the `limit` number of most similar data points t
|
|||
from ultralytics import Explorer
|
||||
|
||||
# create an Explorer object
|
||||
exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
|
||||
exp = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
|
||||
similar = exp.get_similar(img="https://ultralytics.com/images/bus.jpg", limit=10)
|
||||
|
|
@ -99,7 +99,7 @@ You get a pandas DataFrame with the `limit` number of most similar data points t
|
|||
from ultralytics import Explorer
|
||||
|
||||
# create an Explorer object
|
||||
exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
|
||||
exp = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
|
||||
similar = exp.get_similar(idx=1, limit=10)
|
||||
|
|
@ -122,7 +122,7 @@ You can also plot the similar images using the `plot_similar` method. This metho
|
|||
from ultralytics import Explorer
|
||||
|
||||
# create an Explorer object
|
||||
exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
|
||||
exp = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
|
||||
plt = exp.plot_similar(img="https://ultralytics.com/images/bus.jpg", limit=10)
|
||||
|
|
@ -135,7 +135,7 @@ You can also plot the similar images using the `plot_similar` method. This metho
|
|||
from ultralytics import Explorer
|
||||
|
||||
# create an Explorer object
|
||||
exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
|
||||
exp = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
|
||||
plt = exp.plot_similar(idx=1, limit=10)
|
||||
|
|
@ -155,7 +155,7 @@ Note: This feature uses LLMs, so results are probabilistic and may be inaccurate
|
|||
from ultralytics import Explorer
|
||||
|
||||
# create an Explorer object
|
||||
exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
|
||||
exp = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
|
||||
df = exp.ask_ai("show me 100 images with exactly one person and 2 dogs. There can be other objects too")
|
||||
|
|
@ -176,7 +176,7 @@ You can run SQL queries on your dataset using the `sql_query` method. This metho
|
|||
from ultralytics import Explorer
|
||||
|
||||
# create an Explorer object
|
||||
exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
|
||||
exp = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
|
||||
df = exp.sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%dog%'")
|
||||
|
|
@ -193,7 +193,7 @@ You can also plot the results of a SQL query using the `plot_sql_query` method.
|
|||
from ultralytics import Explorer
|
||||
|
||||
# create an Explorer object
|
||||
exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
|
||||
exp = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
|
||||
# plot the SQL Query
|
||||
|
|
@ -240,7 +240,7 @@ Here are some examples of what you can do with the table:
|
|||
```python
|
||||
from ultralytics import Explorer
|
||||
|
||||
exp = Explorer(model="yolo11n.pt")
|
||||
exp = Explorer(model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
table = exp.table
|
||||
|
||||
|
|
@ -362,7 +362,7 @@ You can use the Ultralytics Explorer API to perform similarity searches by creat
|
|||
from ultralytics import Explorer
|
||||
|
||||
# Create an Explorer object
|
||||
explorer = Explorer(data="coco128.yaml", model="yolo11n.pt")
|
||||
explorer = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
explorer.create_embeddings_table()
|
||||
|
||||
# Search for similar images to a given image
|
||||
|
|
@ -384,7 +384,7 @@ The Ask AI feature allows users to filter datasets using natural language querie
|
|||
from ultralytics import Explorer
|
||||
|
||||
# Create an Explorer object
|
||||
explorer = Explorer(data="coco128.yaml", model="yolo11n.pt")
|
||||
explorer = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
explorer.create_embeddings_table()
|
||||
|
||||
# Query with natural language
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ keywords: Ultralytics Explorer GUI, semantic search, vector similarity, SQL quer
|
|||
|
||||
!!! warning "Community Note ⚠️"
|
||||
|
||||
As of **`ultralytics>=8.3.10`**, Ultralytics Explorer support is deprecated. Similar (and expanded) dataset exploration features are available in [Ultralytics HUB](https://hub.ultralytics.com/).
|
||||
As of **`ultralytics>=8.3.10`**, Ultralytics Explorer support is deprecated. Similar (and expanded) dataset exploration features are available in [Ultralytics Platform](https://platform.ultralytics.com/).
|
||||
|
||||
Explorer GUI is built on the [Ultralytics Explorer API](api.md). It allows you to run semantic/vector similarity search, SQL queries, and natural language queries using the Ask AI feature powered by LLMs.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Dive into advanced data exploration with Ultralytics Explorer. Perform semantic searches, execute SQL queries, and leverage AI-powered natural language insights for seamless data analysis.
|
||||
keywords: Ultralytics Explorer, data exploration, semantic search, vector similarity, SQL queries, AI, natural language queries, machine learning, OpenAI, LLMs, Ultralytics HUB
|
||||
keywords: Ultralytics Explorer, data exploration, semantic search, vector similarity, SQL queries, AI, natural language queries, machine learning, OpenAI, LLMs, Ultralytics Platform
|
||||
---
|
||||
|
||||
# VOC Exploration Example
|
||||
|
|
@ -32,7 +32,7 @@ keywords: Ultralytics Explorer, data exploration, semantic search, vector simila
|
|||
<br>
|
||||
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run Ultralytics on Gradient"></a>
|
||||
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Ultralytics In Colab"></a>
|
||||
<a href="https://www.kaggle.com/models/ultralytics/yolo11"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open Ultralytics In Kaggle"></a>
|
||||
<a href="https://www.kaggle.com/models/ultralytics/yolo26"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open Ultralytics In Kaggle"></a>
|
||||
<a href="https://mybinder.org/v2/gh/ultralytics/ultralytics/HEAD?labpath=examples%2Ftutorial.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Open Ultralytics In Binder"></a>
|
||||
<br>
|
||||
</div>
|
||||
|
|
@ -45,7 +45,7 @@ Install `ultralytics` and run `yolo explorer` in your terminal to run custom que
|
|||
|
||||
!!! warning "Community Note ⚠️"
|
||||
|
||||
As of **`ultralytics>=8.3.10`**, Ultralytics Explorer support is deprecated. Similar (and expanded) dataset exploration features are available in [Ultralytics HUB](https://hub.ultralytics.com/).
|
||||
As of **`ultralytics>=8.3.10`**, Ultralytics Explorer support is deprecated. Similar (and expanded) dataset exploration features are available in [Ultralytics Platform](https://platform.ultralytics.com/).
|
||||
|
||||
## Setup
|
||||
|
||||
|
|
@ -61,7 +61,7 @@ yolo checks
|
|||
Utilize the power of vector similarity search to find the similar data points in your dataset along with their distance in the embedding space. Simply create an embeddings table for the given dataset-model pair. It is only needed once, and it is reused automatically.
|
||||
|
||||
```python
|
||||
exp = Explorer("VOC.yaml", model="yolo11n.pt")
|
||||
exp = Explorer("VOC.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ keywords: Ultralytics Explorer, CV datasets, semantic search, SQL queries, vecto
|
|||
|
||||
!!! warning "Community Note ⚠️"
|
||||
|
||||
As of **`ultralytics>=8.3.10`**, Ultralytics Explorer support is deprecated. Similar (and expanded) dataset exploration features are available in [Ultralytics HUB](https://hub.ultralytics.com/).
|
||||
As of **`ultralytics>=8.3.10`**, Ultralytics Explorer support is deprecated. Similar (and expanded) dataset exploration features are available in [Ultralytics Platform](https://platform.ultralytics.com/).
|
||||
|
||||
<p>
|
||||
<img width="1709" alt="Ultralytics Explorer Screenshot 1" src="https://github.com/ultralytics/docs/releases/download/0/explorer-dashboard-screenshot-1.avif">
|
||||
|
|
|
|||
|
|
@ -188,16 +188,16 @@ Contributing a new dataset involves several steps:
|
|||
|
||||
Visit [Contribute New Datasets](#contribute-new-datasets) for a comprehensive guide.
|
||||
|
||||
### Why should I use Ultralytics HUB for my dataset?
|
||||
### Why should I use Ultralytics Platform for my dataset?
|
||||
|
||||
[Ultralytics HUB](https://hub.ultralytics.com/) offers powerful features for dataset management and analysis, including:
|
||||
[Ultralytics Platform](https://platform.ultralytics.com/) offers powerful features for dataset management and analysis, including:
|
||||
|
||||
- **Seamless Dataset Management**: Upload, organize, and manage your datasets in one place.
|
||||
- **Immediate Training Integration**: Use uploaded datasets directly for model training without additional setup.
|
||||
- **Visualization Tools**: Explore and visualize your dataset images and annotations.
|
||||
- **Dataset Analysis**: Get insights into your dataset distribution and characteristics.
|
||||
|
||||
The platform streamlines the transition from dataset management to model training, making the entire process more efficient. Learn more about [Ultralytics HUB Datasets](https://docs.ultralytics.com/hub/datasets/).
|
||||
The platform streamlines the transition from dataset management to model training, making the entire process more efficient. Learn more about [Ultralytics Platform Datasets](https://docs.ultralytics.com/platform/datasets/).
|
||||
|
||||
### What are the unique features of Ultralytics YOLO models for computer vision?
|
||||
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ keywords: DOTA dataset, object detection, aerial images, oriented bounding boxes
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO11 on the DOTA Dataset for Oriented Bounding Boxes in Google Colab
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on the DOTA Dataset for Oriented Bounding Boxes in Google Colab
|
||||
</p>
|
||||
|
||||
- Collection from various sensors and platforms, with image sizes ranging from 800 × 800 to 20,000 × 20,000 pixels.
|
||||
|
|
@ -128,8 +128,8 @@ To train a model on the DOTA v1 dataset, you can utilize the following code snip
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Create a new YOLO11n-OBB model from scratch
|
||||
model = YOLO("yolo11n-obb.yaml")
|
||||
# Create a new YOLO26n-OBB model from scratch
|
||||
model = YOLO("yolo26n-obb.yaml")
|
||||
|
||||
# Train the model on the DOTAv1 dataset
|
||||
results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=1024)
|
||||
|
|
@ -138,8 +138,8 @@ To train a model on the DOTA v1 dataset, you can utilize the following code snip
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train a new YOLO11n-OBB model on the DOTAv1 dataset
|
||||
yolo obb train data=DOTAv1.yaml model=yolo11n-obb.pt epochs=100 imgsz=1024
|
||||
# Train a new YOLO26n-OBB model on the DOTAv1 dataset
|
||||
yolo obb train data=DOTAv1.yaml model=yolo26n-obb.pt epochs=100 imgsz=1024
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
|
@ -196,8 +196,8 @@ To train a model on the DOTA dataset, you can use the following example with [Ul
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Create a new YOLO11n-OBB model from scratch
|
||||
model = YOLO("yolo11n-obb.yaml")
|
||||
# Create a new YOLO26n-OBB model from scratch
|
||||
model = YOLO("yolo26n-obb.yaml")
|
||||
|
||||
# Train the model on the DOTAv1 dataset
|
||||
results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=1024)
|
||||
|
|
@ -206,8 +206,8 @@ To train a model on the DOTA dataset, you can use the following example with [Ul
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train a new YOLO11n-OBB model on the DOTAv1 dataset
|
||||
yolo obb train data=DOTAv1.yaml model=yolo11n-obb.pt epochs=100 imgsz=1024
|
||||
# Train a new YOLO26n-OBB model on the DOTAv1 dataset
|
||||
yolo obb train data=DOTAv1.yaml model=yolo26n-obb.pt epochs=100 imgsz=1024
|
||||
```
|
||||
|
||||
For more details on how to split and preprocess the DOTA images, refer to the [split DOTA images section](#split-dota-images).
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore the DOTA8 dataset - a small, versatile oriented object detection dataset ideal for testing and debugging object detection models using Ultralytics YOLO11.
|
||||
keywords: DOTA8 dataset, Ultralytics, YOLO11, object detection, debugging, training models, oriented object detection, dataset YAML
|
||||
description: Explore the DOTA8 dataset - a small, versatile oriented object detection dataset ideal for testing and debugging object detection models using Ultralytics YOLO26.
|
||||
keywords: DOTA8 dataset, Ultralytics, YOLO26, object detection, debugging, training models, oriented object detection, dataset YAML
|
||||
---
|
||||
|
||||
# DOTA8 Dataset
|
||||
|
|
@ -27,7 +27,7 @@ keywords: DOTA8 dataset, Ultralytics, YOLO11, object detection, debugging, train
|
|||
└── val/
|
||||
```
|
||||
|
||||
This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
|
||||
This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
|
|
@ -41,7 +41,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n-obb model on the DOTA8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n-obb model on the DOTA8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -51,7 +51,7 @@ To train a YOLO11n-obb model on the DOTA8 dataset for 100 [epochs](https://www.u
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-obb.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-obb.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="dota8.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -61,7 +61,7 @@ To train a YOLO11n-obb model on the DOTA8 dataset for 100 [epochs](https://www.u
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo obb train data=dota8.yaml model=yolo11n-obb.pt epochs=100 imgsz=640
|
||||
yolo obb train data=dota8.yaml model=yolo26n-obb.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -101,11 +101,11 @@ A special note of gratitude to the team behind the DOTA datasets for their comme
|
|||
|
||||
### What is the DOTA8 dataset and how can it be used?
|
||||
|
||||
The DOTA8 dataset is a small, versatile oriented object detection dataset made up of the first 8 images from the DOTAv1 split set, with 4 images designated for training and 4 for validation. It's ideal for testing and debugging object detection models like Ultralytics YOLO11. Due to its manageable size and diversity, it helps in identifying pipeline errors and running sanity checks before deploying larger datasets. Learn more about object detection with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics).
|
||||
The DOTA8 dataset is a small, versatile oriented object detection dataset made up of the first 8 images from the DOTAv1 split set, with 4 images designated for training and 4 for validation. It's ideal for testing and debugging object detection models like Ultralytics YOLO26. Due to its manageable size and diversity, it helps in identifying pipeline errors and running sanity checks before deploying larger datasets. Learn more about object detection with [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
### How do I train a YOLO11 model using the DOTA8 dataset?
|
||||
### How do I train a YOLO26 model using the DOTA8 dataset?
|
||||
|
||||
To train a YOLO11n-obb model on the DOTA8 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For comprehensive argument options, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n-obb model on the DOTA8 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For comprehensive argument options, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -115,7 +115,7 @@ To train a YOLO11n-obb model on the DOTA8 dataset for 100 epochs with an image s
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-obb.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-obb.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="dota8.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -125,7 +125,7 @@ To train a YOLO11n-obb model on the DOTA8 dataset for 100 epochs with an image s
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo obb train data=dota8.yaml model=yolo11n-obb.pt epochs=100 imgsz=640
|
||||
yolo obb train data=dota8.yaml model=yolo26n-obb.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### What are the key features of the DOTA dataset and where can I access the YAML file?
|
||||
|
|
@ -136,6 +136,6 @@ The DOTA dataset is known for its large-scale benchmark and the challenges it pr
|
|||
|
||||
Mosaicing combines multiple images into one during training, increasing the variety of objects and contexts within each batch. This improves a model's ability to generalize to different object sizes, aspect ratios, and scenes. This technique can be visually demonstrated through a training batch composed of mosaiced DOTA8 dataset images, helping in robust model development. Explore more about mosaicing and training techniques on our [Training](../../modes/train.md) page.
|
||||
|
||||
### Why should I use Ultralytics YOLO11 for object detection tasks?
|
||||
### Why should I use Ultralytics YOLO26 for object detection tasks?
|
||||
|
||||
Ultralytics YOLO11 provides state-of-the-art real-time object detection capabilities, including features like oriented bounding boxes (OBB), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), and a highly versatile training pipeline. It's suitable for various applications and offers pretrained models for efficient fine-tuning. Explore further about the advantages and usage in the [Ultralytics YOLO11 documentation](https://github.com/ultralytics/ultralytics).
|
||||
Ultralytics YOLO26 provides state-of-the-art real-time object detection capabilities, including features like oriented bounding boxes (OBB), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), and a highly versatile training pipeline. It's suitable for various applications and offers pretrained models for efficient fine-tuning. Explore further about the advantages and usage in the [Ultralytics YOLO26 documentation](https://github.com/ultralytics/ultralytics).
|
||||
|
|
|
|||
|
|
@ -49,8 +49,8 @@ To train a model using these OBB formats:
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Create a new YOLO11n-OBB model from scratch
|
||||
model = YOLO("yolo11n-obb.yaml")
|
||||
# Create a new YOLO26n-OBB model from scratch
|
||||
model = YOLO("yolo26n-obb.yaml")
|
||||
|
||||
# Train the model on the DOTAv1 dataset
|
||||
results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=1024)
|
||||
|
|
@ -59,8 +59,8 @@ To train a model using these OBB formats:
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train a new YOLO11n-OBB model on the DOTAv1 dataset
|
||||
yolo obb train data=DOTAv1.yaml model=yolo11n-obb.pt epochs=100 imgsz=1024
|
||||
# Train a new YOLO26n-OBB model on the DOTAv1 dataset
|
||||
yolo obb train data=DOTAv1.yaml model=yolo26n-obb.pt epochs=100 imgsz=1024
|
||||
```
|
||||
|
||||
## Supported Datasets
|
||||
|
|
@ -92,7 +92,7 @@ Transitioning labels from the DOTA dataset format to the YOLO OBB format can be
|
|||
convert_dota_to_yolo_obb("path/to/DOTA")
|
||||
```
|
||||
|
||||
This conversion mechanism is instrumental for datasets in the DOTA format, ensuring alignment with the [Ultralytics YOLO](../../models/yolo11.md) OBB format.
|
||||
This conversion mechanism is instrumental for datasets in the DOTA format, ensuring alignment with the [Ultralytics YOLO](../../models/yolo26.md) OBB format.
|
||||
|
||||
It's imperative to validate the compatibility of the dataset with your model and adhere to the necessary format conventions. Properly structured datasets are pivotal for training efficient object detection models with oriented bounding boxes.
|
||||
|
||||
|
|
@ -102,7 +102,7 @@ It's imperative to validate the compatibility of the dataset with your model and
|
|||
|
||||
Oriented Bounding Boxes (OBB) are a type of bounding box annotation where the box can be rotated to align more closely with the object being detected, rather than just being axis-aligned. This is particularly useful in aerial or satellite imagery where objects might not be aligned with the image axes. In [Ultralytics YOLO](../../tasks/obb.md) models, OBBs are represented by their four corner points in the YOLO OBB format. This allows for more accurate object detection since the bounding boxes can rotate to fit the objects better.
|
||||
|
||||
### How do I convert my existing DOTA dataset labels to YOLO OBB format for use with Ultralytics YOLO11?
|
||||
### How do I convert my existing DOTA dataset labels to YOLO OBB format for use with Ultralytics YOLO26?
|
||||
|
||||
You can convert DOTA dataset labels to YOLO OBB format using the [`convert_dota_to_yolo_obb`](../../reference/data/converter.md) function from Ultralytics. This conversion ensures compatibility with the Ultralytics YOLO models, enabling you to leverage the OBB capabilities for enhanced object detection. Here's a quick example:
|
||||
|
||||
|
|
@ -114,9 +114,9 @@ convert_dota_to_yolo_obb("path/to/DOTA")
|
|||
|
||||
This script will reformat your DOTA annotations into a YOLO-compatible format.
|
||||
|
||||
### How do I train a YOLO11 model with oriented bounding boxes (OBB) on my dataset?
|
||||
### How do I train a YOLO26 model with oriented bounding boxes (OBB) on my dataset?
|
||||
|
||||
Training a YOLO11 model with OBBs involves ensuring your dataset is in the YOLO OBB format and then using the [Ultralytics API](../../usage/python.md) to train the model. Here's an example in both Python and CLI:
|
||||
Training a YOLO26 model with OBBs involves ensuring your dataset is in the YOLO OBB format and then using the [Ultralytics API](../../usage/python.md) to train the model. Here's an example in both Python and CLI:
|
||||
|
||||
!!! example
|
||||
|
||||
|
|
@ -125,8 +125,8 @@ Training a YOLO11 model with OBBs involves ensuring your dataset is in the YOLO
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Create a new YOLO11n-OBB model from scratch
|
||||
model = YOLO("yolo11n-obb.yaml")
|
||||
# Create a new YOLO26n-OBB model from scratch
|
||||
model = YOLO("yolo26n-obb.yaml")
|
||||
|
||||
# Train the model on the custom dataset
|
||||
results = model.train(data="your_dataset.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -135,8 +135,8 @@ Training a YOLO11 model with OBBs involves ensuring your dataset is in the YOLO
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train a new YOLO11n-OBB model on the custom dataset
|
||||
yolo obb train data=your_dataset.yaml model=yolo11n-obb.yaml epochs=100 imgsz=640
|
||||
# Train a new YOLO26n-OBB model on the custom dataset
|
||||
yolo obb train data=your_dataset.yaml model=yolo26n-obb.yaml epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
This ensures your model leverages the detailed OBB annotations for improved detection [accuracy](https://www.ultralytics.com/glossary/accuracy).
|
||||
|
|
@ -152,6 +152,6 @@ Currently, Ultralytics supports the following datasets for OBB training:
|
|||
|
||||
These datasets are tailored for scenarios where OBBs offer a significant advantage, such as aerial and satellite image analysis.
|
||||
|
||||
### Can I use my own dataset with oriented bounding boxes for YOLO11 training, and if so, how?
|
||||
### Can I use my own dataset with oriented bounding boxes for YOLO26 training, and if so, how?
|
||||
|
||||
Yes, you can use your own dataset with oriented bounding boxes for YOLO11 training. Ensure your dataset annotations are converted to the YOLO OBB format, which involves defining bounding boxes by their four corner points. You can then create a [YAML configuration file](../../usage/cfg.md) specifying the dataset paths, classes, and other necessary details. For more information on creating and configuring your datasets, refer to the [Supported Datasets](#supported-datasets) section.
|
||||
Yes, you can use your own dataset with oriented bounding boxes for YOLO26 training. Ensure your dataset annotations are converted to the YOLO OBB format, which involves defining bounding boxes by their four corner points. You can then create a [YAML configuration file](../../usage/cfg.md) specifying the dataset paths, classes, and other necessary details. For more information on creating and configuring your datasets, refer to the [Supported Datasets](#supported-datasets) section.
|
||||
|
|
|
|||
|
|
@ -44,7 +44,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n-pose model on the COCO-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n-pose model on the COCO-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -54,7 +54,7 @@ To train a YOLO11n-pose model on the COCO-Pose dataset for 100 [epochs](https://
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -64,7 +64,7 @@ To train a YOLO11n-pose model on the COCO-Pose dataset for 100 [epochs](https://
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=coco-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
|
||||
yolo pose train data=coco-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -102,11 +102,11 @@ We would like to acknowledge the COCO Consortium for creating and maintaining th
|
|||
|
||||
### What is the COCO-Pose dataset and how is it used with Ultralytics YOLO for pose estimation?
|
||||
|
||||
The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset designed for pose estimation tasks. It builds upon the COCO Keypoints 2017 images and annotations, allowing for the training of models like Ultralytics YOLO for detailed pose estimation. For instance, you can use the COCO-Pose dataset to train a YOLO11n-pose model by loading a pretrained model and training it with a YAML configuration. For training examples, refer to the [Training](../../modes/train.md) documentation.
|
||||
The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset designed for pose estimation tasks. It builds upon the COCO Keypoints 2017 images and annotations, allowing for the training of models like Ultralytics YOLO for detailed pose estimation. For instance, you can use the COCO-Pose dataset to train a YOLO26n-pose model by loading a pretrained model and training it with a YAML configuration. For training examples, refer to the [Training](../../modes/train.md) documentation.
|
||||
|
||||
### How can I train a YOLO11 model on the COCO-Pose dataset?
|
||||
### How can I train a YOLO26 model on the COCO-Pose dataset?
|
||||
|
||||
Training a YOLO11 model on the COCO-Pose dataset can be accomplished using either Python or CLI commands. For example, to train a YOLO11n-pose model for 100 epochs with an image size of 640, you can follow the steps below:
|
||||
Training a YOLO26 model on the COCO-Pose dataset can be accomplished using either Python or CLI commands. For example, to train a YOLO26n-pose model for 100 epochs with an image size of 640, you can follow the steps below:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -116,7 +116,7 @@ Training a YOLO11 model on the COCO-Pose dataset can be accomplished using eithe
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -126,14 +126,14 @@ Training a YOLO11 model on the COCO-Pose dataset can be accomplished using eithe
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=coco-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
|
||||
yolo pose train data=coco-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For more details on the training process and available arguments, check the [training page](../../modes/train.md).
|
||||
|
||||
### What are the different metrics provided by the COCO-Pose dataset for evaluating model performance?
|
||||
|
||||
The COCO-Pose dataset provides several standardized evaluation metrics for pose estimation tasks, similar to the original COCO dataset. Key metrics include the Object Keypoint Similarity (OKS), which evaluates the [accuracy](https://www.ultralytics.com/glossary/accuracy) of predicted keypoints against ground truth annotations. These metrics allow for thorough performance comparisons between different models. For instance, the COCO-Pose pretrained models such as YOLO11n-pose, YOLO11s-pose, and others have specific performance metrics listed in the documentation, like mAP<sup>pose</sup>50-95 and mAP<sup>pose</sup>50.
|
||||
The COCO-Pose dataset provides several standardized evaluation metrics for pose estimation tasks, similar to the original COCO dataset. Key metrics include the Object Keypoint Similarity (OKS), which evaluates the [accuracy](https://www.ultralytics.com/glossary/accuracy) of predicted keypoints against ground truth annotations. These metrics allow for thorough performance comparisons between different models. For instance, the COCO-Pose pretrained models such as YOLO26n-pose, YOLO26s-pose, and others have specific performance metrics listed in the documentation, like mAP<sup>pose</sup>50-95 and mAP<sup>pose</sup>50.
|
||||
|
||||
### How is the dataset structured and split for the COCO-Pose dataset?
|
||||
|
||||
|
|
@ -147,6 +147,6 @@ These subsets help organize the training, validation, and testing phases effecti
|
|||
|
||||
### What are the key features and applications of the COCO-Pose dataset?
|
||||
|
||||
The COCO-Pose dataset extends the COCO Keypoints 2017 annotations to include 17 keypoints for human figures, enabling detailed pose estimation. Standardized evaluation metrics (e.g., OKS) facilitate comparisons across different models. Applications of the COCO-Pose dataset span various domains, such as sports analytics, healthcare, and human-computer interaction, wherever detailed pose estimation of human figures is required. For practical use, leveraging pretrained models like those provided in the documentation (e.g., YOLO11n-pose) can significantly streamline the process ([Key Features](#key-features)).
|
||||
The COCO-Pose dataset extends the COCO Keypoints 2017 annotations to include 17 keypoints for human figures, enabling detailed pose estimation. Standardized evaluation metrics (e.g., OKS) facilitate comparisons across different models. Applications of the COCO-Pose dataset span various domains, such as sports analytics, healthcare, and human-computer interaction, wherever detailed pose estimation of human figures is required. For practical use, leveraging pretrained models like those provided in the documentation (e.g., YOLO26n-pose) can significantly streamline the process ([Key Features](#key-features)).
|
||||
|
||||
If you use the COCO-Pose dataset in your research or development work, please cite the paper with the following [BibTeX entry](#citations-and-acknowledgments).
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore the compact, versatile COCO8-Pose dataset for testing and debugging object detection models. Ideal for quick experiments with YOLO11.
|
||||
keywords: COCO8-Pose, Ultralytics, pose detection dataset, object detection, YOLO11, machine learning, computer vision, training data
|
||||
description: Explore the compact, versatile COCO8-Pose dataset for testing and debugging object detection models. Ideal for quick experiments with YOLO26.
|
||||
keywords: COCO8-Pose, Ultralytics, pose detection dataset, object detection, YOLO26, machine learning, computer vision, training data
|
||||
---
|
||||
|
||||
# COCO8-Pose Dataset
|
||||
|
|
@ -16,7 +16,7 @@ keywords: COCO8-Pose, Ultralytics, pose detection dataset, object detection, YOL
|
|||
- **Classes**: 1 (person) with 17 keypoints per annotation.
|
||||
- **Recommended directory layout**: `datasets/coco8-pose/images/{train,val}` and `datasets/coco8-pose/labels/{train,val}` with YOLO-format keypoints stored as `.txt` files.
|
||||
|
||||
This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
|
||||
This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
|
|
@ -30,7 +30,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n-pose model on the COCO8-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n-pose model on the COCO8-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -40,7 +40,7 @@ To train a YOLO11n-pose model on the COCO8-Pose dataset for 100 [epochs](https:/
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -50,7 +50,7 @@ To train a YOLO11n-pose model on the COCO8-Pose dataset for 100 [epochs](https:/
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=coco8-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
|
||||
yolo pose train data=coco8-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -86,13 +86,13 @@ We would like to acknowledge the COCO Consortium for creating and maintaining th
|
|||
|
||||
## FAQ
|
||||
|
||||
### What is the COCO8-Pose dataset, and how is it used with Ultralytics YOLO11?
|
||||
### What is the COCO8-Pose dataset, and how is it used with Ultralytics YOLO26?
|
||||
|
||||
The COCO8-Pose dataset is a small, versatile pose detection dataset that includes the first 8 images from the COCO train 2017 set, with 4 images for training and 4 for validation. It's designed for testing and debugging object detection models and experimenting with new detection approaches. This dataset is ideal for quick experiments with [Ultralytics YOLO11](../../models/yolo11.md). For more details on dataset configuration, check out the [dataset YAML file](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml).
|
||||
The COCO8-Pose dataset is a small, versatile pose detection dataset that includes the first 8 images from the COCO train 2017 set, with 4 images for training and 4 for validation. It's designed for testing and debugging object detection models and experimenting with new detection approaches. This dataset is ideal for quick experiments with [Ultralytics YOLO26](../../models/yolo26.md). For more details on dataset configuration, check out the [dataset YAML file](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml).
|
||||
|
||||
### How do I train a YOLO11 model using the COCO8-Pose dataset in Ultralytics?
|
||||
### How do I train a YOLO26 model using the COCO8-Pose dataset in Ultralytics?
|
||||
|
||||
To train a YOLO11n-pose model on the COCO8-Pose dataset for 100 epochs with an image size of 640, follow these examples:
|
||||
To train a YOLO26n-pose model on the COCO8-Pose dataset for 100 epochs with an image size of 640, follow these examples:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -102,7 +102,7 @@ To train a YOLO11n-pose model on the COCO8-Pose dataset for 100 epochs with an i
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-pose.pt")
|
||||
model = YOLO("yolo26n-pose.pt")
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -111,7 +111,7 @@ To train a YOLO11n-pose model on the COCO8-Pose dataset for 100 epochs with an i
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo pose train data=coco8-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
|
||||
yolo pose train data=coco8-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a comprehensive list of training arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
|
@ -126,12 +126,12 @@ The COCO8-Pose dataset offers several benefits:
|
|||
|
||||
For more about its features and usage, see the [Dataset Introduction](#introduction) section.
|
||||
|
||||
### How does mosaicing benefit the YOLO11 training process using the COCO8-Pose dataset?
|
||||
### How does mosaicing benefit the YOLO26 training process using the COCO8-Pose dataset?
|
||||
|
||||
Mosaicing, demonstrated in the sample images of the COCO8-Pose dataset, combines multiple images into one, increasing the variety of objects and scenes within each training batch. This technique helps improve the model's ability to generalize across various object sizes, aspect ratios, and contexts, ultimately enhancing model performance. See the [Sample Images and Annotations](#sample-images-and-annotations) section for example images.
|
||||
|
||||
### Where can I find the COCO8-Pose dataset YAML file and how do I use it?
|
||||
|
||||
The COCO8-Pose dataset YAML file can be found at <https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml>. This file defines the dataset configuration, including paths, classes, and other relevant information. Use this file with the YOLO11 training scripts as mentioned in the [Train Example](#how-do-i-train-a-yolo11-model-using-the-coco8-pose-dataset-in-ultralytics) section.
|
||||
The COCO8-Pose dataset YAML file can be found at <https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml>. This file defines the dataset configuration, including paths, classes, and other relevant information. Use this file with the YOLO26 training scripts as mentioned in the [Train Example](#how-do-i-train-a-yolo26-model-using-the-coco8-pose-dataset-in-ultralytics) section.
|
||||
|
||||
For more FAQs and detailed documentation, visit the [Ultralytics Documentation](https://docs.ultralytics.com/).
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Discover the Dog-Pose dataset for pose detection. Featuring 6,773 training and 1,703 test images, it is a robust dataset for training YOLO11 models.
|
||||
keywords: Dog-Pose, Ultralytics, pose detection dataset, YOLO11, machine learning, computer vision, training data
|
||||
description: Discover the Dog-Pose dataset for pose detection. Featuring 6,773 training and 1,703 test images, it is a robust dataset for training YOLO26 models.
|
||||
keywords: Dog-Pose, Ultralytics, pose detection dataset, YOLO26, machine learning, computer vision, training data
|
||||
---
|
||||
|
||||
# Dog-Pose Dataset
|
||||
|
|
@ -18,14 +18,14 @@ The [Ultralytics](https://www.ultralytics.com/) Dog-Pose dataset is a high-quali
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO11 on the Stanford Dog Pose Estimation Dataset | Step-by-Step Tutorial
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on the Stanford Dog Pose Estimation Dataset | Step-by-Step Tutorial
|
||||
</p>
|
||||
|
||||
Each annotated image includes 24 keypoints with 3 dimensions per keypoint (x, y, visibility), making it a valuable resource for advanced research and development in computer vision.
|
||||
|
||||
<img src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-dogs.avif" alt="Ultralytics Dog-pose display image" width="800">
|
||||
|
||||
This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
|
||||
This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
|
|
@ -51,7 +51,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n-pose model on the Dog-pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n-pose model on the Dog-pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -61,7 +61,7 @@ To train a YOLO11n-pose model on the Dog-pose dataset for 100 [epochs](https://w
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="dog-pose.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -71,7 +71,7 @@ To train a YOLO11n-pose model on the Dog-pose dataset for 100 [epochs](https://w
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=dog-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
|
||||
yolo pose train data=dog-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -111,13 +111,13 @@ We would like to acknowledge the Stanford team for creating and maintaining this
|
|||
|
||||
## FAQ
|
||||
|
||||
### What is the Dog-pose dataset, and how is it used with Ultralytics YOLO11?
|
||||
### What is the Dog-pose dataset, and how is it used with Ultralytics YOLO26?
|
||||
|
||||
The Dog-Pose dataset features 6,773 training and 1,703 test images annotated with 24 keypoints for dog pose estimation. It's designed for training and validating models with [Ultralytics YOLO11](../../models/yolo11.md), supporting applications like animal behavior analysis, pet monitoring, and veterinary studies. The dataset's comprehensive annotations make it ideal for developing accurate pose estimation models for canines.
|
||||
The Dog-Pose dataset features 6,773 training and 1,703 test images annotated with 24 keypoints for dog pose estimation. It's designed for training and validating models with [Ultralytics YOLO26](../../models/yolo26.md), supporting applications like animal behavior analysis, pet monitoring, and veterinary studies. The dataset's comprehensive annotations make it ideal for developing accurate pose estimation models for canines.
|
||||
|
||||
### How do I train a YOLO11 model using the Dog-pose dataset in Ultralytics?
|
||||
### How do I train a YOLO26 model using the Dog-pose dataset in Ultralytics?
|
||||
|
||||
To train a YOLO11n-pose model on the Dog-pose dataset for 100 epochs with an image size of 640, follow these examples:
|
||||
To train a YOLO26n-pose model on the Dog-pose dataset for 100 epochs with an image size of 640, follow these examples:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -127,7 +127,7 @@ To train a YOLO11n-pose model on the Dog-pose dataset for 100 epochs with an ima
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-pose.pt")
|
||||
model = YOLO("yolo26n-pose.pt")
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="dog-pose.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -136,7 +136,7 @@ To train a YOLO11n-pose model on the Dog-pose dataset for 100 epochs with an ima
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo pose train data=dog-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
|
||||
yolo pose train data=dog-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a comprehensive list of training arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
|
@ -155,7 +155,7 @@ The Dog-pose dataset offers several benefits:
|
|||
|
||||
For more about its features and usage, see the [Dataset Introduction](#introduction) section.
|
||||
|
||||
### How does mosaicing benefit the YOLO11 training process using the Dog-pose dataset?
|
||||
### How does mosaicing benefit the YOLO26 training process using the Dog-pose dataset?
|
||||
|
||||
Mosaicing, as illustrated in the sample images from the Dog-pose dataset, merges multiple images into a single composite, enriching the diversity of objects and scenes in each training batch. This technique offers several benefits:
|
||||
|
||||
|
|
@ -170,6 +170,6 @@ This approach leads to more robust models that perform better in real-world scen
|
|||
|
||||
The Dog-pose dataset YAML file can be found at <https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dog-pose.yaml>. This file defines the dataset configuration, including paths, classes, keypoint details, and other relevant information. The YAML specifies 24 keypoints with 3 dimensions per keypoint, making it suitable for detailed pose estimation tasks.
|
||||
|
||||
To use this file with YOLO11 training scripts, simply reference it in your training command as shown in the [Usage](#usage) section. The dataset will be automatically downloaded when first used, making setup straightforward.
|
||||
To use this file with YOLO26 training scripts, simply reference it in your training command as shown in the [Usage](#usage) section. The dataset will be automatically downloaded when first used, making setup straightforward.
|
||||
|
||||
For more FAQs and detailed documentation, visit the [Ultralytics Documentation](https://docs.ultralytics.com/).
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ keywords: Hand KeyPoints, pose estimation, dataset, keypoints, MediaPipe, YOLO,
|
|||
|
||||
## Introduction
|
||||
|
||||
The hand-keypoints dataset contains 26,768 images of hands annotated with keypoints, making it suitable for training models like Ultralytics YOLO for pose estimation tasks. The annotations were generated using the Google MediaPipe library, ensuring high [accuracy](https://www.ultralytics.com/glossary/accuracy) and consistency, and the dataset is compatible with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) formats.
|
||||
The hand-keypoints dataset contains 26,768 images of hands annotated with keypoints, making it suitable for training models like Ultralytics YOLO for pose estimation tasks. The annotations were generated using the Google MediaPipe library, ensuring high [accuracy](https://www.ultralytics.com/glossary/accuracy) and consistency, and the dataset is compatible with [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics) formats.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
|
|
@ -18,7 +18,7 @@ The hand-keypoints dataset contains 26,768 images of hands annotated with keypoi
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Hand Keypoints Estimation with Ultralytics YOLO11 | Human Hand Pose Estimation Tutorial
|
||||
<strong>Watch:</strong> Hand Keypoints Estimation with Ultralytics YOLO26 | Human Hand Pose Estimation Tutorial
|
||||
</p>
|
||||
|
||||
## Hand Landmarks
|
||||
|
|
@ -41,7 +41,7 @@ Each hand has a total of 21 keypoints.
|
|||
## Key Features
|
||||
|
||||
- **Large Dataset**: 26,768 images with hand keypoint annotations.
|
||||
- **YOLO11 Compatibility**: Labels ship in YOLO keypoint format and are ready for use with YOLO11 models.
|
||||
- **YOLO26 Compatibility**: Labels ship in YOLO keypoint format and are ready for use with YOLO26 models.
|
||||
- **21 Keypoints**: Detailed hand pose representation spanning the wrist and four points per finger.
|
||||
|
||||
## Dataset Structure
|
||||
|
|
@ -67,7 +67,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n-pose model on the Hand Keypoints dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n-pose model on the Hand Keypoints dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -77,7 +77,7 @@ To train a YOLO11n-pose model on the Hand Keypoints dataset for 100 [epochs](htt
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="hand-keypoints.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -87,7 +87,7 @@ To train a YOLO11n-pose model on the Hand Keypoints dataset for 100 [epochs](htt
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=hand-keypoints.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
|
||||
yolo pose train data=hand-keypoints.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -120,9 +120,9 @@ We would also like to acknowledge the creator of this dataset, [Rion Dsilva](htt
|
|||
|
||||
## FAQ
|
||||
|
||||
### How do I train a YOLO11 model on the Hand Keypoints dataset?
|
||||
### How do I train a YOLO26 model on the Hand Keypoints dataset?
|
||||
|
||||
To train a YOLO11 model on the Hand Keypoints dataset, you can use either Python or the command line interface (CLI). Here's an example for training a YOLO11n-pose model for 100 epochs with an image size of 640:
|
||||
To train a YOLO26 model on the Hand Keypoints dataset, you can use either Python or the command line interface (CLI). Here's an example for training a YOLO26n-pose model for 100 epochs with an image size of 640:
|
||||
|
||||
!!! example
|
||||
|
||||
|
|
@ -132,7 +132,7 @@ To train a YOLO11 model on the Hand Keypoints dataset, you can use either Python
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="hand-keypoints.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -142,7 +142,7 @@ To train a YOLO11 model on the Hand Keypoints dataset, you can use either Python
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=hand-keypoints.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
|
||||
yolo pose train data=hand-keypoints.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
|
@ -152,7 +152,7 @@ For a comprehensive list of available arguments, refer to the model [Training](.
|
|||
The Hand Keypoints dataset is designed for advanced [pose estimation](https://docs.ultralytics.com/datasets/pose/) tasks and includes several key features:
|
||||
|
||||
- **Large Dataset**: Contains 26,768 images with hand keypoint annotations.
|
||||
- **YOLO11 Compatibility**: Ready for use with YOLO11 models.
|
||||
- **YOLO26 Compatibility**: Ready for use with YOLO26 models.
|
||||
- **21 Keypoints**: Detailed hand pose representation, including wrist and finger joints.
|
||||
|
||||
For more details, you can explore the [Hand Keypoints Dataset](#introduction) section.
|
||||
|
|
|
|||
|
|
@ -62,7 +62,7 @@ The `train` and `val` fields specify the paths to the directories containing the
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -72,7 +72,7 @@ The `train` and `val` fields specify the paths to the directories containing the
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=coco8-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
|
||||
yolo pose train data=coco8-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Supported Datasets
|
||||
|
|
@ -170,7 +170,7 @@ To use the [COCO-Pose dataset](https://docs.ultralytics.com/datasets/pose/coco/)
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo11n-pose.pt") # load pretrained model
|
||||
model = YOLO("yolo26n-pose.pt") # load pretrained model
|
||||
results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
|
|
@ -187,7 +187,7 @@ To add your dataset:
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo11n-pose.pt")
|
||||
model = YOLO("yolo26n-pose.pt")
|
||||
results = model.train(data="your-dataset.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore Ultralytics Tiger-Pose dataset with 263 diverse images. Ideal for testing, training, and refining pose estimation algorithms.
|
||||
keywords: Ultralytics, Tiger-Pose, dataset, pose estimation, YOLO11, training data, machine learning, neural networks
|
||||
keywords: Ultralytics, Tiger-Pose, dataset, pose estimation, YOLO26, training data, machine learning, neural networks
|
||||
---
|
||||
|
||||
# Tiger-Pose Dataset
|
||||
|
|
@ -12,7 +12,7 @@ keywords: Ultralytics, Tiger-Pose, dataset, pose estimation, YOLO11, training da
|
|||
|
||||
Despite its manageable training split of 210 images, the Tiger-Pose dataset offers diversity, making it suitable for assessing training pipelines, identifying potential errors, and serving as a valuable preliminary step before working with larger datasets for [pose estimation](https://docs.ultralytics.com/tasks/pose/).
|
||||
|
||||
This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
|
||||
This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
|
|
@ -28,7 +28,7 @@ This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Train YOLO11 Pose Model on Tiger-Pose Dataset Using Ultralytics HUB
|
||||
<strong>Watch:</strong> Train YOLO26 Pose Model on Tiger-Pose Dataset Using Ultralytics Platform
|
||||
</p>
|
||||
|
||||
## Dataset YAML
|
||||
|
|
@ -43,7 +43,7 @@ A YAML (Yet Another Markup Language) file serves as the means to specify the con
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n-pose model on the Tiger-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n-pose model on the Tiger-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -53,7 +53,7 @@ To train a YOLO11n-pose model on the Tiger-Pose dataset for 100 [epochs](https:/
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="tiger-pose.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -63,7 +63,7 @@ To train a YOLO11n-pose model on the Tiger-Pose dataset for 100 [epochs](https:/
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=tiger-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
|
||||
yolo pose train data=tiger-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -107,11 +107,11 @@ The dataset has been released available under the [AGPL-3.0 License](https://git
|
|||
|
||||
### What is the Ultralytics Tiger-Pose dataset used for?
|
||||
|
||||
The Ultralytics Tiger-Pose dataset is designed for pose estimation tasks, consisting of 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0). The dataset is divided into 210 training images and 53 validation images. It is particularly useful for testing, training, and refining pose estimation algorithms using [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
|
||||
The Ultralytics Tiger-Pose dataset is designed for pose estimation tasks, consisting of 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0). The dataset is divided into 210 training images and 53 validation images. It is particularly useful for testing, training, and refining pose estimation algorithms using [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
### How do I train a YOLO11 model on the Tiger-Pose dataset?
|
||||
### How do I train a YOLO26 model on the Tiger-Pose dataset?
|
||||
|
||||
To train a YOLO11n-pose model on the Tiger-Pose dataset for 100 epochs with an image size of 640, use the following code snippets. For more details, visit the [Training](../../modes/train.md) page:
|
||||
To train a YOLO26n-pose model on the Tiger-Pose dataset for 100 epochs with an image size of 640, use the following code snippets. For more details, visit the [Training](../../modes/train.md) page:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -121,7 +121,7 @@ To train a YOLO11n-pose model on the Tiger-Pose dataset for 100 epochs with an i
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="tiger-pose.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -132,16 +132,16 @@ To train a YOLO11n-pose model on the Tiger-Pose dataset for 100 epochs with an i
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=tiger-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
|
||||
yolo pose train data=tiger-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### What configurations does the `tiger-pose.yaml` file include?
|
||||
|
||||
The `tiger-pose.yaml` file is used to specify the configuration details of the Tiger-Pose dataset. It includes crucial data such as file paths and class definitions. To see the exact configuration, you can check out the [Ultralytics Tiger-Pose Dataset Configuration File](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/tiger-pose.yaml).
|
||||
|
||||
### How can I run inference using a YOLO11 model trained on the Tiger-Pose dataset?
|
||||
### How can I run inference using a YOLO26 model trained on the Tiger-Pose dataset?
|
||||
|
||||
To perform inference using a YOLO11 model trained on the Tiger-Pose dataset, you can use the following code snippets. For a detailed guide, visit the [Prediction](../../modes/predict.md) page:
|
||||
To perform inference using a YOLO26 model trained on the Tiger-Pose dataset, you can use the following code snippets. For a detailed guide, visit the [Prediction](../../modes/predict.md) page:
|
||||
|
||||
!!! example "Inference Example"
|
||||
|
||||
|
|
@ -167,4 +167,4 @@ To perform inference using a YOLO11 model trained on the Tiger-Pose dataset, you
|
|||
|
||||
### What are the benefits of using the Tiger-Pose dataset for pose estimation?
|
||||
|
||||
The Tiger-Pose dataset, despite its manageable size of 210 images for training, provides a diverse collection of images that are ideal for testing pose estimation pipelines. The dataset helps identify potential errors and acts as a preliminary step before working with larger datasets. Additionally, the dataset supports the training and refinement of pose estimation algorithms using advanced tools like [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics), enhancing model performance and [accuracy](https://www.ultralytics.com/glossary/accuracy).
|
||||
The Tiger-Pose dataset, despite its manageable size of 210 images for training, provides a diverse collection of images that are ideal for testing pose estimation pipelines. The dataset helps identify potential errors and acts as a preliminary step before working with larger datasets. Additionally, the dataset supports the training and refinement of pose estimation algorithms using advanced tools like [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics), enhancing model performance and [accuracy](https://www.ultralytics.com/glossary/accuracy).
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ keywords: Carparts Segmentation Dataset, computer vision, automotive AI, vehicle
|
|||
|
||||
The Carparts Segmentation Dataset, available on Roboflow Universe, is a curated collection of images and videos designed for [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications, specifically focusing on [segmentation tasks](https://docs.ultralytics.com/tasks/segment/). Hosted on Roboflow Universe, this dataset provides a diverse set of visuals captured from multiple perspectives, offering valuable [annotated](https://www.ultralytics.com/glossary/data-labeling) examples for training and testing segmentation models.
|
||||
|
||||
Whether you're working on [automotive research](https://www.ultralytics.com/solutions/ai-in-automotive), developing AI solutions for vehicle maintenance, or exploring computer vision applications, the Carparts Segmentation Dataset serves as a valuable resource for enhancing the [accuracy](https://www.ultralytics.com/glossary/accuracy) and efficiency of your projects using models like [Ultralytics YOLO](../../models/yolo11.md).
|
||||
Whether you're working on [automotive research](https://www.ultralytics.com/solutions/ai-in-automotive), developing AI solutions for vehicle maintenance, or exploring computer vision applications, the Carparts Segmentation Dataset serves as a valuable resource for enhancing the [accuracy](https://www.ultralytics.com/glossary/accuracy) and efficiency of your projects using models like [Ultralytics YOLO](../../models/yolo26.md).
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
|
|
@ -20,7 +20,7 @@ Whether you're working on [automotive research](https://www.ultralytics.com/solu
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Carparts <a href="https://www.ultralytics.com/glossary/instance-segmentation">Instance Segmentation</a> with Ultralytics YOLO11.
|
||||
<strong>Watch:</strong> Carparts <a href="https://www.ultralytics.com/glossary/instance-segmentation">Instance Segmentation</a> with Ultralytics YOLO26.
|
||||
</p>
|
||||
|
||||
## Dataset Structure
|
||||
|
|
@ -58,7 +58,7 @@ A [YAML](https://www.ultralytics.com/glossary/yaml) (Yet Another Markup Language
|
|||
|
||||
## Usage
|
||||
|
||||
To train an [Ultralytics YOLO11](../../models/yolo11.md) model on the Carparts Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following code snippets. Refer to the model [Training guide](../../modes/train.md) for a comprehensive list of available arguments and explore [model training tips](https://docs.ultralytics.com/guides/model-training-tips/) for best practices.
|
||||
To train an [Ultralytics YOLO26](../../models/yolo26.md) model on the Carparts Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following code snippets. Refer to the model [Training guide](../../modes/train.md) for a comprehensive list of available arguments and explore [model training tips](https://docs.ultralytics.com/guides/model-training-tips/) for best practices.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -67,8 +67,8 @@ To train an [Ultralytics YOLO11](../../models/yolo11.md) model on the Carparts S
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained segmentation model like YOLO11n-seg
|
||||
model = YOLO("yolo11n-seg.pt") # load a pretrained model (recommended for training)
|
||||
# Load a pretrained segmentation model like YOLO26n-seg
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model on the Carparts Segmentation dataset
|
||||
results = model.train(data="carparts-seg.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -85,7 +85,7 @@ To train an [Ultralytics YOLO11](../../models/yolo11.md) model on the Carparts S
|
|||
```bash
|
||||
# Start training from a pretrained *.pt model using the Command Line Interface
|
||||
# Specify the dataset config file, model, number of epochs, and image size
|
||||
yolo segment train data=carparts-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
|
||||
yolo segment train data=carparts-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
|
||||
# Validate the trained model using the validation set
|
||||
yolo segment val data=carparts-seg.yaml model=path/to/best.pt
|
||||
|
|
@ -134,9 +134,9 @@ We acknowledge the contribution of Gianmarco Russo and the Roboflow team in crea
|
|||
|
||||
The Carparts Segmentation Dataset is a specialized collection of images and videos for training computer vision models to perform [segmentation](https://docs.ultralytics.com/tasks/segment/) on car parts. It includes diverse visuals with detailed annotations, suitable for automotive AI applications.
|
||||
|
||||
### How can I use the Carparts Segmentation Dataset with Ultralytics YOLO11?
|
||||
### How can I use the Carparts Segmentation Dataset with Ultralytics YOLO26?
|
||||
|
||||
You can train an [Ultralytics YOLO11](../../models/yolo11.md) segmentation model using this dataset. Load a pretrained model (e.g., `yolo11n-seg.pt`) and initiate training using the provided Python or CLI examples, referencing the `carparts-seg.yaml` configuration file. Check the [Training Guide](../../modes/train.md) for detailed instructions.
|
||||
You can train an [Ultralytics YOLO26](../../models/yolo26.md) segmentation model using this dataset. Load a pretrained model (e.g., `yolo26n-seg.pt`) and initiate training using the provided Python or CLI examples, referencing the `carparts-seg.yaml` configuration file. Check the [Training Guide](../../modes/train.md) for detailed instructions.
|
||||
|
||||
!!! example "Train Example Snippet"
|
||||
|
||||
|
|
@ -146,7 +146,7 @@ You can train an [Ultralytics YOLO11](../../models/yolo11.md) segmentation model
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-seg.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="carparts-seg.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -155,7 +155,7 @@ You can train an [Ultralytics YOLO11](../../models/yolo11.md) segmentation model
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo segment train data=carparts-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
|
||||
yolo segment train data=carparts-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### What are some applications of Carparts Segmentation?
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore the COCO-Seg dataset, an extension of COCO, with detailed segmentation annotations. Learn how to train YOLO models with COCO-Seg.
|
||||
keywords: COCO-Seg, dataset, YOLO models, instance segmentation, object detection, COCO dataset, YOLO11, computer vision, Ultralytics, machine learning
|
||||
keywords: COCO-Seg, dataset, YOLO models, instance segmentation, object detection, COCO dataset, YOLO26, computer vision, Ultralytics, machine learning
|
||||
---
|
||||
|
||||
# COCO-Seg Dataset
|
||||
|
|
@ -43,7 +43,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n-seg model on the COCO-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n-seg model on the COCO-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -53,7 +53,7 @@ To train a YOLO11n-seg model on the COCO-Seg dataset for 100 [epochs](https://ww
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-seg.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -63,7 +63,7 @@ To train a YOLO11n-seg model on the COCO-Seg dataset for 100 [epochs](https://ww
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo segment train data=coco.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
|
||||
yolo segment train data=coco.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -103,9 +103,9 @@ We extend our thanks to the COCO Consortium for creating and maintaining this in
|
|||
|
||||
The [COCO-Seg](https://cocodataset.org/#home) dataset is an extension of the original COCO (Common Objects in Context) dataset, specifically designed for instance segmentation tasks. While it uses the same images as the COCO dataset, COCO-Seg includes more detailed segmentation annotations, making it a powerful resource for researchers and developers focusing on [object instance segmentation](https://docs.ultralytics.com/tasks/segment/).
|
||||
|
||||
### How can I train a YOLO11 model using the COCO-Seg dataset?
|
||||
### How can I train a YOLO26 model using the COCO-Seg dataset?
|
||||
|
||||
To train a YOLO11n-seg model on the COCO-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a detailed list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n-seg model on the COCO-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a detailed list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -115,7 +115,7 @@ To train a YOLO11n-seg model on the COCO-Seg dataset for 100 epochs with an imag
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-seg.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -125,7 +125,7 @@ To train a YOLO11n-seg model on the COCO-Seg dataset for 100 epochs with an imag
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo segment train data=coco.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
|
||||
yolo segment train data=coco.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### What are the key features of the COCO-Seg dataset?
|
||||
|
|
@ -139,11 +139,11 @@ The COCO-Seg dataset includes several key features:
|
|||
|
||||
### What pretrained models are available for COCO-Seg, and what are their performance metrics?
|
||||
|
||||
The COCO-Seg dataset supports multiple pretrained YOLO11 segmentation models with varying performance metrics. Here's a summary of the available models and their key metrics:
|
||||
The COCO-Seg dataset supports multiple pretrained YOLO26 segmentation models with varying performance metrics. Here's a summary of the available models and their key metrics:
|
||||
|
||||
{% include "macros/yolo-seg-perf.md" %}
|
||||
|
||||
These models range from the lightweight YOLO11n-seg to the more powerful YOLO11x-seg, offering different trade-offs between speed and accuracy to suit various application requirements. For more information on model selection, visit the [Ultralytics models page](https://docs.ultralytics.com/models/).
|
||||
These models range from the lightweight YOLO26n-seg to the more powerful YOLO26x-seg, offering different trade-offs between speed and accuracy to suit various application requirements. For more information on model selection, visit the [Ultralytics models page](https://docs.ultralytics.com/models/).
|
||||
|
||||
### How is the COCO-Seg dataset structured and what subsets does it contain?
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Discover the COCO128-Seg dataset by Ultralytics, a compact yet diverse segmentation dataset ideal for testing and training YOLO11 models.
|
||||
keywords: COCO128-Seg, Ultralytics, segmentation dataset, YOLO11, COCO 2017, model training, computer vision, dataset configuration
|
||||
description: Discover the COCO128-Seg dataset by Ultralytics, a compact yet diverse segmentation dataset ideal for testing and training YOLO26 models.
|
||||
keywords: COCO128-Seg, Ultralytics, segmentation dataset, YOLO26, COCO 2017, model training, computer vision, dataset configuration
|
||||
---
|
||||
|
||||
# COCO128-Seg Dataset
|
||||
|
|
@ -16,7 +16,7 @@ keywords: COCO128-Seg, Ultralytics, segmentation dataset, YOLO11, COCO 2017, mod
|
|||
- **Classes**: Same 80 object categories as COCO.
|
||||
- **Labels**: YOLO-format polygons saved beside each image inside `labels/{train,val}`.
|
||||
|
||||
This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
|
||||
This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
|
|
@ -30,7 +30,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n-seg model on the COCO128-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n-seg model on the COCO128-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -40,7 +40,7 @@ To train a YOLO11n-seg model on the COCO128-Seg dataset for 100 [epochs](https:/
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-seg.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco128-seg.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -50,7 +50,7 @@ To train a YOLO11n-seg model on the COCO128-Seg dataset for 100 [epochs](https:/
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo segment train data=coco128-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
|
||||
yolo segment train data=coco128-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -86,13 +86,13 @@ We would like to acknowledge the COCO Consortium for creating and maintaining th
|
|||
|
||||
## FAQ
|
||||
|
||||
### What is the COCO128-Seg dataset, and how is it used in Ultralytics YOLO11?
|
||||
### What is the COCO128-Seg dataset, and how is it used in Ultralytics YOLO26?
|
||||
|
||||
The **COCO128-Seg dataset** is a compact instance segmentation dataset by Ultralytics, consisting of the first 128 images from the COCO train 2017 set. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics [YOLO11](https://github.com/ultralytics/ultralytics) and [HUB](https://hub.ultralytics.com/) for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model [Training](../../modes/train.md) page.
|
||||
The **COCO128-Seg dataset** is a compact instance segmentation dataset by Ultralytics, consisting of the first 128 images from the COCO train 2017 set. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics [YOLO26](https://github.com/ultralytics/ultralytics) and [Platform](https://platform.ultralytics.com/) for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### How can I train a YOLO11n-seg model using the COCO128-Seg dataset?
|
||||
### How can I train a YOLO26n-seg model using the COCO128-Seg dataset?
|
||||
|
||||
To train a **YOLO11n-seg** model on the COCO128-Seg dataset for 100 epochs with an image size of 640, you can use Python or CLI commands. Here's a quick example:
|
||||
To train a **YOLO26n-seg** model on the COCO128-Seg dataset for 100 epochs with an image size of 640, you can use Python or CLI commands. Here's a quick example:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -102,7 +102,7 @@ To train a **YOLO11n-seg** model on the COCO128-Seg dataset for 100 epochs with
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-seg.pt") # Load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-seg.pt") # Load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco128-seg.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -112,7 +112,7 @@ To train a **YOLO11n-seg** model on the COCO128-Seg dataset for 100 epochs with
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo segment train data=coco128-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
|
||||
yolo segment train data=coco128-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a thorough explanation of available arguments and configuration options, you can check the [Training](../../modes/train.md) documentation.
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Discover the versatile and manageable COCO8-Seg dataset by Ultralytics, ideal for testing and debugging segmentation models or new detection approaches.
|
||||
keywords: COCO8-Seg, Ultralytics, segmentation dataset, YOLO11, COCO 2017, model training, computer vision, dataset configuration
|
||||
keywords: COCO8-Seg, Ultralytics, segmentation dataset, YOLO26, COCO 2017, model training, computer vision, dataset configuration
|
||||
---
|
||||
|
||||
# COCO8-Seg Dataset
|
||||
|
|
@ -16,7 +16,7 @@ keywords: COCO8-Seg, Ultralytics, segmentation dataset, YOLO11, COCO 2017, model
|
|||
- **Classes**: 80 COCO categories.
|
||||
- **Labels**: YOLO-format polygons stored under `labels/{train,val}` matching each image file.
|
||||
|
||||
This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
|
||||
This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
|
|
@ -30,7 +30,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
## Usage
|
||||
|
||||
To train a YOLO11n-seg model on the COCO8-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
To train a YOLO26n-seg model on the COCO8-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -40,7 +40,7 @@ To train a YOLO11n-seg model on the COCO8-Seg dataset for 100 [epochs](https://w
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-seg.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -50,7 +50,7 @@ To train a YOLO11n-seg model on the COCO8-Seg dataset for 100 [epochs](https://w
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo segment train data=coco8-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
|
||||
yolo segment train data=coco8-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
|
@ -86,13 +86,13 @@ We would like to acknowledge the COCO Consortium for creating and maintaining th
|
|||
|
||||
## FAQ
|
||||
|
||||
### What is the COCO8-Seg dataset, and how is it used in Ultralytics YOLO11?
|
||||
### What is the COCO8-Seg dataset, and how is it used in Ultralytics YOLO26?
|
||||
|
||||
The **COCO8-Seg dataset** is a compact instance segmentation dataset by Ultralytics, consisting of the first 8 images from the COCO train 2017 set—4 images for training and 4 for validation. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics [YOLO11](https://github.com/ultralytics/ultralytics) and [HUB](https://hub.ultralytics.com/) for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model [Training](../../modes/train.md) page.
|
||||
The **COCO8-Seg dataset** is a compact instance segmentation dataset by Ultralytics, consisting of the first 8 images from the COCO train 2017 set—4 images for training and 4 for validation. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics [YOLO26](https://github.com/ultralytics/ultralytics) and [Platform](https://platform.ultralytics.com/) for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### How can I train a YOLO11n-seg model using the COCO8-Seg dataset?
|
||||
### How can I train a YOLO26n-seg model using the COCO8-Seg dataset?
|
||||
|
||||
To train a **YOLO11n-seg** model on the COCO8-Seg dataset for 100 epochs with an image size of 640, you can use Python or CLI commands. Here's a quick example:
|
||||
To train a **YOLO26n-seg** model on the COCO8-Seg dataset for 100 epochs with an image size of 640, you can use Python or CLI commands. Here's a quick example:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -102,7 +102,7 @@ To train a **YOLO11n-seg** model on the COCO8-Seg dataset for 100 epochs with an
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-seg.pt") # Load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-seg.pt") # Load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -112,7 +112,7 @@ To train a **YOLO11n-seg** model on the COCO8-Seg dataset for 100 epochs with an
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo segment train data=coco8-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
|
||||
yolo segment train data=coco8-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a thorough explanation of available arguments and configuration options, you can check the [Training](../../modes/train.md) documentation.
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@ The Crack Segmentation Dataset is organized into three subsets:
|
|||
|
||||
Crack segmentation finds practical applications in [infrastructure maintenance](https://www.ultralytics.com/blog/using-ai-for-crack-detection-and-segmentation), aiding in the identification and assessment of structural damage in buildings, bridges, and roads. It also plays a crucial role in enhancing [road safety](https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries) by enabling automated systems to detect pavement cracks for timely repairs.
|
||||
|
||||
In industrial settings, crack detection using deep learning models like [Ultralytics YOLO11](../../models/yolo11.md) helps ensure building integrity in construction, prevents costly downtimes in [manufacturing](https://www.ultralytics.com/solutions/ai-in-manufacturing), and makes road inspections safer and more effective. Automatically identifying and classifying cracks allows maintenance teams to prioritize repairs efficiently, contributing to better [model evaluation insights](../../guides/model-evaluation-insights.md).
|
||||
In industrial settings, crack detection using deep learning models like [Ultralytics YOLO26](../../models/yolo26.md) helps ensure building integrity in construction, prevents costly downtimes in [manufacturing](https://www.ultralytics.com/solutions/ai-in-manufacturing), and makes road inspections safer and more effective. Automatically identifying and classifying cracks allows maintenance teams to prioritize repairs efficiently, contributing to better [model evaluation insights](../../guides/model-evaluation-insights.md).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
|
|
@ -49,7 +49,7 @@ A [YAML](https://www.ultralytics.com/glossary/yaml) (Yet Another Markup Language
|
|||
|
||||
## Usage
|
||||
|
||||
To train the Ultralytics YOLO11n-seg model on the Crack Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following [Python](https://www.python.org/) or CLI snippets. Refer to the model [Training](../../modes/train.md) documentation page for a comprehensive list of available arguments and configurations like [hyperparameter tuning](../../guides/hyperparameter-tuning.md).
|
||||
To train the Ultralytics YOLO26n-seg model on the Crack Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following [Python](https://www.python.org/) or CLI snippets. Refer to the model [Training](../../modes/train.md) documentation page for a comprehensive list of available arguments and configurations like [hyperparameter tuning](../../guides/hyperparameter-tuning.md).
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -59,8 +59,8 @@ To train the Ultralytics YOLO11n-seg model on the Crack Segmentation dataset for
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
# Using a pretrained model like yolo11n-seg.pt is recommended for faster convergence
|
||||
model = YOLO("yolo11n-seg.pt")
|
||||
# Using a pretrained model like yolo26n-seg.pt is recommended for faster convergence
|
||||
model = YOLO("yolo26n-seg.pt")
|
||||
|
||||
# Train the model on the Crack Segmentation dataset
|
||||
# Ensure 'crack-seg.yaml' is accessible or provide the full path
|
||||
|
|
@ -75,7 +75,7 @@ To train the Ultralytics YOLO11n-seg model on the Crack Segmentation dataset for
|
|||
```bash
|
||||
# Start training from a pretrained *.pt model using the Command Line Interface
|
||||
# Ensure the dataset YAML file 'crack-seg.yaml' is correctly configured and accessible
|
||||
yolo segment train data=crack-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
|
||||
yolo segment train data=crack-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
|
@ -118,9 +118,9 @@ We acknowledge the team at Roboflow for making the Crack Segmentation dataset av
|
|||
|
||||
The Crack Segmentation Dataset is a collection of 4029 static images designed for transportation and public safety studies. It's suitable for tasks like [self-driving car](https://www.ultralytics.com/blog/ai-in-self-driving-cars) model development and [infrastructure maintenance](https://www.ultralytics.com/blog/using-ai-for-crack-detection-and-segmentation). It includes training, testing, and validation sets for crack detection and [segmentation](../../tasks/segment.md) tasks.
|
||||
|
||||
### How do I train a model using the Crack Segmentation Dataset with Ultralytics YOLO11?
|
||||
### How do I train a model using the Crack Segmentation Dataset with Ultralytics YOLO26?
|
||||
|
||||
To train an [Ultralytics YOLO11](../../models/yolo11.md) model on this dataset, use the provided Python or CLI examples. Detailed instructions and parameters are available on the model [Training](../../modes/train.md) page. You can manage your training process using tools like [Ultralytics HUB](https://www.ultralytics.com/hub).
|
||||
To train an [Ultralytics YOLO26](../../models/yolo26.md) model on this dataset, use the provided Python or CLI examples. Detailed instructions and parameters are available on the model [Training](../../modes/train.md) page. You can manage your training process using tools like [Ultralytics Platform](https://platform.ultralytics.com).
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -130,7 +130,7 @@ To train an [Ultralytics YOLO11](../../models/yolo11.md) model on this dataset,
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained model (recommended)
|
||||
model = YOLO("yolo11n-seg.pt")
|
||||
model = YOLO("yolo26n-seg.pt")
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="crack-seg.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -140,7 +140,7 @@ To train an [Ultralytics YOLO11](../../models/yolo11.md) model on this dataset,
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained model via CLI
|
||||
yolo segment train data=crack-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
|
||||
yolo segment train data=crack-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### Why use the Crack Segmentation Dataset for self-driving car projects?
|
||||
|
|
|
|||
|
|
@ -64,7 +64,7 @@ The `train` and `val` fields specify the paths to the directories containing the
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-seg.pt") # load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -74,7 +74,7 @@ The `train` and `val` fields specify the paths to the directories containing the
|
|||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo segment train data=coco8-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
|
||||
yolo segment train data=coco8-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Supported Datasets
|
||||
|
|
@ -127,12 +127,12 @@ To auto-annotate your dataset using the Ultralytics framework, you can use the `
|
|||
```python
|
||||
from ultralytics.data.annotator import auto_annotate
|
||||
|
||||
auto_annotate(data="path/to/images", det_model="yolo11x.pt", sam_model="sam_b.pt")
|
||||
auto_annotate(data="path/to/images", det_model="yolo26x.pt", sam_model="sam_b.pt")
|
||||
```
|
||||
|
||||
{% include "macros/sam-auto-annotate.md" %}
|
||||
|
||||
The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pretrained detection models i.e. [YOLO11](../../models/yolo11.md), [YOLOv8](../../models/yolov8.md) or other [models](../../models/index.md) and segmentation models i.e, [SAM](../../models/sam.md), [SAM2](../../models/sam-2.md) or [MobileSAM](../../models/mobile-sam.md), the device to run the models on, and the output directory for saving the annotated results.
|
||||
The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pretrained detection models i.e. [YOLO26](../../models/yolo26.md), [YOLO11](../../models/yolo11.md) or other [models](../../models/index.md) and segmentation models i.e, [SAM](../../models/sam.md), [SAM2](../../models/sam-2.md) or [MobileSAM](../../models/mobile-sam.md), the device to run the models on, and the output directory for saving the annotated results.
|
||||
|
||||
By leveraging the power of pretrained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.
|
||||
|
||||
|
|
@ -206,7 +206,7 @@ Auto-annotation in Ultralytics YOLO allows you to generate segmentation annotati
|
|||
```python
|
||||
from ultralytics.data.annotator import auto_annotate
|
||||
|
||||
auto_annotate(data="path/to/images", det_model="yolo11x.pt", sam_model="sam_b.pt") # or sam_model="mobile_sam.pt"
|
||||
auto_annotate(data="path/to/images", det_model="yolo26x.pt", sam_model="sam_b.pt") # or sam_model="mobile_sam.pt"
|
||||
```
|
||||
|
||||
This function automates the annotation process, making it faster and more efficient. For more details, explore the [Auto-Annotate Reference](https://docs.ultralytics.com/reference/data/annotator/#ultralytics.data.annotator.auto_annotate).
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ The Package Segmentation Dataset, available on Roboflow Universe, is a curated c
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Train Package Segmentation Model using Ultralytics YOLO11 | Industrial Packages 🎉
|
||||
<strong>Watch:</strong> Train Package Segmentation Model using Ultralytics YOLO26 | Industrial Packages 🎉
|
||||
</p>
|
||||
|
||||
Containing a diverse set of images showcasing various packages in different contexts and environments, the dataset serves as a valuable resource for training and evaluating segmentation models. Whether you are engaged in logistics, warehouse automation, or any application requiring precise package analysis, the Package Segmentation Dataset provides a targeted and comprehensive set of images to enhance the performance of your computer vision algorithms. Explore more datasets for segmentation tasks on our [datasets overview page](https://docs.ultralytics.com/datasets/segment/).
|
||||
|
|
@ -37,7 +37,7 @@ Package segmentation, facilitated by the Package Segmentation Dataset, is crucia
|
|||
|
||||
### Smart Warehouses and Logistics
|
||||
|
||||
In modern warehouses, [vision AI solutions](https://www.ultralytics.com/solutions) can streamline operations by automating package identification and sorting. Computer vision models trained on this dataset can quickly detect and segment packages in real-time, even in challenging environments with dim lighting or cluttered spaces. This leads to faster processing times, reduced errors, and improved overall efficiency in [logistics operations](https://www.ultralytics.com/blog/ultralytics-yolo11-the-key-to-computer-vision-in-logistics).
|
||||
In modern warehouses, [vision AI solutions](https://www.ultralytics.com/solutions) can streamline operations by automating package identification and sorting. Computer vision models trained on this dataset can quickly detect and segment packages in real-time, even in challenging environments with dim lighting or cluttered spaces. This leads to faster processing times, reduced errors, and improved overall efficiency in [logistics operations](https://www.ultralytics.com/blog/ultralytics-yolo26-the-key-to-computer-vision-in-logistics).
|
||||
|
||||
### Quality Control and Damage Detection
|
||||
|
||||
|
|
@ -55,7 +55,7 @@ A YAML (Yet Another Markup Language) file defines the dataset configuration, inc
|
|||
|
||||
## Usage
|
||||
|
||||
To train an [Ultralytics YOLO11n](https://docs.ultralytics.com/models/yolo11/) model on the Package Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training page](../../modes/train.md).
|
||||
To train an [Ultralytics YOLO26n](https://docs.ultralytics.com/models/yolo26/) model on the Package Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training page](../../modes/train.md).
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
|
|
@ -65,7 +65,7 @@ To train an [Ultralytics YOLO11n](https://docs.ultralytics.com/models/yolo11/) m
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-seg.pt") # load a pretrained segmentation model (recommended for training)
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained segmentation model (recommended for training)
|
||||
|
||||
# Train the model on the Package Segmentation dataset
|
||||
results = model.train(data="package-seg.yaml", epochs=100, imgsz=640)
|
||||
|
|
@ -81,7 +81,7 @@ To train an [Ultralytics YOLO11n](https://docs.ultralytics.com/models/yolo11/) m
|
|||
|
||||
```bash
|
||||
# Load a pretrained segmentation model and start training
|
||||
yolo segment train data=package-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
|
||||
yolo segment train data=package-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
|
||||
# Resume training from the last checkpoint
|
||||
yolo segment train data=package-seg.yaml model=path/to/last.pt resume=True
|
||||
|
|
@ -102,17 +102,17 @@ The Package Segmentation dataset comprises a varied collection of images capture
|
|||
- This image displays an instance of package segmentation, featuring annotated masks outlining recognized package objects. The dataset incorporates a diverse collection of images taken in different locations, environments, and densities. It serves as a comprehensive resource for developing models specific to this [segmentation task](https://docs.ultralytics.com/tasks/segment/).
|
||||
- The example emphasizes the diversity and complexity present in the dataset, underscoring the significance of high-quality data for computer vision tasks involving package segmentation.
|
||||
|
||||
## Benefits of Using YOLO11 for Package Segmentation
|
||||
## Benefits of Using YOLO26 for Package Segmentation
|
||||
|
||||
[Ultralytics YOLO11](https://docs.ultralytics.com/models/yolo11/) offers several advantages for package segmentation tasks:
|
||||
[Ultralytics YOLO26](https://docs.ultralytics.com/models/yolo26/) offers several advantages for package segmentation tasks:
|
||||
|
||||
1. **Speed and Accuracy Balance**: YOLO11 achieves high precision and efficiency, making it ideal for [real-time inference](https://www.ultralytics.com/glossary/real-time-inference) in fast-paced logistics environments. It provides a strong balance compared to models like [YOLOv8](https://docs.ultralytics.com/models/yolov8/).
|
||||
1. **Speed and Accuracy Balance**: YOLO26 achieves high precision and efficiency, making it ideal for [real-time inference](https://www.ultralytics.com/glossary/real-time-inference) in fast-paced logistics environments. It provides a strong balance compared to models like [YOLOv8](https://docs.ultralytics.com/models/yolov8/).
|
||||
|
||||
2. **Adaptability**: Models trained with YOLO11 can adapt to various warehouse conditions, from dim lighting to cluttered spaces, ensuring robust performance.
|
||||
2. **Adaptability**: Models trained with YOLO26 can adapt to various warehouse conditions, from dim lighting to cluttered spaces, ensuring robust performance.
|
||||
|
||||
3. **Scalability**: During peak periods like holiday seasons, YOLO11 models can efficiently scale to handle increased package volumes without compromising performance or [accuracy](https://www.ultralytics.com/glossary/accuracy).
|
||||
3. **Scalability**: During peak periods like holiday seasons, YOLO26 models can efficiently scale to handle increased package volumes without compromising performance or [accuracy](https://www.ultralytics.com/glossary/accuracy).
|
||||
|
||||
4. **Integration Capabilities**: YOLO11 can be easily integrated with existing warehouse management systems and deployed across various platforms using formats like [ONNX](https://docs.ultralytics.com/integrations/onnx/) or [TensorRT](https://docs.ultralytics.com/integrations/tensorrt/), facilitating end-to-end automated solutions.
|
||||
4. **Integration Capabilities**: YOLO26 can be easily integrated with existing warehouse management systems and deployed across various platforms using formats like [ONNX](https://docs.ultralytics.com/integrations/onnx/) or [TensorRT](https://docs.ultralytics.com/integrations/tensorrt/), facilitating end-to-end automated solutions.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
|
|
@ -144,9 +144,9 @@ We express our gratitude to the creators of the Package Segmentation dataset for
|
|||
|
||||
- The Package Segmentation Dataset is a curated collection of images tailored for tasks involving package [image segmentation](https://www.ultralytics.com/glossary/image-segmentation). It includes diverse images of packages in various contexts, making it invaluable for training and evaluating segmentation models. This dataset is particularly useful for applications in logistics, warehouse automation, and any project requiring precise package analysis.
|
||||
|
||||
### How do I train an Ultralytics YOLO11 model on the Package Segmentation Dataset?
|
||||
### How do I train an Ultralytics YOLO26 model on the Package Segmentation Dataset?
|
||||
|
||||
- You can train an [Ultralytics YOLO11](https://docs.ultralytics.com/models/yolo11/) model using both Python and CLI methods. Use the code snippets provided in the [Usage](#usage) section. Refer to the model [Training page](../../modes/train.md) for more details on arguments and configurations.
|
||||
- You can train an [Ultralytics YOLO26](https://docs.ultralytics.com/models/yolo26/) model using both Python and CLI methods. Use the code snippets provided in the [Usage](#usage) section. Refer to the model [Training page](../../modes/train.md) for more details on arguments and configurations.
|
||||
|
||||
### What are the components of the Package Segmentation Dataset, and how is it structured?
|
||||
|
||||
|
|
@ -156,9 +156,9 @@ We express our gratitude to the creators of the Package Segmentation dataset for
|
|||
- **Validation set**: Includes 188 images with annotations.
|
||||
- This structure ensures a balanced dataset for thorough model training, validation, and testing, following best practices outlined in [model evaluation guides](https://docs.ultralytics.com/guides/model-evaluation-insights/).
|
||||
|
||||
### Why should I use Ultralytics YOLO11 with the Package Segmentation Dataset?
|
||||
### Why should I use Ultralytics YOLO26 with the Package Segmentation Dataset?
|
||||
|
||||
- Ultralytics YOLO11 provides state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) and segmentation tasks. Using it with the Package Segmentation Dataset allows you to leverage YOLO11's capabilities for precise package segmentation, which is especially beneficial for industries like [logistics](https://www.ultralytics.com/blog/ultralytics-yolo11-the-key-to-computer-vision-in-logistics) and warehouse automation.
|
||||
- Ultralytics YOLO26 provides state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) and segmentation tasks. Using it with the Package Segmentation Dataset allows you to leverage YOLO26's capabilities for precise package segmentation, which is especially beneficial for industries like [logistics](https://www.ultralytics.com/blog/ultralytics-yolo26-the-key-to-computer-vision-in-logistics) and warehouse automation.
|
||||
|
||||
### How can I access and use the package-seg.yaml file for the Package Segmentation Dataset?
|
||||
|
||||
|
|
|
|||
|
|
@ -28,14 +28,14 @@ Ultralytics YOLO supports the following tracking algorithms:
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.1, iou=0.7, show=True)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo track model=yolo11n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.1 iou=0.7 show=True
|
||||
yolo track model=yolo26n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.1 iou=0.7 show=True
|
||||
```
|
||||
|
||||
## Persisting Tracks Between Frames
|
||||
|
|
@ -52,7 +52,7 @@ For continuous tracking across video frames, you can use the `persist=True` para
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load the YOLO model
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Open the video file
|
||||
cap = cv2.VideoCapture("path/to/video.mp4")
|
||||
|
|
@ -89,17 +89,17 @@ To use Multi-Object Tracking with Ultralytics YOLO, you can start by using the P
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo11n.pt") # Load the YOLO11 model
|
||||
model = YOLO("yolo26n.pt") # Load the YOLO26 model
|
||||
results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.1, iou=0.7, show=True)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo track model=yolo11n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.1 iou=0.7 show=True
|
||||
yolo track model=yolo26n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.1 iou=0.7 show=True
|
||||
```
|
||||
|
||||
These commands load the YOLO11 model and use it for tracking objects in the given video source with specific confidence (`conf`) and [Intersection over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (`iou`) thresholds. For more details, refer to the [track mode documentation](../../modes/track.md).
|
||||
These commands load the YOLO26 model and use it for tracking objects in the given video source with specific confidence (`conf`) and [Intersection over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (`iou`) thresholds. For more details, refer to the [track mode documentation](../../modes/track.md).
|
||||
|
||||
### What are the upcoming features for training trackers in Ultralytics?
|
||||
|
||||
|
|
@ -137,6 +137,6 @@ You can customize the tracker by creating a modified version of the tracker conf
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
results = model.track(source="video.mp4", tracker="custom_tracker.yaml")
|
||||
```
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn to create line graphs, bar plots, and pie charts using Python with guided instructions and code snippets. Maximize your data visualization skills!
|
||||
keywords: Ultralytics, YOLO11, data visualization, line graphs, bar plots, pie charts, Python, analytics, tutorial, guide
|
||||
keywords: Ultralytics, YOLO26, data visualization, line graphs, bar plots, pie charts, Python, analytics, tutorial, guide
|
||||
---
|
||||
|
||||
# Analytics using Ultralytics YOLO11
|
||||
# Analytics using Ultralytics YOLO26
|
||||
|
||||
## Introduction
|
||||
|
||||
|
|
@ -76,7 +76,7 @@ This guide provides a comprehensive overview of three fundamental types of [data
|
|||
analytics = solutions.Analytics(
|
||||
show=True, # display the output
|
||||
analytics_type="line", # pass the analytics type, could be "pie", "bar" or "area".
|
||||
model="yolo11n.pt", # path to the YOLO11 model file
|
||||
model="yolo26n.pt", # path to the YOLO26 model file
|
||||
# classes=[0, 2], # display analytics for specific detection classes
|
||||
)
|
||||
|
||||
|
|
@ -118,15 +118,15 @@ Additionally, the following visualization arguments are supported:
|
|||
|
||||
## Conclusion
|
||||
|
||||
Understanding when and how to use different types of visualizations is crucial for effective data analysis. Line graphs, bar plots, and pie charts are fundamental tools that can help you convey your data's story more clearly and effectively. The Ultralytics YOLO11 Analytics solution provides a streamlined way to generate these visualizations from your [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking results, making it easier to extract meaningful insights from your visual data.
|
||||
Understanding when and how to use different types of visualizations is crucial for effective data analysis. Line graphs, bar plots, and pie charts are fundamental tools that can help you convey your data's story more clearly and effectively. The Ultralytics YOLO26 Analytics solution provides a streamlined way to generate these visualizations from your [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking results, making it easier to extract meaningful insights from your visual data.
|
||||
|
||||
## FAQ
|
||||
|
||||
### How do I create a line graph using Ultralytics YOLO11 Analytics?
|
||||
### How do I create a line graph using Ultralytics YOLO26 Analytics?
|
||||
|
||||
To create a line graph using Ultralytics YOLO11 Analytics, follow these steps:
|
||||
To create a line graph using Ultralytics YOLO26 Analytics, follow these steps:
|
||||
|
||||
1. Load a YOLO11 model and open your video file.
|
||||
1. Load a YOLO26 model and open your video file.
|
||||
2. Initialize the `Analytics` class with the type set to "line."
|
||||
3. Iterate through video frames, updating the line graph with relevant data, such as object counts per frame.
|
||||
4. Save the output video displaying the line graph.
|
||||
|
|
@ -170,11 +170,11 @@ out.release()
|
|||
cv2.destroyAllWindows()
|
||||
```
|
||||
|
||||
For further details on configuring the `Analytics` class, visit the [Analytics using Ultralytics YOLO11](#analytics-using-ultralytics-yolo11) section.
|
||||
For further details on configuring the `Analytics` class, visit the [Analytics using Ultralytics YOLO26](#analytics-using-ultralytics-yolo26) section.
|
||||
|
||||
### What are the benefits of using Ultralytics YOLO11 for creating bar plots?
|
||||
### What are the benefits of using Ultralytics YOLO26 for creating bar plots?
|
||||
|
||||
Using Ultralytics YOLO11 for creating bar plots offers several benefits:
|
||||
Using Ultralytics YOLO26 for creating bar plots offers several benefits:
|
||||
|
||||
1. **Real-time Data Visualization**: Seamlessly integrate [object detection](https://www.ultralytics.com/glossary/object-detection) results into bar plots for dynamic updates.
|
||||
2. **Ease of Use**: Simple API and functions make it straightforward to implement and visualize data.
|
||||
|
|
@ -222,9 +222,9 @@ cv2.destroyAllWindows()
|
|||
|
||||
To learn more, visit the [Bar Plot](#visual-samples) section in the guide.
|
||||
|
||||
### Why should I use Ultralytics YOLO11 for creating pie charts in my data visualization projects?
|
||||
### Why should I use Ultralytics YOLO26 for creating pie charts in my data visualization projects?
|
||||
|
||||
Ultralytics YOLO11 is an excellent choice for creating pie charts because:
|
||||
Ultralytics YOLO26 is an excellent choice for creating pie charts because:
|
||||
|
||||
1. **Integration with Object Detection**: Directly integrate object detection results into pie charts for immediate insights.
|
||||
2. **User-Friendly API**: Simple to set up and use with minimal code.
|
||||
|
|
@ -272,9 +272,9 @@ cv2.destroyAllWindows()
|
|||
|
||||
For more information, refer to the [Pie Chart](#visual-samples) section in the guide.
|
||||
|
||||
### Can Ultralytics YOLO11 be used to track objects and dynamically update visualizations?
|
||||
### Can Ultralytics YOLO26 be used to track objects and dynamically update visualizations?
|
||||
|
||||
Yes, Ultralytics YOLO11 can be used to track objects and dynamically update visualizations. It supports tracking multiple objects in real-time and can update various visualizations like line graphs, bar plots, and pie charts based on the tracked objects' data.
|
||||
Yes, Ultralytics YOLO26 can be used to track objects and dynamically update visualizations. It supports tracking multiple objects in real-time and can update various visualizations like line graphs, bar plots, and pie charts based on the tracked objects' data.
|
||||
|
||||
Example for tracking and updating a line graph:
|
||||
|
||||
|
|
@ -317,11 +317,11 @@ cv2.destroyAllWindows()
|
|||
|
||||
To learn about the complete functionality, see the [Tracking](../modes/track.md) section.
|
||||
|
||||
### What makes Ultralytics YOLO11 different from other object detection solutions like [OpenCV](https://www.ultralytics.com/glossary/opencv) and [TensorFlow](https://www.ultralytics.com/glossary/tensorflow)?
|
||||
### What makes Ultralytics YOLO26 different from other object detection solutions like [OpenCV](https://www.ultralytics.com/glossary/opencv) and [TensorFlow](https://www.ultralytics.com/glossary/tensorflow)?
|
||||
|
||||
Ultralytics YOLO11 stands out from other object detection solutions like OpenCV and TensorFlow for multiple reasons:
|
||||
Ultralytics YOLO26 stands out from other object detection solutions like OpenCV and TensorFlow for multiple reasons:
|
||||
|
||||
1. **State-of-the-art [Accuracy](https://www.ultralytics.com/glossary/accuracy)**: YOLO11 provides superior accuracy in object detection, segmentation, and classification tasks.
|
||||
1. **State-of-the-art [Accuracy](https://www.ultralytics.com/glossary/accuracy)**: YOLO26 provides superior accuracy in object detection, segmentation, and classification tasks.
|
||||
2. **Ease of Use**: User-friendly API allows for quick implementation and integration without extensive coding.
|
||||
3. **Real-time Performance**: Optimized for high-speed inference, suitable for real-time applications.
|
||||
4. **Diverse Applications**: Supports various tasks including multi-object tracking, custom model training, and exporting to different formats like ONNX, TensorRT, and CoreML.
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn how to run YOLO11 on AzureML. Quickstart instructions for terminal and notebooks to harness Azure's cloud computing for efficient model training.
|
||||
keywords: YOLO11, AzureML, machine learning, cloud computing, quickstart, terminal, notebooks, model training, Python SDK, AI, Ultralytics
|
||||
description: Learn how to run YOLO26 on AzureML. Quickstart instructions for terminal and notebooks to harness Azure's cloud computing for efficient model training.
|
||||
keywords: YOLO26, AzureML, machine learning, cloud computing, quickstart, terminal, notebooks, model training, Python SDK, AI, Ultralytics
|
||||
---
|
||||
|
||||
# YOLO11 🚀 on AzureML
|
||||
# YOLO26 🚀 on AzureML
|
||||
|
||||
## What is Azure?
|
||||
|
||||
|
|
@ -22,7 +22,7 @@ For users of YOLO (You Only Look Once), AzureML provides a robust, scalable, and
|
|||
- Utilize built-in tools for data preprocessing, feature selection, and model training.
|
||||
- Collaborate more efficiently with capabilities for MLOps (Machine Learning Operations), including but not limited to monitoring, auditing, and versioning of models and data.
|
||||
|
||||
In the subsequent sections, you will find a quickstart guide detailing how to run YOLO11 object detection models using AzureML, either from a compute terminal or a notebook.
|
||||
In the subsequent sections, you will find a quickstart guide detailing how to run YOLO26 object detection models using AzureML, either from a compute terminal or a notebook.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
|
|
@ -49,8 +49,8 @@ Start your compute and open a Terminal:
|
|||
Create a conda virtual environment with your preferred Python version and install pip in it. Python 3.13.1 currently has dependency issues in AzureML, so use Python 3.12 instead.
|
||||
|
||||
```bash
|
||||
conda create --name yolo11env -y python=3.12
|
||||
conda activate yolo11env
|
||||
conda create --name yolo26env -y python=3.12
|
||||
conda activate yolo26env
|
||||
conda install pip -y
|
||||
```
|
||||
|
||||
|
|
@ -63,18 +63,18 @@ pip install ultralytics
|
|||
pip install onnx
|
||||
```
|
||||
|
||||
### Perform YOLO11 tasks
|
||||
### Perform YOLO26 tasks
|
||||
|
||||
Predict:
|
||||
|
||||
```bash
|
||||
yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
|
||||
yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'
|
||||
```
|
||||
|
||||
Train a detection model for 10 [epochs](https://www.ultralytics.com/glossary/epoch) with an initial learning_rate of 0.01:
|
||||
|
||||
```bash
|
||||
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
|
||||
yolo train data=coco8.yaml model=yolo26n.pt epochs=10 lr0=0.01
|
||||
```
|
||||
|
||||
You can find more [instructions to use the Ultralytics CLI here](../quickstart.md#use-ultralytics-with-cli).
|
||||
|
|
@ -92,11 +92,11 @@ Open the compute Terminal.
|
|||
From your compute terminal, create a new ipykernel using Python 3.12 that will be used by your notebook to manage dependencies:
|
||||
|
||||
```bash
|
||||
conda create --name yolo11env -y python=3.12
|
||||
conda activate yolo11env
|
||||
conda create --name yolo26env -y python=3.12
|
||||
conda activate yolo26env
|
||||
conda install pip -y
|
||||
conda install ipykernel -y
|
||||
python -m ipykernel install --user --name yolo11env --display-name "yolo11env"
|
||||
python -m ipykernel install --user --name yolo26env --display-name "yolo26env"
|
||||
```
|
||||
|
||||
Close your terminal and create a new notebook. From your notebook, select the newly created kernel.
|
||||
|
|
@ -105,21 +105,21 @@ Then open a notebook cell and install the required dependencies:
|
|||
|
||||
```bash
|
||||
%%bash
|
||||
source activate yolo11env
|
||||
source activate yolo26env
|
||||
cd ultralytics
|
||||
pip install -r requirements.txt
|
||||
pip install ultralytics
|
||||
pip install onnx
|
||||
```
|
||||
|
||||
Note that you need to run `source activate yolo11env` in every `%%bash` cell to ensure the cell uses the intended environment.
|
||||
Note that you need to run `source activate yolo26env` in every `%%bash` cell to ensure the cell uses the intended environment.
|
||||
|
||||
Run some predictions using the [Ultralytics CLI](../quickstart.md#use-ultralytics-with-cli):
|
||||
|
||||
```bash
|
||||
%%bash
|
||||
source activate yolo11env
|
||||
yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
|
||||
source activate yolo26env
|
||||
yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'
|
||||
```
|
||||
|
||||
Or with the [Ultralytics Python interface](../quickstart.md#use-ultralytics-with-python), for example to train the model:
|
||||
|
|
@ -128,7 +128,7 @@ Or with the [Ultralytics Python interface](../quickstart.md#use-ultralytics-with
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n.pt") # load an official YOLO11n model
|
||||
model = YOLO("yolo26n.pt") # load an official YOLO26n model
|
||||
|
||||
# Use the model
|
||||
model.train(data="coco8.yaml", epochs=3) # train the model
|
||||
|
|
@ -137,47 +137,47 @@ results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
|
|||
path = model.export(format="onnx") # export the model to ONNX format
|
||||
```
|
||||
|
||||
You can use either the Ultralytics CLI or Python interface for running YOLO11 tasks, as described in the terminal section above.
|
||||
You can use either the Ultralytics CLI or Python interface for running YOLO26 tasks, as described in the terminal section above.
|
||||
|
||||
By following these steps, you should be able to get YOLO11 running quickly on AzureML for quick trials. For more advanced uses, you may refer to the full AzureML documentation linked at the beginning of this guide.
|
||||
By following these steps, you should be able to get YOLO26 running quickly on AzureML for quick trials. For more advanced uses, you may refer to the full AzureML documentation linked at the beginning of this guide.
|
||||
|
||||
## Explore More with AzureML
|
||||
|
||||
This guide serves as an introduction to get you up and running with YOLO11 on AzureML. However, it only scratches the surface of what AzureML can offer. To delve deeper and unlock the full potential of AzureML for your machine learning projects, consider exploring the following resources:
|
||||
This guide serves as an introduction to get you up and running with YOLO26 on AzureML. However, it only scratches the surface of what AzureML can offer. To delve deeper and unlock the full potential of AzureML for your machine learning projects, consider exploring the following resources:
|
||||
|
||||
- [Create a Data Asset](https://learn.microsoft.com/azure/machine-learning/how-to-create-data-assets): Learn how to set up and manage your data assets effectively within the AzureML environment.
|
||||
- [Initiate an AzureML Job](https://learn.microsoft.com/azure/machine-learning/how-to-train-model): Get a comprehensive understanding of how to kickstart your machine learning training jobs on AzureML.
|
||||
- [Register a Model](https://learn.microsoft.com/azure/machine-learning/how-to-manage-models): Familiarize yourself with model management practices including registration, versioning, and deployment.
|
||||
- [Train YOLO11 with AzureML Python SDK](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azure-machine-learning-python-sdk-8268696be8ba): Explore a step-by-step guide on using the AzureML Python SDK to train your YOLO11 models.
|
||||
- [Train YOLO11 with AzureML CLI](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azureml-and-the-az-cli-73d3c870ba8e): Discover how to utilize the command-line interface for streamlined training and management of YOLO11 models on AzureML.
|
||||
- [Train YOLO26 with AzureML Python SDK](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azure-machine-learning-python-sdk-8268696be8ba): Explore a step-by-step guide on using the AzureML Python SDK to train your YOLO26 models.
|
||||
- [Train YOLO26 with AzureML CLI](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azureml-and-the-az-cli-73d3c870ba8e): Discover how to utilize the command-line interface for streamlined training and management of YOLO26 models on AzureML.
|
||||
|
||||
## FAQ
|
||||
|
||||
### How do I run YOLO11 on AzureML for model training?
|
||||
### How do I run YOLO26 on AzureML for model training?
|
||||
|
||||
Running YOLO11 on AzureML for model training involves several steps:
|
||||
Running YOLO26 on AzureML for model training involves several steps:
|
||||
|
||||
1. **Create a Compute Instance**: From your AzureML workspace, navigate to Compute > Compute instances > New, and select the required instance.
|
||||
|
||||
2. **Set Up the Environment**: Start your compute instance, open a terminal, and create a Conda environment. Set your Python version (Python 3.13.1 is not supported yet):
|
||||
|
||||
```bash
|
||||
conda create --name yolo11env -y python=3.12
|
||||
conda activate yolo11env
|
||||
conda create --name yolo26env -y python=3.12
|
||||
conda activate yolo26env
|
||||
conda install pip -y
|
||||
pip install ultralytics onnx
|
||||
```
|
||||
|
||||
3. **Run YOLO11 Tasks**: Use the Ultralytics CLI to train your model:
|
||||
3. **Run YOLO26 Tasks**: Use the Ultralytics CLI to train your model:
|
||||
```bash
|
||||
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
|
||||
yolo train data=coco8.yaml model=yolo26n.pt epochs=10 lr0=0.01
|
||||
```
|
||||
|
||||
For more details, you can refer to the [instructions to use the Ultralytics CLI](../quickstart.md#use-ultralytics-with-cli).
|
||||
|
||||
### What are the benefits of using AzureML for YOLO11 training?
|
||||
### What are the benefits of using AzureML for YOLO26 training?
|
||||
|
||||
AzureML provides a robust and efficient ecosystem for training YOLO11 models:
|
||||
AzureML provides a robust and efficient ecosystem for training YOLO26 models:
|
||||
|
||||
- **Scalability**: Easily scale your compute resources as your data and model complexity grows.
|
||||
- **MLOps Integration**: Utilize features like versioning, monitoring, and auditing to streamline ML operations.
|
||||
|
|
@ -185,9 +185,9 @@ AzureML provides a robust and efficient ecosystem for training YOLO11 models:
|
|||
|
||||
These advantages make AzureML an ideal platform for projects ranging from quick prototypes to large-scale deployments. For more tips, check out [AzureML Jobs](https://learn.microsoft.com/azure/machine-learning/how-to-train-model).
|
||||
|
||||
### How do I troubleshoot common issues when running YOLO11 on AzureML?
|
||||
### How do I troubleshoot common issues when running YOLO26 on AzureML?
|
||||
|
||||
Troubleshooting common issues with YOLO11 on AzureML can involve the following steps:
|
||||
Troubleshooting common issues with YOLO26 on AzureML can involve the following steps:
|
||||
|
||||
- **Dependency Issues**: Ensure all required packages are installed. Refer to the `requirements.txt` file for dependencies.
|
||||
- **Environment Setup**: Verify that your conda environment is correctly activated before running commands.
|
||||
|
|
@ -202,7 +202,7 @@ Yes, AzureML allows you to use both the Ultralytics CLI and the Python interface
|
|||
- **CLI**: Ideal for quick tasks and running standard scripts directly from the terminal.
|
||||
|
||||
```bash
|
||||
yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
|
||||
yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'
|
||||
```
|
||||
|
||||
- **Python Interface**: Useful for more complex tasks requiring custom coding and integration within notebooks.
|
||||
|
|
@ -210,18 +210,18 @@ Yes, AzureML allows you to use both the Ultralytics CLI and the Python interface
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
model.train(data="coco8.yaml", epochs=3)
|
||||
```
|
||||
|
||||
For step-by-step instructions, refer to the [CLI quickstart guide](../quickstart.md#use-ultralytics-with-cli) and the [Python quickstart guide](../quickstart.md#use-ultralytics-with-python).
|
||||
|
||||
### What is the advantage of using Ultralytics YOLO11 over other [object detection](https://www.ultralytics.com/glossary/object-detection) models?
|
||||
### What is the advantage of using Ultralytics YOLO26 over other [object detection](https://www.ultralytics.com/glossary/object-detection) models?
|
||||
|
||||
Ultralytics YOLO11 offers several unique advantages over competing object detection models:
|
||||
Ultralytics YOLO26 offers several unique advantages over competing object detection models:
|
||||
|
||||
- **Speed**: Faster inference and training times compared to models like Faster R-CNN and SSD.
|
||||
- **[Accuracy](https://www.ultralytics.com/glossary/accuracy)**: High accuracy in detection tasks with features like anchor-free design and enhanced augmentation strategies.
|
||||
- **Ease of Use**: Intuitive API and CLI for quick setup, making it accessible both to beginners and experts.
|
||||
|
||||
To explore more about YOLO11's features, visit the [Ultralytics YOLO](https://www.ultralytics.com/yolo) page for detailed insights.
|
||||
To explore more about YOLO26's features, visit the [Ultralytics YOLO](https://www.ultralytics.com/yolo) page for detailed insights.
|
||||
|
|
|
|||
|
|
@ -73,7 +73,7 @@ With Ultralytics installed, you can now start using its robust features for [obj
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo11n.pt") # initialize model
|
||||
model = YOLO("yolo26n.pt") # initialize model
|
||||
results = model("path/to/image.jpg") # perform inference
|
||||
results[0].show() # display results for the first image
|
||||
```
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn how to boost your Raspberry Pi's ML performance using Coral Edge TPU with Ultralytics YOLO11. Follow our detailed setup and installation guide.
|
||||
keywords: Coral Edge TPU, Raspberry Pi, YOLO11, Ultralytics, TensorFlow Lite, ML inference, machine learning, AI, installation guide, setup tutorial
|
||||
description: Learn how to boost your Raspberry Pi's ML performance using Coral Edge TPU with Ultralytics YOLO26. Follow our detailed setup and installation guide.
|
||||
keywords: Coral Edge TPU, Raspberry Pi, YOLO26, Ultralytics, TensorFlow Lite, ML inference, machine learning, AI, installation guide, setup tutorial
|
||||
---
|
||||
|
||||
# Coral Edge TPU on a Raspberry Pi with Ultralytics YOLO11 🚀
|
||||
# Coral Edge TPU on a Raspberry Pi with Ultralytics YOLO26 🚀
|
||||
|
||||
<p align="center">
|
||||
<img width="800" src="https://github.com/ultralytics/docs/releases/download/0/edge-tpu-usb-accelerator-and-pi.avif" alt="Raspberry Pi single board computer with USB Edge TPU accelerator">
|
||||
|
|
@ -84,7 +84,7 @@ After installing the runtime, plug your Coral Edge TPU into a USB 3.0 port on th
|
|||
|
||||
## Export to Edge TPU
|
||||
|
||||
To use the Edge TPU, you need to convert your model into a compatible format. It is recommended that you run export on Google Colab, x86_64 Linux machine, using the official [Ultralytics Docker container](docker-quickstart.md), or using [Ultralytics HUB](../hub/quickstart.md), since the Edge TPU compiler is not available on ARM. See the [Export Mode](../modes/export.md) for the available arguments.
|
||||
To use the Edge TPU, you need to convert your model into a compatible format. It is recommended that you run export on Google Colab, x86_64 Linux machine, using the official [Ultralytics Docker container](docker-quickstart.md), or using [Ultralytics Platform](../platform/quickstart.md), since the Edge TPU compiler is not available on ARM. See the [Export Mode](../modes/export.md) for the available arguments.
|
||||
|
||||
!!! example "Exporting the model"
|
||||
|
||||
|
|
@ -204,9 +204,9 @@ Find comprehensive information on the [Predict](../modes/predict.md) page for fu
|
|||
|
||||
## FAQ
|
||||
|
||||
### What is a Coral Edge TPU and how does it enhance Raspberry Pi's performance with Ultralytics YOLO11?
|
||||
### What is a Coral Edge TPU and how does it enhance Raspberry Pi's performance with Ultralytics YOLO26?
|
||||
|
||||
The Coral Edge TPU is a compact device designed to add an Edge TPU coprocessor to your system. This coprocessor enables low-power, high-performance [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) inference, particularly optimized for TensorFlow Lite models. When using a Raspberry Pi, the Edge TPU accelerates ML model inference, significantly boosting performance, especially for Ultralytics YOLO11 models. You can read more about the Coral Edge TPU on their [home page](https://developers.google.com/coral).
|
||||
The Coral Edge TPU is a compact device designed to add an Edge TPU coprocessor to your system. This coprocessor enables low-power, high-performance [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) inference, particularly optimized for TensorFlow Lite models. When using a Raspberry Pi, the Edge TPU accelerates ML model inference, significantly boosting performance, especially for Ultralytics YOLO26 models. You can read more about the Coral Edge TPU on their [home page](https://developers.google.com/coral).
|
||||
|
||||
### How do I install the Coral Edge TPU runtime on a Raspberry Pi?
|
||||
|
||||
|
|
@ -218,9 +218,9 @@ sudo dpkg -i path/to/package.deb
|
|||
|
||||
Make sure to uninstall any previous Coral Edge TPU runtime versions by following the steps outlined in the [Installation Walkthrough](#installation-walkthrough) section.
|
||||
|
||||
### Can I export my Ultralytics YOLO11 model to be compatible with Coral Edge TPU?
|
||||
### Can I export my Ultralytics YOLO26 model to be compatible with Coral Edge TPU?
|
||||
|
||||
Yes, you can export your Ultralytics YOLO11 model to be compatible with the Coral Edge TPU. It is recommended to perform the export on Google Colab, an x86_64 Linux machine, or using the [Ultralytics Docker container](docker-quickstart.md). You can also use [Ultralytics HUB](../hub/quickstart.md) for exporting. Here is how you can export your model using Python and CLI:
|
||||
Yes, you can export your Ultralytics YOLO26 model to be compatible with the Coral Edge TPU. It is recommended to perform the export on Google Colab, an x86_64 Linux machine, or using the [Ultralytics Docker container](docker-quickstart.md). You can also use [Ultralytics Platform](../platform/quickstart.md) for exporting. Here is how you can export your model using Python and CLI:
|
||||
|
||||
!!! example "Exporting the model"
|
||||
|
||||
|
|
@ -260,9 +260,9 @@ pip install -U tflite-runtime
|
|||
|
||||
For detailed instructions, refer to the [Running the Model](#running-the-model) section.
|
||||
|
||||
### How do I run inference with an exported YOLO11 model on a Raspberry Pi using the Coral Edge TPU?
|
||||
### How do I run inference with an exported YOLO26 model on a Raspberry Pi using the Coral Edge TPU?
|
||||
|
||||
After exporting your YOLO11 model to an Edge TPU-compatible format, you can run inference using the following code snippets:
|
||||
After exporting your YOLO26 model to an Edge TPU-compatible format, you can run inference using the following code snippets:
|
||||
|
||||
!!! example "Running the model"
|
||||
|
||||
|
|
|
|||
|
|
@ -160,12 +160,12 @@ Bouncing your ideas and queries off other [computer vision](https://www.ultralyt
|
|||
|
||||
### Where to Find Help and Support
|
||||
|
||||
- **GitHub Issues:** Visit the YOLO11 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers are there to help with any issues you face.
|
||||
- **GitHub Issues:** Visit the YOLO26 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers are there to help with any issues you face.
|
||||
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to connect with other users and developers, get support, share knowledge, and brainstorm ideas.
|
||||
|
||||
### Official Documentation
|
||||
|
||||
- **Ultralytics YOLO11 Documentation:** Refer to the [official YOLO11 documentation](./index.md) for thorough guides and valuable insights on numerous computer vision tasks and projects.
|
||||
- **Ultralytics YOLO26 Documentation:** Refer to the [official YOLO26 documentation](./index.md) for thorough guides and valuable insights on numerous computer vision tasks and projects.
|
||||
|
||||
## Conclusion
|
||||
|
||||
|
|
@ -183,7 +183,7 @@ Ensuring high consistency and accuracy in data annotation involves establishing
|
|||
|
||||
### How many images do I need for training Ultralytics YOLO models?
|
||||
|
||||
For effective [transfer learning](https://www.ultralytics.com/glossary/transfer-learning) and object detection with Ultralytics YOLO models, start with a minimum of a few hundred annotated objects per class. If training for just one class, begin with at least 100 annotated images and train for approximately 100 [epochs](https://www.ultralytics.com/glossary/epoch). More complex tasks might require thousands of images per class to achieve high reliability and performance. Quality annotations are crucial, so ensure your data collection and annotation processes are rigorous and aligned with your project's specific goals. Explore detailed training strategies in the [YOLO11 training guide](../modes/train.md).
|
||||
For effective [transfer learning](https://www.ultralytics.com/glossary/transfer-learning) and object detection with Ultralytics YOLO models, start with a minimum of a few hundred annotated objects per class. If training for just one class, begin with at least 100 annotated images and train for approximately 100 [epochs](https://www.ultralytics.com/glossary/epoch). More complex tasks might require thousands of images per class to achieve high reliability and performance. Quality annotations are crucial, so ensure your data collection and annotation processes are rigorous and aligned with your project's specific goals. Explore detailed training strategies in the [YOLO26 training guide](../modes/train.md).
|
||||
|
||||
### What are some popular tools for data annotation?
|
||||
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn how to deploy Ultralytics YOLO11 on NVIDIA Jetson devices using TensorRT and DeepStream SDK. Explore performance benchmarks and maximize AI capabilities.
|
||||
keywords: Ultralytics, YOLO11, NVIDIA Jetson, JetPack, AI deployment, embedded systems, deep learning, TensorRT, DeepStream SDK, computer vision
|
||||
description: Learn how to deploy Ultralytics YOLO26 on NVIDIA Jetson devices using TensorRT and DeepStream SDK. Explore performance benchmarks and maximize AI capabilities.
|
||||
keywords: Ultralytics, YOLO26, NVIDIA Jetson, JetPack, AI deployment, embedded systems, deep learning, TensorRT, DeepStream SDK, computer vision
|
||||
---
|
||||
|
||||
# Ultralytics YOLO11 on NVIDIA Jetson using DeepStream SDK and TensorRT
|
||||
# Ultralytics YOLO26 on NVIDIA Jetson using DeepStream SDK and TensorRT
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
|
|
@ -14,10 +14,10 @@ keywords: Ultralytics, YOLO11, NVIDIA Jetson, JetPack, AI deployment, embedded s
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to use Ultralytics YOLO11 models with NVIDIA Deepstream on Jetson Orin NX 🚀
|
||||
<strong>Watch:</strong> How to use Ultralytics YOLO26 models with NVIDIA Deepstream on Jetson Orin NX 🚀
|
||||
</p>
|
||||
|
||||
This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLO11 on [NVIDIA Jetson](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) devices using DeepStream SDK and TensorRT. Here we use TensorRT to maximize the inference performance on the Jetson platform.
|
||||
This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLO26 on [NVIDIA Jetson](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) devices using DeepStream SDK and TensorRT. Here we use TensorRT to maximize the inference performance on the Jetson platform.
|
||||
|
||||
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/deepstream-nvidia-jetson.avif" alt="DeepStream on NVIDIA Jetson">
|
||||
|
||||
|
|
@ -34,7 +34,7 @@ This comprehensive guide provides a detailed walkthrough for deploying Ultralyti
|
|||
|
||||
Before you start to follow this guide:
|
||||
|
||||
- Visit our documentation, [Quick Start Guide: NVIDIA Jetson with Ultralytics YOLO11](nvidia-jetson.md) to set up your NVIDIA Jetson device with Ultralytics YOLO11
|
||||
- Visit our documentation, [Quick Start Guide: NVIDIA Jetson with Ultralytics YOLO26](nvidia-jetson.md) to set up your NVIDIA Jetson device with Ultralytics YOLO26
|
||||
- Install [DeepStream SDK](https://developer.nvidia.com/deepstream-getting-started) according to the JetPack version
|
||||
- For JetPack 4.6.4, install [DeepStream 6.0.1](https://docs.nvidia.com/metropolis/deepstream/6.0.1/dev-guide/text/DS_Quickstart.html)
|
||||
- For JetPack 5.1.3, install [DeepStream 6.3](https://docs.nvidia.com/metropolis/deepstream/6.3/dev-guide/text/DS_Quickstart.html)
|
||||
|
|
@ -44,7 +44,7 @@ Before you start to follow this guide:
|
|||
|
||||
In this guide we have used the Debian package method of installing DeepStream SDK to the Jetson device. You can also visit the [DeepStream SDK on Jetson (Archived)](https://developer.nvidia.com/embedded/deepstream-on-jetson-downloads-archived) to access legacy versions of DeepStream.
|
||||
|
||||
## DeepStream Configuration for YOLO11
|
||||
## DeepStream Configuration for YOLO26
|
||||
|
||||
Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) GitHub repository which includes NVIDIA DeepStream SDK support for YOLO models. We appreciate the efforts of marcoslucianops for his contributions!
|
||||
|
||||
|
|
@ -65,27 +65,27 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
|
|||
git clone https://github.com/marcoslucianops/DeepStream-Yolo
|
||||
```
|
||||
|
||||
3. Copy the `export_yolo11.py` file from `DeepStream-Yolo/utils` directory to the `ultralytics` folder
|
||||
3. Copy the `export_yolo26.py` file from `DeepStream-Yolo/utils` directory to the `ultralytics` folder
|
||||
|
||||
```bash
|
||||
cp ~/DeepStream-Yolo/utils/export_yolo11.py ~/ultralytics
|
||||
cp ~/DeepStream-Yolo/utils/export_yolo26.py ~/ultralytics
|
||||
cd ultralytics
|
||||
```
|
||||
|
||||
4. Download Ultralytics YOLO11 detection model (.pt) of your choice from [YOLO11 releases](https://github.com/ultralytics/assets/releases). Here we use [yolo11s.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt).
|
||||
4. Download Ultralytics YOLO26 detection model (.pt) of your choice from [YOLO26 releases](https://github.com/ultralytics/assets/releases). Here we use [yolo26s.pt](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s.pt).
|
||||
|
||||
```bash
|
||||
wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt
|
||||
wget https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s.pt
|
||||
```
|
||||
|
||||
!!! note
|
||||
|
||||
You can also use a [custom-trained YOLO11 model](https://docs.ultralytics.com/modes/train/).
|
||||
You can also use a [custom-trained YOLO26 model](https://docs.ultralytics.com/modes/train/).
|
||||
|
||||
5. Convert model to ONNX
|
||||
|
||||
```bash
|
||||
python3 export_yolo11.py -w yolo11s.pt
|
||||
python3 export_yolo26.py -w yolo26s.pt
|
||||
```
|
||||
|
||||
!!! note "Pass the below arguments to the above command"
|
||||
|
|
@ -134,7 +134,7 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
|
|||
6. Copy the generated `.onnx` model file and `labels.txt` file to the `DeepStream-Yolo` folder
|
||||
|
||||
```bash
|
||||
cp yolo11s.pt.onnx labels.txt ~/DeepStream-Yolo
|
||||
cp yolo26s.pt.onnx labels.txt ~/DeepStream-Yolo
|
||||
cd ~/DeepStream-Yolo
|
||||
```
|
||||
|
||||
|
|
@ -164,12 +164,12 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
|
|||
make -C nvdsinfer_custom_impl_Yolo clean && make -C nvdsinfer_custom_impl_Yolo
|
||||
```
|
||||
|
||||
9. Edit the `config_infer_primary_yolo11.txt` file according to your model (for YOLO11s with 80 classes)
|
||||
9. Edit the `config_infer_primary_yolo26.txt` file according to your model (for YOLO26s with 80 classes)
|
||||
|
||||
```bash
|
||||
[property]
|
||||
...
|
||||
onnx-file=yolo11s.pt.onnx
|
||||
onnx-file=yolo26s.pt.onnx
|
||||
...
|
||||
num-detected-classes=80
|
||||
...
|
||||
|
|
@ -181,7 +181,7 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
|
|||
...
|
||||
[primary-gie]
|
||||
...
|
||||
config-file=config_infer_primary_yolo11.txt
|
||||
config-file=config_infer_primary_yolo26.txt
|
||||
```
|
||||
|
||||
11. You can also change the video source in `deepstream_app_config` file. Here, a default video file is loaded
|
||||
|
|
@ -203,11 +203,11 @@ deepstream-app -c deepstream_app_config.txt
|
|||
|
||||
It will take a long time to generate the TensorRT engine file before starting the inference. So please be patient.
|
||||
|
||||
<div align=center><img width=1000 src="https://github.com/ultralytics/docs/releases/download/0/yolov8-with-deepstream.avif" alt="YOLO11 with deepstream"></div>
|
||||
<div align=center><img width=1000 src="https://github.com/ultralytics/docs/releases/download/0/yolov8-with-deepstream.avif" alt="YOLO26 with deepstream"></div>
|
||||
|
||||
!!! tip
|
||||
|
||||
If you want to convert the model to FP16 precision, simply set `model-engine-file=model_b1_gpu0_fp16.engine` and `network-mode=2` inside `config_infer_primary_yolo11.txt`
|
||||
If you want to convert the model to FP16 precision, simply set `model-engine-file=model_b1_gpu0_fp16.engine` and `network-mode=2` inside `config_infer_primary_yolo26.txt`
|
||||
|
||||
## INT8 Calibration
|
||||
|
||||
|
|
@ -266,7 +266,7 @@ If you want to use INT8 precision for inference, you need to follow the steps be
|
|||
|
||||
Higher INT8_CALIB_BATCH_SIZE values will result in more accuracy and faster calibration speed. Set it according to your GPU memory.
|
||||
|
||||
8. Update the `config_infer_primary_yolo11.txt` file
|
||||
8. Update the `config_infer_primary_yolo26.txt` file
|
||||
|
||||
From
|
||||
|
||||
|
|
@ -306,7 +306,7 @@ deepstream-app -c deepstream_app_config.txt
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Run Multiple Streams with DeepStream SDK on Jetson Nano using Ultralytics YOLO11 🎉
|
||||
<strong>Watch:</strong> How to Run Multiple Streams with DeepStream SDK on Jetson Nano using Ultralytics YOLO26 🎉
|
||||
</p>
|
||||
|
||||
To set up multiple streams under a single DeepStream application, make the following changes to the `deepstream_app_config.txt` file:
|
||||
|
|
@ -342,7 +342,7 @@ deepstream-app -c deepstream_app_config.txt
|
|||
|
||||
## Benchmark Results
|
||||
|
||||
The following benchmarks summarizes how YOLO11 models perform at different TensorRT precision levels with an input size of 640x640 on NVIDIA Jetson Orin NX 16GB.
|
||||
The following benchmarks summarizes how YOLO26 models perform at different TensorRT precision levels with an input size of 640x640 on NVIDIA Jetson Orin NX 16GB.
|
||||
|
||||
### Comparison Chart
|
||||
|
||||
|
|
@ -398,36 +398,36 @@ This guide was initially created by our friends at Seeed Studio, Lakshantha and
|
|||
|
||||
## FAQ
|
||||
|
||||
### How do I set up Ultralytics YOLO11 on an NVIDIA Jetson device?
|
||||
### How do I set up Ultralytics YOLO26 on an NVIDIA Jetson device?
|
||||
|
||||
To set up Ultralytics YOLO11 on an [NVIDIA Jetson](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) device, you first need to install the [DeepStream SDK](https://developer.nvidia.com/deepstream-getting-started) compatible with your JetPack version. Follow the step-by-step guide in our [Quick Start Guide](nvidia-jetson.md) to configure your NVIDIA Jetson for YOLO11 deployment.
|
||||
To set up Ultralytics YOLO26 on an [NVIDIA Jetson](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) device, you first need to install the [DeepStream SDK](https://developer.nvidia.com/deepstream-getting-started) compatible with your JetPack version. Follow the step-by-step guide in our [Quick Start Guide](nvidia-jetson.md) to configure your NVIDIA Jetson for YOLO26 deployment.
|
||||
|
||||
### What is the benefit of using TensorRT with YOLO11 on NVIDIA Jetson?
|
||||
### What is the benefit of using TensorRT with YOLO26 on NVIDIA Jetson?
|
||||
|
||||
Using TensorRT with YOLO11 optimizes the model for inference, significantly reducing latency and improving throughput on NVIDIA Jetson devices. TensorRT provides high-performance, low-latency [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) inference through layer fusion, precision calibration, and kernel auto-tuning. This leads to faster and more efficient execution, particularly useful for real-time applications like video analytics and autonomous machines.
|
||||
Using TensorRT with YOLO26 optimizes the model for inference, significantly reducing latency and improving throughput on NVIDIA Jetson devices. TensorRT provides high-performance, low-latency [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) inference through layer fusion, precision calibration, and kernel auto-tuning. This leads to faster and more efficient execution, particularly useful for real-time applications like video analytics and autonomous machines.
|
||||
|
||||
### Can I run Ultralytics YOLO11 with DeepStream SDK across different NVIDIA Jetson hardware?
|
||||
### Can I run Ultralytics YOLO26 with DeepStream SDK across different NVIDIA Jetson hardware?
|
||||
|
||||
Yes, the guide for deploying Ultralytics YOLO11 with the DeepStream SDK and TensorRT is compatible across the entire NVIDIA Jetson lineup. This includes devices like the Jetson Orin NX 16GB with [JetPack 5.1.3](https://developer.nvidia.com/embedded/jetpack-sdk-513) and the Jetson Nano 4GB with [JetPack 4.6.4](https://developer.nvidia.com/jetpack-sdk-464). Refer to the section [DeepStream Configuration for YOLO11](#deepstream-configuration-for-yolo11) for detailed steps.
|
||||
Yes, the guide for deploying Ultralytics YOLO26 with the DeepStream SDK and TensorRT is compatible across the entire NVIDIA Jetson lineup. This includes devices like the Jetson Orin NX 16GB with [JetPack 5.1.3](https://developer.nvidia.com/embedded/jetpack-sdk-513) and the Jetson Nano 4GB with [JetPack 4.6.4](https://developer.nvidia.com/jetpack-sdk-464). Refer to the section [DeepStream Configuration for YOLO26](#deepstream-configuration-for-yolo26) for detailed steps.
|
||||
|
||||
### How can I convert a YOLO11 model to ONNX for DeepStream?
|
||||
### How can I convert a YOLO26 model to ONNX for DeepStream?
|
||||
|
||||
To convert a YOLO11 model to ONNX format for deployment with DeepStream, use the `utils/export_yolo11.py` script from the [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) repository.
|
||||
To convert a YOLO26 model to ONNX format for deployment with DeepStream, use the `utils/export_yolo26.py` script from the [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) repository.
|
||||
|
||||
Here's an example command:
|
||||
|
||||
```bash
|
||||
python3 utils/export_yolo11.py -w yolo11s.pt --opset 12 --simplify
|
||||
python3 utils/export_yolo26.py -w yolo26s.pt --opset 12 --simplify
|
||||
```
|
||||
|
||||
For more details on model conversion, check out our [model export section](../modes/export.md).
|
||||
|
||||
### What are the performance benchmarks for YOLO on NVIDIA Jetson Orin NX?
|
||||
|
||||
The performance of YOLO11 models on NVIDIA Jetson Orin NX 16GB varies based on TensorRT precision levels. For example, YOLO11s models achieve:
|
||||
The performance of YOLO26 models on NVIDIA Jetson Orin NX 16GB varies based on TensorRT precision levels. For example, YOLO26s models achieve:
|
||||
|
||||
- **FP32 Precision**: 14.6 ms/im, 68.5 FPS
|
||||
- **FP16 Precision**: 7.94 ms/im, 126 FPS
|
||||
- **INT8 Precision**: 5.95 ms/im, 168 FPS
|
||||
|
||||
These benchmarks underscore the efficiency and capability of using TensorRT-optimized YOLO11 models on NVIDIA Jetson hardware. For further details, see our [Benchmark Results](#benchmark-results) section.
|
||||
These benchmarks underscore the efficiency and capability of using TensorRT-optimized YOLO26 models on NVIDIA Jetson hardware. For further details, see our [Benchmark Results](#benchmark-results) section.
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn how to define clear goals and objectives for your computer vision project with our practical guide. Includes tips on problem statements, measurable objectives, and key decisions.
|
||||
keywords: computer vision, project planning, problem statement, measurable objectives, dataset preparation, model selection, YOLO11, Ultralytics
|
||||
keywords: computer vision, project planning, problem statement, measurable objectives, dataset preparation, model selection, YOLO26, Ultralytics
|
||||
---
|
||||
|
||||
# A Practical Guide for Defining Your Computer Vision Project
|
||||
|
|
@ -41,7 +41,7 @@ Let's walk through an example.
|
|||
Consider a computer vision project where you want to [estimate the speed of vehicles](./speed-estimation.md) on a highway. The core issue is that current speed monitoring methods are inefficient and error-prone due to outdated radar systems and manual processes. The project aims to develop a real-time computer vision system that can replace legacy [speed estimation](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects) systems.
|
||||
|
||||
<p align="center">
|
||||
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/speed-estimation-using-yolov8.avif" alt="Speed Estimation Using YOLO11">
|
||||
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/speed-estimation-using-yolov8.avif" alt="Speed Estimation Using YOLO26">
|
||||
</p>
|
||||
|
||||
Primary users include traffic management authorities and law enforcement, while secondary stakeholders are highway planners and the public benefiting from safer roads. Key requirements involve evaluating budget, time, and personnel, as well as addressing technical needs like high-resolution cameras and real-time data processing. Additionally, regulatory constraints on privacy and [data security](https://www.ultralytics.com/glossary/data-security) must be considered.
|
||||
|
|
@ -94,7 +94,7 @@ The most popular computer vision tasks include [image classification](https://ww
|
|||
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/image-classification-vs-object-detection-vs-image-segmentation.avif" alt="Overview of Computer Vision Tasks">
|
||||
</p>
|
||||
|
||||
For a detailed explanation of various tasks, please take a look at the Ultralytics Docs page on [YOLO11 Tasks](../tasks/index.md).
|
||||
For a detailed explanation of various tasks, please take a look at the Ultralytics Docs page on [YOLO26 Tasks](../tasks/index.md).
|
||||
|
||||
### Can a Pretrained Model Remember Classes It Knew Before Custom Training?
|
||||
|
||||
|
|
@ -123,12 +123,12 @@ Connecting with other computer vision enthusiasts can be incredibly helpful for
|
|||
|
||||
### Community Support Channels
|
||||
|
||||
- **GitHub Issues:** Head over to the YOLO11 GitHub repository. You can use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers can assist with specific problems you encounter.
|
||||
- **GitHub Issues:** Head over to the YOLO26 GitHub repository. You can use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers can assist with specific problems you encounter.
|
||||
- **Ultralytics Discord Server:** Become part of the [Ultralytics Discord server](https://discord.com/invite/ultralytics). Connect with fellow users and developers, seek support, exchange knowledge, and discuss ideas.
|
||||
|
||||
### Comprehensive Guides and Documentation
|
||||
|
||||
- **Ultralytics YOLO11 Documentation:** Explore the [official YOLO11 documentation](./index.md) for in-depth guides and valuable tips on various computer vision tasks and projects.
|
||||
- **Ultralytics YOLO26 Documentation:** Explore the [official YOLO26 documentation](./index.md) for in-depth guides and valuable tips on various computer vision tasks and projects.
|
||||
|
||||
## Conclusion
|
||||
|
||||
|
|
@ -147,11 +147,11 @@ To define a clear problem statement for your Ultralytics computer vision project
|
|||
|
||||
Providing a well-defined problem statement ensures that the project remains focused and aligned with your objectives. For a detailed guide, refer to our [practical guide](#defining-a-clear-problem-statement).
|
||||
|
||||
### Why should I use Ultralytics YOLO11 for speed estimation in my computer vision project?
|
||||
### Why should I use Ultralytics YOLO26 for speed estimation in my computer vision project?
|
||||
|
||||
Ultralytics YOLO11 is ideal for speed estimation because of its real-time object tracking capabilities, high accuracy, and robust performance in detecting and monitoring vehicle speeds. It overcomes inefficiencies and inaccuracies of traditional radar systems by leveraging cutting-edge computer vision technology. Check out our blog on [speed estimation using YOLO11](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects) for more insights and practical examples.
|
||||
Ultralytics YOLO26 is ideal for speed estimation because of its real-time object tracking capabilities, high accuracy, and robust performance in detecting and monitoring vehicle speeds. It overcomes inefficiencies and inaccuracies of traditional radar systems by leveraging cutting-edge computer vision technology. Check out our blog on [speed estimation using YOLO26](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects) for more insights and practical examples.
|
||||
|
||||
### How do I set effective measurable objectives for my computer vision project with Ultralytics YOLO11?
|
||||
### How do I set effective measurable objectives for my computer vision project with Ultralytics YOLO26?
|
||||
|
||||
Set effective and measurable objectives using the SMART criteria:
|
||||
|
||||
|
|
|
|||
|
|
@ -1,14 +1,14 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn how to calculate distances between objects using Ultralytics YOLO11 for accurate spatial positioning and scene understanding.
|
||||
keywords: Ultralytics, YOLO11, distance calculation, computer vision, object tracking, spatial positioning
|
||||
description: Learn how to calculate distances between objects using Ultralytics YOLO26 for accurate spatial positioning and scene understanding.
|
||||
keywords: Ultralytics, YOLO26, distance calculation, computer vision, object tracking, spatial positioning
|
||||
---
|
||||
|
||||
# Distance Calculation using Ultralytics YOLO11
|
||||
# Distance Calculation using Ultralytics YOLO26
|
||||
|
||||
## What is Distance Calculation?
|
||||
|
||||
Measuring the gap between two objects is known as distance calculation within a specified space. In the case of [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics), the [bounding box](https://www.ultralytics.com/glossary/bounding-box) centroid is employed to calculate the distance for bounding boxes highlighted by the user.
|
||||
Measuring the gap between two objects is known as distance calculation within a specified space. In the case of [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics), the [bounding box](https://www.ultralytics.com/glossary/bounding-box) centroid is employed to calculate the distance for bounding boxes highlighted by the user.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
|
|
@ -23,9 +23,9 @@ Measuring the gap between two objects is known as distance calculation within a
|
|||
|
||||
## Visuals
|
||||
|
||||
| Distance Calculation using Ultralytics YOLO11 |
|
||||
| Distance Calculation using Ultralytics YOLO26 |
|
||||
| :---------------------------------------------------------------------------------------------------------------------------: |
|
||||
|  |
|
||||
|  |
|
||||
|
||||
## Advantages of Distance Calculation
|
||||
|
||||
|
|
@ -64,7 +64,7 @@ Measuring the gap between two objects is known as distance calculation within a
|
|||
|
||||
# Initialize distance calculation object
|
||||
distancecalculator = solutions.DistanceCalculation(
|
||||
model="yolo11n.pt", # path to the YOLO11 model file.
|
||||
model="yolo26n.pt", # path to the YOLO26 model file.
|
||||
show=True, # display the output
|
||||
)
|
||||
|
||||
|
|
@ -116,7 +116,7 @@ The implementation uses the `mouse_event_for_distance` method to handle mouse in
|
|||
|
||||
## Applications
|
||||
|
||||
Distance calculation with YOLO11 has numerous practical applications:
|
||||
Distance calculation with YOLO26 has numerous practical applications:
|
||||
|
||||
- **Retail Analytics:** Measure customer proximity to products and analyze store layout effectiveness
|
||||
- **Industrial Safety:** Monitor safe distances between workers and machinery
|
||||
|
|
@ -127,33 +127,33 @@ Distance calculation with YOLO11 has numerous practical applications:
|
|||
|
||||
## FAQ
|
||||
|
||||
### How do I calculate distances between objects using Ultralytics YOLO11?
|
||||
### How do I calculate distances between objects using Ultralytics YOLO26?
|
||||
|
||||
To calculate distances between objects using [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics), you need to identify the bounding box centroids of the detected objects. This process involves initializing the `DistanceCalculation` class from Ultralytics' `solutions` module and using the model's tracking outputs to calculate the distances.
|
||||
To calculate distances between objects using [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics), you need to identify the bounding box centroids of the detected objects. This process involves initializing the `DistanceCalculation` class from Ultralytics' `solutions` module and using the model's tracking outputs to calculate the distances.
|
||||
|
||||
### What are the advantages of using distance calculation with Ultralytics YOLO11?
|
||||
### What are the advantages of using distance calculation with Ultralytics YOLO26?
|
||||
|
||||
Using distance calculation with Ultralytics YOLO11 offers several advantages:
|
||||
Using distance calculation with Ultralytics YOLO26 offers several advantages:
|
||||
|
||||
- **Localization Precision:** Provides accurate spatial positioning for objects.
|
||||
- **Size Estimation:** Helps estimate physical sizes, contributing to better contextual understanding.
|
||||
- **Scene Understanding:** Enhances 3D scene comprehension, aiding improved decision-making in applications like autonomous driving and surveillance.
|
||||
- **Real-time Processing:** Performs calculations on-the-fly, making it suitable for live video analysis.
|
||||
- **Integration Capabilities:** Works seamlessly with other YOLO11 solutions like [object tracking](../modes/track.md) and [speed estimation](speed-estimation.md).
|
||||
- **Integration Capabilities:** Works seamlessly with other YOLO26 solutions like [object tracking](../modes/track.md) and [speed estimation](speed-estimation.md).
|
||||
|
||||
### Can I perform distance calculation in real-time video streams with Ultralytics YOLO11?
|
||||
### Can I perform distance calculation in real-time video streams with Ultralytics YOLO26?
|
||||
|
||||
Yes, you can perform distance calculation in real-time video streams with Ultralytics YOLO11. The process involves capturing video frames using [OpenCV](https://www.ultralytics.com/glossary/opencv), running YOLO11 [object detection](https://www.ultralytics.com/glossary/object-detection), and using the `DistanceCalculation` class to calculate distances between objects in successive frames. For a detailed implementation, see the [video stream example](#distance-calculation-using-ultralytics-yolo11).
|
||||
Yes, you can perform distance calculation in real-time video streams with Ultralytics YOLO26. The process involves capturing video frames using [OpenCV](https://www.ultralytics.com/glossary/opencv), running YOLO26 [object detection](https://www.ultralytics.com/glossary/object-detection), and using the `DistanceCalculation` class to calculate distances between objects in successive frames. For a detailed implementation, see the [video stream example](#distance-calculation-using-ultralytics-yolo26).
|
||||
|
||||
### How do I delete points drawn during distance calculation using Ultralytics YOLO11?
|
||||
### How do I delete points drawn during distance calculation using Ultralytics YOLO26?
|
||||
|
||||
To delete points drawn during distance calculation with Ultralytics YOLO11, you can use a right mouse click. This action will clear all the points you have drawn. For more details, refer to the note section under the [distance calculation example](#distance-calculation-using-ultralytics-yolo11).
|
||||
To delete points drawn during distance calculation with Ultralytics YOLO26, you can use a right mouse click. This action will clear all the points you have drawn. For more details, refer to the note section under the [distance calculation example](#distance-calculation-using-ultralytics-yolo26).
|
||||
|
||||
### What are the key arguments for initializing the DistanceCalculation class in Ultralytics YOLO11?
|
||||
### What are the key arguments for initializing the DistanceCalculation class in Ultralytics YOLO26?
|
||||
|
||||
The key arguments for initializing the `DistanceCalculation` class in Ultralytics YOLO11 include:
|
||||
The key arguments for initializing the `DistanceCalculation` class in Ultralytics YOLO26 include:
|
||||
|
||||
- `model`: Path to the YOLO11 model file.
|
||||
- `model`: Path to the YOLO26 model file.
|
||||
- `tracker`: Tracking algorithm to use (default is 'botsort.yaml').
|
||||
- `conf`: Confidence threshold for detections.
|
||||
- `show`: Flag to display the output.
|
||||
|
|
|
|||
|
|
@ -216,7 +216,7 @@ To persist training outputs:
|
|||
```bash
|
||||
# Recommended: mount workspace and specify project path
|
||||
sudo docker run --rm -it -v "$(pwd)":/w -w /w ultralytics/ultralytics:latest \
|
||||
yolo train model=yolo11n.pt data=coco8.yaml project=/w/runs
|
||||
yolo train model=yolo26n.pt data=coco8.yaml project=/w/runs
|
||||
```
|
||||
|
||||
This saves all training outputs to `./runs` on your host machine.
|
||||
|
|
@ -273,10 +273,10 @@ Setup and configuration of an X11 or Wayland display server is outside the scope
|
|||
|
||||
### Using Docker with a GUI
|
||||
|
||||
Now you can display graphical applications inside your Docker container. For example, you can run the following [CLI command](../usage/cli.md) to visualize the [predictions](../modes/predict.md) from a [YOLO11 model](../models/yolo11.md):
|
||||
Now you can display graphical applications inside your Docker container. For example, you can run the following [CLI command](../usage/cli.md) to visualize the [predictions](../modes/predict.md) from a [YOLO26 model](../models/yolo26.md):
|
||||
|
||||
```bash
|
||||
yolo predict model=yolo11n.pt show=True
|
||||
yolo predict model=yolo26n.pt show=True
|
||||
```
|
||||
|
||||
??? info "Testing"
|
||||
|
|
|
|||
|
|
@ -1,16 +1,16 @@
|
|||
---
|
||||
comments: true
|
||||
description: Transform complex data into insightful heatmaps using Ultralytics YOLO11. Discover patterns, trends, and anomalies with vibrant visualizations.
|
||||
keywords: Ultralytics, YOLO11, heatmaps, data visualization, data analysis, complex data, patterns, trends, anomalies
|
||||
description: Transform complex data into insightful heatmaps using Ultralytics YOLO26. Discover patterns, trends, and anomalies with vibrant visualizations.
|
||||
keywords: Ultralytics, YOLO26, heatmaps, data visualization, data analysis, complex data, patterns, trends, anomalies
|
||||
---
|
||||
|
||||
# Advanced [Data Visualization](https://www.ultralytics.com/glossary/data-visualization): Heatmaps using Ultralytics YOLO11 🚀
|
||||
# Advanced [Data Visualization](https://www.ultralytics.com/glossary/data-visualization): Heatmaps using Ultralytics YOLO26 🚀
|
||||
|
||||
## Introduction to Heatmaps
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-generate-heatmaps-using-ultralytics-yolo.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Heatmaps In Colab"></a>
|
||||
|
||||
A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) transforms complex data into a vibrant, color-coded matrix. This visual tool employs a spectrum of colors to represent varying data values, where warmer hues indicate higher intensities and cooler tones signify lower values. Heatmaps excel in visualizing intricate data patterns, correlations, and anomalies, offering an accessible and engaging approach to data interpretation across diverse domains.
|
||||
A heatmap generated with [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics/) transforms complex data into a vibrant, color-coded matrix. This visual tool employs a spectrum of colors to represent varying data values, where warmer hues indicate higher intensities and cooler tones signify lower values. Heatmaps excel in visualizing intricate data patterns, correlations, and anomalies, offering an accessible and engaging approach to data interpretation across diverse domains.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
|
|
@ -20,7 +20,7 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Heatmaps using Ultralytics YOLO11
|
||||
<strong>Watch:</strong> Heatmaps using Ultralytics YOLO26
|
||||
</p>
|
||||
|
||||
## Why Choose Heatmaps for Data Analysis?
|
||||
|
|
@ -33,8 +33,8 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
|
|||
|
||||
| Transportation | Retail |
|
||||
| :--------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------: |
|
||||
|  |  |
|
||||
| Ultralytics YOLO11 Transportation Heatmap | Ultralytics YOLO11 Retail Heatmap |
|
||||
|  |  |
|
||||
| Ultralytics YOLO26 Transportation Heatmap | Ultralytics YOLO26 Retail Heatmap |
|
||||
|
||||
!!! example "Heatmaps using Ultralytics YOLO"
|
||||
|
||||
|
|
@ -76,7 +76,7 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
|
|||
# Initialize heatmap object
|
||||
heatmap = solutions.Heatmap(
|
||||
show=True, # display the output
|
||||
model="yolo11n.pt", # path to the YOLO11 model file
|
||||
model="yolo26n.pt", # path to the YOLO26 model file
|
||||
colormap=cv2.COLORMAP_PARULA, # colormap of heatmap
|
||||
# region=region_points, # object counting with heatmaps, you can pass region_points
|
||||
# classes=[0, 2], # generate heatmap for specific classes, e.g., person and car.
|
||||
|
|
@ -147,13 +147,13 @@ Additionally, the supported visualization arguments are listed below:
|
|||
|
||||
These colormaps are commonly used for visualizing data with different color representations.
|
||||
|
||||
## How Heatmaps Work in Ultralytics YOLO11
|
||||
## How Heatmaps Work in Ultralytics YOLO26
|
||||
|
||||
The [Heatmap solution](../reference/solutions/heatmap.md) in Ultralytics YOLO11 extends the [ObjectCounter](../reference/solutions/object_counter.md) class to generate and visualize movement patterns in video streams. When initialized, the solution creates a blank heatmap layer that gets updated as objects move through the frame.
|
||||
The [Heatmap solution](../reference/solutions/heatmap.md) in Ultralytics YOLO26 extends the [ObjectCounter](../reference/solutions/object_counter.md) class to generate and visualize movement patterns in video streams. When initialized, the solution creates a blank heatmap layer that gets updated as objects move through the frame.
|
||||
|
||||
For each detected object, the solution:
|
||||
|
||||
1. Tracks the object across frames using YOLO11's tracking capabilities
|
||||
1. Tracks the object across frames using YOLO26's tracking capabilities
|
||||
2. Updates the heatmap intensity at the object's location
|
||||
3. Applies a selected colormap to visualize the intensity values
|
||||
4. Overlays the colored heatmap on the original frame
|
||||
|
|
@ -162,13 +162,13 @@ The result is a dynamic visualization that builds up over time, revealing traffi
|
|||
|
||||
## FAQ
|
||||
|
||||
### How does Ultralytics YOLO11 generate heatmaps and what are their benefits?
|
||||
### How does Ultralytics YOLO26 generate heatmaps and what are their benefits?
|
||||
|
||||
Ultralytics YOLO11 generates heatmaps by transforming complex data into a color-coded matrix where different hues represent data intensities. Heatmaps make it easier to visualize patterns, correlations, and anomalies in the data. Warmer hues indicate higher values, while cooler tones represent lower values. The primary benefits include intuitive visualization of data distribution, efficient pattern detection, and enhanced spatial analysis for decision-making. For more details and configuration options, refer to the [Heatmap Configuration](#heatmap-arguments) section.
|
||||
Ultralytics YOLO26 generates heatmaps by transforming complex data into a color-coded matrix where different hues represent data intensities. Heatmaps make it easier to visualize patterns, correlations, and anomalies in the data. Warmer hues indicate higher values, while cooler tones represent lower values. The primary benefits include intuitive visualization of data distribution, efficient pattern detection, and enhanced spatial analysis for decision-making. For more details and configuration options, refer to the [Heatmap Configuration](#heatmap-arguments) section.
|
||||
|
||||
### Can I use Ultralytics YOLO11 to perform object tracking and generate a heatmap simultaneously?
|
||||
### Can I use Ultralytics YOLO26 to perform object tracking and generate a heatmap simultaneously?
|
||||
|
||||
Yes, Ultralytics YOLO11 supports object tracking and heatmap generation concurrently. This can be achieved through its `Heatmap` solution integrated with object tracking models. To do so, you need to initialize the heatmap object and use YOLO11's tracking capabilities. Here's a simple example:
|
||||
Yes, Ultralytics YOLO26 supports object tracking and heatmap generation concurrently. This can be achieved through its `Heatmap` solution integrated with object tracking models. To do so, you need to initialize the heatmap object and use YOLO26's tracking capabilities. Here's a simple example:
|
||||
|
||||
```python
|
||||
import cv2
|
||||
|
|
@ -176,7 +176,7 @@ import cv2
|
|||
from ultralytics import solutions
|
||||
|
||||
cap = cv2.VideoCapture("path/to/video.mp4")
|
||||
heatmap = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, show=True, model="yolo11n.pt")
|
||||
heatmap = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, show=True, model="yolo26n.pt")
|
||||
|
||||
while cap.isOpened():
|
||||
success, im0 = cap.read()
|
||||
|
|
@ -189,11 +189,11 @@ cv2.destroyAllWindows()
|
|||
|
||||
For further guidance, check the [Tracking Mode](../modes/track.md) page.
|
||||
|
||||
### What makes Ultralytics YOLO11 heatmaps different from other data visualization tools like those from [OpenCV](https://www.ultralytics.com/glossary/opencv) or Matplotlib?
|
||||
### What makes Ultralytics YOLO26 heatmaps different from other data visualization tools like those from [OpenCV](https://www.ultralytics.com/glossary/opencv) or Matplotlib?
|
||||
|
||||
Ultralytics YOLO11 heatmaps are specifically designed for integration with its [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking models, providing an end-to-end solution for real-time data analysis. Unlike generic visualization tools like OpenCV or Matplotlib, YOLO11 heatmaps are optimized for performance and automated processing, supporting features like persistent tracking, decay factor adjustment, and real-time video overlay. For more information on YOLO11's unique features, visit the [Ultralytics YOLO11 Introduction](https://www.ultralytics.com/blog/introducing-ultralytics-yolov8).
|
||||
Ultralytics YOLO26 heatmaps are specifically designed for integration with its [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking models, providing an end-to-end solution for real-time data analysis. Unlike generic visualization tools like OpenCV or Matplotlib, YOLO26 heatmaps are optimized for performance and automated processing, supporting features like persistent tracking, decay factor adjustment, and real-time video overlay. For more information on YOLO26's unique features, visit the [Ultralytics YOLO26 Introduction](https://www.ultralytics.com/blog/introducing-ultralytics-yolov8).
|
||||
|
||||
### How can I visualize only specific object classes in heatmaps using Ultralytics YOLO11?
|
||||
### How can I visualize only specific object classes in heatmaps using Ultralytics YOLO26?
|
||||
|
||||
You can visualize specific object classes by specifying the desired classes in the `track()` method of the YOLO model. For instance, if you only want to visualize cars and persons (assuming their class indices are 0 and 2), you can set the `classes` parameter accordingly.
|
||||
|
||||
|
|
@ -203,7 +203,7 @@ import cv2
|
|||
from ultralytics import solutions
|
||||
|
||||
cap = cv2.VideoCapture("path/to/video.mp4")
|
||||
heatmap = solutions.Heatmap(show=True, model="yolo11n.pt", classes=[0, 2])
|
||||
heatmap = solutions.Heatmap(show=True, model="yolo26n.pt", classes=[0, 2])
|
||||
|
||||
while cap.isOpened():
|
||||
success, im0 = cap.read()
|
||||
|
|
@ -214,6 +214,6 @@ cap.release()
|
|||
cv2.destroyAllWindows()
|
||||
```
|
||||
|
||||
### Why should businesses choose Ultralytics YOLO11 for heatmap generation in data analysis?
|
||||
### Why should businesses choose Ultralytics YOLO26 for heatmap generation in data analysis?
|
||||
|
||||
Ultralytics YOLO11 offers seamless integration of advanced object detection and real-time heatmap generation, making it an ideal choice for businesses looking to visualize data more effectively. The key advantages include intuitive data distribution visualization, efficient pattern detection, and enhanced spatial analysis for better decision-making. Additionally, YOLO11's cutting-edge features such as persistent tracking, customizable colormaps, and support for various export formats make it superior to other tools like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and OpenCV for comprehensive data analysis. Learn more about business applications at [Ultralytics Plans](https://www.ultralytics.com/plans).
|
||||
Ultralytics YOLO26 offers seamless integration of advanced object detection and real-time heatmap generation, making it an ideal choice for businesses looking to visualize data more effectively. The key advantages include intuitive data distribution visualization, efficient pattern detection, and enhanced spatial analysis for better decision-making. Additionally, YOLO26's cutting-edge features such as persistent tracking, customizable colormaps, and support for various export formats make it superior to other tools like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and OpenCV for comprehensive data analysis. Learn more about business applications at [Ultralytics Plans](https://www.ultralytics.com/plans).
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@ Hyperparameters are high-level, structural settings for the algorithm. They are
|
|||
<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/hyperparameter-tuning-visual.avif" alt="Hyperparameter Tuning Visual">
|
||||
</p>
|
||||
|
||||
For a full list of augmentation hyperparameters used in YOLO11 please refer to the [configurations page](../usage/cfg.md#augmentation-settings).
|
||||
For a full list of augmentation hyperparameters used in YOLO26 please refer to the [configurations page](../usage/cfg.md#augmentation-settings).
|
||||
|
||||
### Genetic Evolution and Mutation
|
||||
|
||||
|
|
@ -78,7 +78,7 @@ The process is repeated until either the set number of iterations is reached or
|
|||
|
||||
## Default Search Space Description
|
||||
|
||||
The following table lists the default search space parameters for hyperparameter tuning in YOLO11. Each parameter has a specific value range defined by a tuple `(min, max)`.
|
||||
The following table lists the default search space parameters for hyperparameter tuning in YOLO26. Each parameter has a specific value range defined by a tuple `(min, max)`.
|
||||
|
||||
| Parameter | Type | Value Range | Description |
|
||||
| ----------------- | ------- | -------------- | -------------------------------------------------------------------------------------------------------------------------- |
|
||||
|
|
@ -109,7 +109,7 @@ The following table lists the default search space parameters for hyperparameter
|
|||
|
||||
## Custom Search Space Example
|
||||
|
||||
Here's how to define a search space and use the `model.tune()` method to utilize the `Tuner` class for hyperparameter tuning of YOLO11n on COCO8 for 30 epochs with an AdamW optimizer and skipping plotting, checkpointing and validation other than on final epoch for faster Tuning.
|
||||
Here's how to define a search space and use the `model.tune()` method to utilize the `Tuner` class for hyperparameter tuning of YOLO26n on COCO8 for 30 epochs with an AdamW optimizer and skipping plotting, checkpointing and validation other than on final epoch for faster Tuning.
|
||||
|
||||
!!! warning
|
||||
|
||||
|
|
@ -123,7 +123,7 @@ Here's how to define a search space and use the `model.tune()` method to utilize
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Initialize the YOLO model
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Define search space
|
||||
search_space = {
|
||||
|
|
@ -154,7 +154,7 @@ You can resume an interrupted hyperparameter tuning session by passing `resume=T
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Define a YOLO model
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Define search space
|
||||
search_space = {
|
||||
|
|
@ -288,7 +288,7 @@ The hyperparameter tuning process in Ultralytics YOLO is simplified yet powerful
|
|||
|
||||
1. [Hyperparameter Optimization in Wikipedia](https://en.wikipedia.org/wiki/Hyperparameter_optimization)
|
||||
2. [YOLOv5 Hyperparameter Evolution Guide](../yolov5/tutorials/hyperparameter_evolution.md)
|
||||
3. [Efficient Hyperparameter Tuning with Ray Tune and YOLO11](../integrations/ray-tune.md)
|
||||
3. [Efficient Hyperparameter Tuning with Ray Tune and YOLO26](../integrations/ray-tune.md)
|
||||
|
||||
For deeper insights, you can explore the [`Tuner` class](https://docs.ultralytics.com/reference/engine/tuner/) source code and accompanying documentation. Should you have any questions, feature requests, or need further assistance, feel free to reach out to us on [GitHub](https://github.com/ultralytics/ultralytics/issues/new/choose) or [Discord](https://discord.com/invite/ultralytics).
|
||||
|
||||
|
|
@ -306,7 +306,7 @@ To optimize the learning rate for Ultralytics YOLO, start by setting an initial
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Initialize the YOLO model
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Tune hyperparameters on COCO8 for 30 epochs
|
||||
model.tune(data="coco8.yaml", epochs=30, iterations=300, optimizer="AdamW", plots=False, save=False, val=False)
|
||||
|
|
@ -314,9 +314,9 @@ To optimize the learning rate for Ultralytics YOLO, start by setting an initial
|
|||
|
||||
For more details, check the [Ultralytics YOLO configuration page](../usage/cfg.md#augmentation-settings).
|
||||
|
||||
### What are the benefits of using genetic algorithms for hyperparameter tuning in YOLO11?
|
||||
### What are the benefits of using genetic algorithms for hyperparameter tuning in YOLO26?
|
||||
|
||||
Genetic algorithms in Ultralytics YOLO11 provide a robust method for exploring the hyperparameter space, leading to highly optimized model performance. Key benefits include:
|
||||
Genetic algorithms in Ultralytics YOLO26 provide a robust method for exploring the hyperparameter space, leading to highly optimized model performance. Key benefits include:
|
||||
|
||||
- **Efficient Search**: Genetic algorithms like mutation can quickly explore a large set of hyperparameters.
|
||||
- **Avoiding Local Minima**: By introducing randomness, they help in avoiding local minima, ensuring better global optimization.
|
||||
|
|
@ -326,7 +326,7 @@ To see how genetic algorithms can optimize hyperparameters, check out the [hyper
|
|||
|
||||
### How long does the hyperparameter tuning process take for Ultralytics YOLO?
|
||||
|
||||
The time required for hyperparameter tuning with Ultralytics YOLO largely depends on several factors such as the size of the dataset, the complexity of the model architecture, the number of iterations, and the computational resources available. For instance, tuning YOLO11n on a dataset like COCO8 for 30 epochs might take several hours to days, depending on the hardware.
|
||||
The time required for hyperparameter tuning with Ultralytics YOLO largely depends on several factors such as the size of the dataset, the complexity of the model architecture, the number of iterations, and the computational resources available. For instance, tuning YOLO26n on a dataset like COCO8 for 30 epochs might take several hours to days, depending on the hardware.
|
||||
|
||||
To effectively manage tuning time, define a clear tuning budget beforehand ([internal section link](#preparing-for-hyperparameter-tuning)). This helps in balancing resource allocation and optimization goals.
|
||||
|
||||
|
|
@ -340,8 +340,8 @@ When evaluating model performance during hyperparameter tuning in YOLO, you can
|
|||
|
||||
These metrics help you understand different aspects of your model's performance. Refer to the [Ultralytics YOLO performance metrics](../guides/yolo-performance-metrics.md) guide for a comprehensive overview.
|
||||
|
||||
### Can I use Ray Tune for advanced hyperparameter optimization with YOLO11?
|
||||
### Can I use Ray Tune for advanced hyperparameter optimization with YOLO26?
|
||||
|
||||
Yes, Ultralytics YOLO11 integrates with [Ray Tune](https://docs.ray.io/en/latest/tune/index.html) for advanced hyperparameter optimization. Ray Tune offers sophisticated search algorithms like Bayesian Optimization and Hyperband, along with parallel execution capabilities to speed up the tuning process.
|
||||
Yes, Ultralytics YOLO26 integrates with [Ray Tune](https://docs.ray.io/en/latest/tune/index.html) for advanced hyperparameter optimization. Ray Tune offers sophisticated search algorithms like Bayesian Optimization and Hyperband, along with parallel execution capabilities to speed up the tuning process.
|
||||
|
||||
To use Ray Tune with YOLO11, simply set the `use_ray=True` parameter in your `model.tune()` method call. For more details and examples, check out the [Ray Tune integration guide](../integrations/ray-tune.md).
|
||||
To use Ray Tune with YOLO26, simply set the `use_ray=True` parameter in your `model.tune()` method call. For more details and examples, check out the [Ray Tune integration guide](../integrations/ray-tune.md).
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ Whether you're a beginner or an expert in [deep learning](https://www.ultralytic
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Ultralytics YOLO11 Guides Overview
|
||||
<strong>Watch:</strong> Ultralytics YOLO26 Guides Overview
|
||||
</p>
|
||||
|
||||
## Guides
|
||||
|
|
@ -44,13 +44,13 @@ Here's a compilation of in-depth guides to help you master different aspects of
|
|||
- [NVIDIA DGX Spark](nvidia-dgx-spark.md): Quickstart guide for deploying YOLO models on NVIDIA DGX Spark devices.
|
||||
- [NVIDIA Jetson](nvidia-jetson.md): Quickstart guide for deploying YOLO models on NVIDIA Jetson devices.
|
||||
- [OpenVINO Latency vs Throughput Modes](optimizing-openvino-latency-vs-throughput-modes.md): Learn latency and throughput optimization techniques for peak YOLO inference performance.
|
||||
- [Preprocessing Annotated Data](preprocessing_annotated_data.md): Learn about preprocessing and augmenting image data in computer vision projects using YOLO11, including normalization, dataset augmentation, splitting, and exploratory data analysis (EDA).
|
||||
- [Preprocessing Annotated Data](preprocessing_annotated_data.md): Learn about preprocessing and augmenting image data in computer vision projects using YOLO26, including normalization, dataset augmentation, splitting, and exploratory data analysis (EDA).
|
||||
- [Raspberry Pi](raspberry-pi.md): Quickstart tutorial to run YOLO models on the latest Raspberry Pi hardware.
|
||||
- [ROS Quickstart](ros-quickstart.md): Learn how to integrate YOLO with the Robot Operating System (ROS) for real-time object detection in robotics applications, including Point Cloud and Depth images.
|
||||
- [SAHI Tiled Inference](sahi-tiled-inference.md): Comprehensive guide on leveraging SAHI's sliced inference capabilities with YOLO11 for object detection in high-resolution images.
|
||||
- [SAHI Tiled Inference](sahi-tiled-inference.md): Comprehensive guide on leveraging SAHI's sliced inference capabilities with YOLO26 for object detection in high-resolution images.
|
||||
- [Steps of a Computer Vision Project](steps-of-a-cv-project.md): Learn about the key steps involved in a computer vision project, including defining goals, selecting models, preparing data, and evaluating results.
|
||||
- [Tips for Model Training](model-training-tips.md): Explore tips on optimizing [batch sizes](https://www.ultralytics.com/glossary/batch-size), using [mixed precision](https://www.ultralytics.com/glossary/mixed-precision), applying pretrained weights, and more to make training your computer vision model a breeze.
|
||||
- [Triton Inference Server Integration](triton-inference-server.md): Dive into the integration of Ultralytics YOLO11 with NVIDIA's Triton Inference Server for scalable and efficient deep learning inference deployments.
|
||||
- [Triton Inference Server Integration](triton-inference-server.md): Dive into the integration of Ultralytics YOLO26 with NVIDIA's Triton Inference Server for scalable and efficient deep learning inference deployments.
|
||||
- [Vertex AI Deployment with Docker](vertex-ai-deployment-with-docker.md): Streamlined guide to containerizing YOLO models with Docker and deploying them on Google Cloud Vertex AI—covering build, push, autoscaling, and monitoring.
|
||||
- [View Inference Images in a Terminal](view-results-in-terminal.md): Use VSCode's integrated terminal to view inference results when using Remote Tunnel or SSH sessions.
|
||||
- [YOLO Common Issues](yolo-common-issues.md) ⭐ RECOMMENDED: Practical solutions and troubleshooting tips to the most frequently encountered issues when working with Ultralytics YOLO models.
|
||||
|
|
@ -77,14 +77,14 @@ Training a custom object detection model with Ultralytics YOLO is straightforwar
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo11n.pt") # Load a pretrained YOLO model
|
||||
model = YOLO("yolo26n.pt") # Load a pretrained YOLO model
|
||||
model.train(data="path/to/dataset.yaml", epochs=50) # Train on custom dataset
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo task=detect mode=train model=yolo11n.pt data=path/to/dataset.yaml epochs=50
|
||||
yolo task=detect mode=train model=yolo26n.pt data=path/to/dataset.yaml epochs=50
|
||||
```
|
||||
|
||||
For detailed dataset formatting and additional options, refer to our [Tips for Model Training](model-training-tips.md) guide.
|
||||
|
|
@ -93,9 +93,9 @@ For detailed dataset formatting and additional options, refer to our [Tips for M
|
|||
|
||||
Evaluating your YOLO model performance is crucial to understanding its efficacy. Key metrics include [Mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP), [Intersection over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (IoU), and F1 score. These metrics help assess the accuracy and [precision](https://www.ultralytics.com/glossary/precision) of object detection tasks. You can learn more about these metrics and how to improve your model in our [YOLO Performance Metrics](yolo-performance-metrics.md) guide.
|
||||
|
||||
### Why should I use Ultralytics HUB for my computer vision projects?
|
||||
### Why should I use Ultralytics Platform for my computer vision projects?
|
||||
|
||||
Ultralytics HUB is a no-code platform that simplifies managing, training, and deploying YOLO models. It supports seamless integration, real-time tracking, and cloud training, making it ideal for both beginners and professionals. Discover more about its features and how it can streamline your workflow with our [Ultralytics HUB](https://docs.ultralytics.com/hub/) quickstart guide.
|
||||
Ultralytics Platform is a no-code platform that simplifies managing, training, and deploying YOLO models. It supports seamless integration, real-time tracking, and cloud training, making it ideal for both beginners and professionals. Discover more about its features and how it can streamline your workflow with our [Ultralytics Platform](https://docs.ultralytics.com/platform/) quickstart guide.
|
||||
|
||||
### What are the common issues faced during YOLO model training, and how can I resolve them?
|
||||
|
||||
|
|
|
|||
|
|
@ -1,16 +1,16 @@
|
|||
---
|
||||
comments: true
|
||||
description: Master instance segmentation and tracking with Ultralytics YOLO11. Learn techniques for precise object identification and tracking.
|
||||
keywords: instance segmentation, tracking, YOLO11, Ultralytics, object detection, machine learning, computer vision, python
|
||||
description: Master instance segmentation and tracking with Ultralytics YOLO26. Learn techniques for precise object identification and tracking.
|
||||
keywords: instance segmentation, tracking, YOLO26, Ultralytics, object detection, machine learning, computer vision, python
|
||||
---
|
||||
|
||||
# Instance Segmentation and Tracking using Ultralytics YOLO11 🚀
|
||||
# Instance Segmentation and Tracking using Ultralytics YOLO26 🚀
|
||||
|
||||
## What is Instance Segmentation?
|
||||
|
||||
[Instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) is a computer vision task that involves identifying and outlining individual objects in an image at the pixel level. Unlike [semantic segmentation](https://www.ultralytics.com/glossary/semantic-segmentation) which only classifies pixels by category, instance segmentation uniquely labels and precisely delineates each object instance, making it crucial for applications requiring detailed spatial understanding like medical imaging, autonomous driving, and industrial automation.
|
||||
|
||||
[Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) provides powerful instance segmentation capabilities that enable precise object boundary detection while maintaining the speed and efficiency YOLO models are known for.
|
||||
[Ultralytics YOLO26](https://github.com/ultralytics/ultralytics/) provides powerful instance segmentation capabilities that enable precise object boundary detection while maintaining the speed and efficiency YOLO models are known for.
|
||||
|
||||
There are two types of instance segmentation tracking available in the Ultralytics package:
|
||||
|
||||
|
|
@ -26,7 +26,7 @@ There are two types of instance segmentation tracking available in the Ultralyti
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Instance Segmentation with Object Tracking using Ultralytics YOLO11
|
||||
<strong>Watch:</strong> Instance Segmentation with Object Tracking using Ultralytics YOLO26
|
||||
</p>
|
||||
|
||||
## Samples
|
||||
|
|
@ -41,7 +41,7 @@ There are two types of instance segmentation tracking available in the Ultralyti
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Instance segmentation using Ultralytics YOLO11
|
||||
# Instance segmentation using Ultralytics YOLO26
|
||||
yolo solutions isegment show=True
|
||||
|
||||
# Pass a source video
|
||||
|
|
@ -68,7 +68,7 @@ There are two types of instance segmentation tracking available in the Ultralyti
|
|||
# Initialize instance segmentation object
|
||||
isegment = solutions.InstanceSegmentation(
|
||||
show=True, # display the output
|
||||
model="yolo11n-seg.pt", # model="yolo11n-seg.pt" for object segmentation using YOLO11.
|
||||
model="yolo26n-seg.pt", # model="yolo26n-seg.pt" for object segmentation using YOLO26.
|
||||
# classes=[0, 2], # segment specific classes, e.g., person and car with the pretrained model.
|
||||
)
|
||||
|
||||
|
|
@ -110,19 +110,19 @@ Moreover, the following visualization arguments are available:
|
|||
|
||||
## Applications of Instance Segmentation
|
||||
|
||||
Instance segmentation with YOLO11 has numerous real-world applications across various industries:
|
||||
Instance segmentation with YOLO26 has numerous real-world applications across various industries:
|
||||
|
||||
### Waste Management and Recycling
|
||||
|
||||
YOLO11 can be used in [waste management facilities](https://www.ultralytics.com/blog/simplifying-e-waste-management-with-ai-innovations) to identify and sort different types of materials. The model can segment plastic waste, cardboard, metal, and other recyclables with high precision, enabling automated sorting systems to process waste more efficiently. This is particularly valuable considering that only about 10% of the 7 billion tonnes of plastic waste generated globally gets recycled.
|
||||
YOLO26 can be used in [waste management facilities](https://www.ultralytics.com/blog/simplifying-e-waste-management-with-ai-innovations) to identify and sort different types of materials. The model can segment plastic waste, cardboard, metal, and other recyclables with high precision, enabling automated sorting systems to process waste more efficiently. This is particularly valuable considering that only about 10% of the 7 billion tonnes of plastic waste generated globally gets recycled.
|
||||
|
||||
### Autonomous Vehicles
|
||||
|
||||
In [self-driving cars](https://www.ultralytics.com/solutions/ai-in-automotive), instance segmentation helps identify and track pedestrians, vehicles, traffic signs, and other road elements at the pixel level. This precise understanding of the environment is crucial for navigation and safety decisions. YOLO11's real-time performance makes it ideal for these time-sensitive applications.
|
||||
In [self-driving cars](https://www.ultralytics.com/solutions/ai-in-automotive), instance segmentation helps identify and track pedestrians, vehicles, traffic signs, and other road elements at the pixel level. This precise understanding of the environment is crucial for navigation and safety decisions. YOLO26's real-time performance makes it ideal for these time-sensitive applications.
|
||||
|
||||
### Medical Imaging
|
||||
|
||||
Instance segmentation can identify and outline tumors, organs, or cellular structures in medical scans. YOLO11's ability to precisely delineate object boundaries makes it valuable for [medical diagnostics](https://www.ultralytics.com/blog/ai-and-radiology-a-new-era-of-precision-and-efficiency) and treatment planning.
|
||||
Instance segmentation can identify and outline tumors, organs, or cellular structures in medical scans. YOLO26's ability to precisely delineate object boundaries makes it valuable for [medical diagnostics](https://www.ultralytics.com/blog/ai-and-radiology-a-new-era-of-precision-and-efficiency) and treatment planning.
|
||||
|
||||
### Construction Site Monitoring
|
||||
|
||||
|
|
@ -134,9 +134,9 @@ For any inquiries, feel free to post your questions in the [Ultralytics Issue Se
|
|||
|
||||
## FAQ
|
||||
|
||||
### How do I perform instance segmentation using Ultralytics YOLO11?
|
||||
### How do I perform instance segmentation using Ultralytics YOLO26?
|
||||
|
||||
To perform instance segmentation using Ultralytics YOLO11, initialize the YOLO model with a segmentation version of YOLO11 and process video frames through it. Here's a simplified code example:
|
||||
To perform instance segmentation using Ultralytics YOLO26, initialize the YOLO model with a segmentation version of YOLO26 and process video frames through it. Here's a simplified code example:
|
||||
|
||||
```python
|
||||
import cv2
|
||||
|
|
@ -153,7 +153,7 @@ video_writer = cv2.VideoWriter("instance-segmentation.avi", cv2.VideoWriter_four
|
|||
# Init InstanceSegmentation
|
||||
isegment = solutions.InstanceSegmentation(
|
||||
show=True, # display the output
|
||||
model="yolo11n-seg.pt", # model="yolo11n-seg.pt" for object segmentation using YOLO11.
|
||||
model="yolo26n-seg.pt", # model="yolo26n-seg.pt" for object segmentation using YOLO26.
|
||||
)
|
||||
|
||||
# Process video
|
||||
|
|
@ -170,16 +170,16 @@ video_writer.release()
|
|||
cv2.destroyAllWindows()
|
||||
```
|
||||
|
||||
Learn more about instance segmentation in the [Ultralytics YOLO11 guide](https://docs.ultralytics.com/tasks/segment/).
|
||||
Learn more about instance segmentation in the [Ultralytics YOLO26 guide](https://docs.ultralytics.com/tasks/segment/).
|
||||
|
||||
### What is the difference between instance segmentation and object tracking in Ultralytics YOLO11?
|
||||
### What is the difference between instance segmentation and object tracking in Ultralytics YOLO26?
|
||||
|
||||
Instance segmentation identifies and outlines individual objects within an image, giving each object a unique label and mask. Object tracking extends this by assigning consistent IDs to objects across video frames, facilitating continuous tracking of the same objects over time. When combined, as in YOLO11's implementation, you get powerful capabilities for analyzing object movement and behavior in videos while maintaining precise boundary information.
|
||||
Instance segmentation identifies and outlines individual objects within an image, giving each object a unique label and mask. Object tracking extends this by assigning consistent IDs to objects across video frames, facilitating continuous tracking of the same objects over time. When combined, as in YOLO26's implementation, you get powerful capabilities for analyzing object movement and behavior in videos while maintaining precise boundary information.
|
||||
|
||||
### Why should I use Ultralytics YOLO11 for instance segmentation and tracking over other models like Mask R-CNN or Faster R-CNN?
|
||||
### Why should I use Ultralytics YOLO26 for instance segmentation and tracking over other models like Mask R-CNN or Faster R-CNN?
|
||||
|
||||
Ultralytics YOLO11 offers real-time performance, superior [accuracy](https://www.ultralytics.com/glossary/accuracy), and ease of use compared to other models like Mask R-CNN or Faster R-CNN. YOLO11 processes images in a single pass (one-stage detection), making it significantly faster while maintaining high precision. It also provides seamless integration with [Ultralytics HUB](https://www.ultralytics.com/hub), allowing users to manage models, datasets, and training pipelines efficiently. For applications requiring both speed and accuracy, YOLO11 provides an optimal balance.
|
||||
Ultralytics YOLO26 offers real-time performance, superior [accuracy](https://www.ultralytics.com/glossary/accuracy), and ease of use compared to other models like Mask R-CNN or Faster R-CNN. YOLO26 processes images in a single pass (one-stage detection), making it significantly faster while maintaining high precision. It also provides seamless integration with [Ultralytics Platform](https://platform.ultralytics.com), allowing users to manage models, datasets, and training pipelines efficiently. For applications requiring both speed and accuracy, YOLO26 provides an optimal balance.
|
||||
|
||||
### Are there any datasets provided by Ultralytics suitable for training YOLO11 models for instance segmentation and tracking?
|
||||
### Are there any datasets provided by Ultralytics suitable for training YOLO26 models for instance segmentation and tracking?
|
||||
|
||||
Yes, Ultralytics offers several datasets suitable for training YOLO11 models for instance segmentation, including [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/), [COCO8-Seg](https://docs.ultralytics.com/datasets/segment/coco8-seg/) (a smaller subset for quick testing), [Package-Seg](https://docs.ultralytics.com/datasets/segment/package-seg/), and [Crack-Seg](https://docs.ultralytics.com/datasets/segment/crack-seg/). These datasets come with pixel-level annotations needed for instance segmentation tasks. For more specialized applications, you can also create custom datasets following the Ultralytics format. Complete dataset information and usage instructions can be found in the [Ultralytics Datasets documentation](https://docs.ultralytics.com/datasets/).
|
||||
Yes, Ultralytics offers several datasets suitable for training YOLO26 models for instance segmentation, including [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/), [COCO8-Seg](https://docs.ultralytics.com/datasets/segment/coco8-seg/) (a smaller subset for quick testing), [Package-Seg](https://docs.ultralytics.com/datasets/segment/package-seg/), and [Crack-Seg](https://docs.ultralytics.com/datasets/segment/crack-seg/). These datasets come with pixel-level annotations needed for instance segmentation tasks. For more specialized applications, you can also create custom datasets following the Ultralytics format. Complete dataset information and usage instructions can be found in the [Ultralytics Datasets documentation](https://docs.ultralytics.com/datasets/).
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn to extract isolated objects from inference results using Ultralytics Predict Mode. Step-by-step guide for segmentation object isolation.
|
||||
keywords: Ultralytics, segmentation, object isolation, Predict Mode, YOLO11, machine learning, object detection, binary mask, image processing
|
||||
keywords: Ultralytics, segmentation, object isolation, Predict Mode, YOLO26, machine learning, object detection, binary mask, image processing
|
||||
---
|
||||
|
||||
# Isolating Segmentation Objects
|
||||
|
|
@ -31,7 +31,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo11n-seg.pt")
|
||||
model = YOLO("yolo26n-seg.pt")
|
||||
|
||||
# Run inference
|
||||
results = model.predict()
|
||||
|
|
@ -264,7 +264,7 @@ import numpy as np
|
|||
|
||||
from ultralytics import YOLO
|
||||
|
||||
m = YOLO("yolo11n-seg.pt") # (4)!
|
||||
m = YOLO("yolo26n-seg.pt") # (4)!
|
||||
res = m.predict(source="path/to/image.jpg") # (3)!
|
||||
|
||||
# Iterate detection results (5)
|
||||
|
|
@ -307,16 +307,16 @@ for r in res:
|
|||
|
||||
## FAQ
|
||||
|
||||
### How do I isolate objects using Ultralytics YOLO11 for segmentation tasks?
|
||||
### How do I isolate objects using Ultralytics YOLO26 for segmentation tasks?
|
||||
|
||||
To isolate objects using Ultralytics YOLO11, follow these steps:
|
||||
To isolate objects using Ultralytics YOLO26, follow these steps:
|
||||
|
||||
1. **Load the model and run inference:**
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo11n-seg.pt")
|
||||
model = YOLO("yolo26n-seg.pt")
|
||||
results = model.predict(source="path/to/your/image.jpg")
|
||||
```
|
||||
|
||||
|
|
@ -342,7 +342,7 @@ Refer to the guide on [Predict Mode](../modes/predict.md) and the [Segment Task]
|
|||
|
||||
### What options are available for saving the isolated objects after segmentation?
|
||||
|
||||
Ultralytics YOLO11 offers two main options for saving isolated objects:
|
||||
Ultralytics YOLO26 offers two main options for saving isolated objects:
|
||||
|
||||
1. **With a Black Background:**
|
||||
|
||||
|
|
@ -358,7 +358,7 @@ Ultralytics YOLO11 offers two main options for saving isolated objects:
|
|||
|
||||
For further details, visit the [Predict Mode](../modes/predict.md) section.
|
||||
|
||||
### How can I crop isolated objects to their bounding boxes using Ultralytics YOLO11?
|
||||
### How can I crop isolated objects to their bounding boxes using Ultralytics YOLO26?
|
||||
|
||||
To crop isolated objects to their bounding boxes:
|
||||
|
||||
|
|
@ -375,9 +375,9 @@ To crop isolated objects to their bounding boxes:
|
|||
|
||||
Learn more about bounding box results in the [Predict Mode](../modes/predict.md#boxes) documentation.
|
||||
|
||||
### Why should I use Ultralytics YOLO11 for object isolation in segmentation tasks?
|
||||
### Why should I use Ultralytics YOLO26 for object isolation in segmentation tasks?
|
||||
|
||||
Ultralytics YOLO11 provides:
|
||||
Ultralytics YOLO26 provides:
|
||||
|
||||
- **High-speed** real-time object detection and segmentation.
|
||||
- **Accurate bounding box and mask generation** for precise object isolation.
|
||||
|
|
@ -385,9 +385,9 @@ Ultralytics YOLO11 provides:
|
|||
|
||||
Explore the benefits of using YOLO in the [Segment Task documentation](../tasks/segment.md).
|
||||
|
||||
### Can I save isolated objects including the background using Ultralytics YOLO11?
|
||||
### Can I save isolated objects including the background using Ultralytics YOLO26?
|
||||
|
||||
Yes, this is a built-in feature in Ultralytics YOLO11. Use the `save_crop` argument in the `predict()` method. For example:
|
||||
Yes, this is a built-in feature in Ultralytics YOLO26. Use the `save_crop` argument in the `predict()` method. For example:
|
||||
|
||||
```python
|
||||
results = model.predict(source="path/to/your/image.jpg", save_crop=True)
|
||||
|
|
|
|||
|
|
@ -249,7 +249,7 @@ fold_lbl_distrb.to_csv(save_path / "kfold_label_distribution.csv")
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
weights_path = "path/to/weights.pt" # use yolo11n.pt for a small model
|
||||
weights_path = "path/to/weights.pt" # use yolo26n.pt for a small model
|
||||
model = YOLO(weights_path, task="detect")
|
||||
```
|
||||
|
||||
|
|
@ -313,7 +313,7 @@ For a comprehensive guide, see the [K-Fold Dataset Split](#k-fold-dataset-split)
|
|||
|
||||
### Why should I use Ultralytics YOLO for object detection?
|
||||
|
||||
Ultralytics YOLO offers state-of-the-art, real-time object detection with high [accuracy](https://www.ultralytics.com/glossary/accuracy) and efficiency. It's versatile, supporting multiple [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks such as detection, segmentation, and classification. Additionally, it integrates seamlessly with tools like [Ultralytics HUB](https://docs.ultralytics.com/hub/) for no-code model training and deployment. For more details, explore the benefits and features on our [Ultralytics YOLO page](https://www.ultralytics.com/yolo).
|
||||
Ultralytics YOLO offers state-of-the-art, real-time object detection with high [accuracy](https://www.ultralytics.com/glossary/accuracy) and efficiency. It's versatile, supporting multiple [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks such as detection, segmentation, and classification. Additionally, it integrates seamlessly with tools like [Ultralytics Platform](https://docs.ultralytics.com/platform/) for no-code model training and deployment. For more details, explore the benefits and features on our [Ultralytics YOLO page](https://www.ultralytics.com/yolo).
|
||||
|
||||
### How can I ensure my annotations are in the correct format for Ultralytics YOLO?
|
||||
|
||||
|
|
|
|||
|
|
@ -1,14 +1,14 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn about YOLO11's diverse deployment options to maximize your model's performance. Explore PyTorch, TensorRT, OpenVINO, TF Lite, and more!
|
||||
keywords: YOLO11, deployment options, export formats, PyTorch, TensorRT, OpenVINO, TF Lite, machine learning, model deployment
|
||||
description: Learn about YOLO26's diverse deployment options to maximize your model's performance. Explore PyTorch, TensorRT, OpenVINO, TF Lite, and more!
|
||||
keywords: YOLO26, deployment options, export formats, PyTorch, TensorRT, OpenVINO, TF Lite, machine learning, model deployment
|
||||
---
|
||||
|
||||
# Comparative Analysis of YOLO11 Deployment Options
|
||||
# Comparative Analysis of YOLO26 Deployment Options
|
||||
|
||||
## Introduction
|
||||
|
||||
You've come a long way on your journey with YOLO11. You've diligently collected data, meticulously annotated it, and put in the hours to train and rigorously evaluate your custom YOLO11 model. Now, it's time to put your model to work for your specific application, use case, or project. But there's a critical decision that stands before you: how to export and deploy your model effectively.
|
||||
You've come a long way on your journey with YOLO26. You've diligently collected data, meticulously annotated it, and put in the hours to train and rigorously evaluate your custom YOLO26 model. Now, it's time to put your model to work for your specific application, use case, or project. But there's a critical decision that stands before you: how to export and deploy your model effectively.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
|
|
@ -18,20 +18,20 @@ You've come a long way on your journey with YOLO11. You've diligently collected
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Choose the Best Ultralytics YOLO11 Deployment Format for Your Project | TensorRT | OpenVINO 🚀
|
||||
<strong>Watch:</strong> How to Choose the Best Ultralytics YOLO26 Deployment Format for Your Project | TensorRT | OpenVINO 🚀
|
||||
</p>
|
||||
|
||||
This guide walks you through YOLO11's deployment options and the essential factors to consider to choose the right option for your project.
|
||||
This guide walks you through YOLO26's deployment options and the essential factors to consider to choose the right option for your project.
|
||||
|
||||
## How to Select the Right Deployment Option for Your YOLO11 Model
|
||||
## How to Select the Right Deployment Option for Your YOLO26 Model
|
||||
|
||||
When it's time to deploy your YOLO11 model, selecting a suitable export format is very important. As outlined in the [Ultralytics YOLO11 Modes documentation](../modes/export.md#usage-examples), the `model.export()` function allows you to convert your trained model into a variety of formats tailored to diverse environments and performance requirements.
|
||||
When it's time to deploy your YOLO26 model, selecting a suitable export format is very important. As outlined in the [Ultralytics YOLO26 Modes documentation](../modes/export.md#usage-examples), the `model.export()` function allows you to convert your trained model into a variety of formats tailored to diverse environments and performance requirements.
|
||||
|
||||
The ideal format depends on your model's intended operational context, balancing speed, hardware constraints, and ease of integration. In the following section, we'll take a closer look at each export option, understanding when to choose each one.
|
||||
|
||||
## YOLO11's Deployment Options
|
||||
## YOLO26's Deployment Options
|
||||
|
||||
Let's walk through the different YOLO11 deployment options. For a detailed walkthrough of the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
|
||||
Let's walk through the different YOLO26 deployment options. For a detailed walkthrough of the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
|
||||
|
||||
### PyTorch
|
||||
|
||||
|
|
@ -207,9 +207,9 @@ NCNN is a high-performance neural network inference framework optimized for the
|
|||
- **Security Considerations**: Focuses on running locally on the device, leveraging the inherent security of on-device processing.
|
||||
- **Hardware Acceleration**: Tailored for ARM CPUs and GPUs, with specific optimizations for these architectures.
|
||||
|
||||
## Comparative Analysis of YOLO11 Deployment Options
|
||||
## Comparative Analysis of YOLO26 Deployment Options
|
||||
|
||||
The following table provides a snapshot of the various deployment options available for YOLO11 models, helping you to assess which may best fit your project needs based on several critical criteria. For an in-depth look at each deployment option's format, please see the [Ultralytics documentation page on export formats](../modes/export.md#export-formats).
|
||||
The following table provides a snapshot of the various deployment options available for YOLO26 models, helping you to assess which may best fit your project needs based on several critical criteria. For an in-depth look at each deployment option's format, please see the [Ultralytics documentation page on export formats](../modes/export.md#export-formats).
|
||||
|
||||
| Deployment Option | Performance Benchmarks | Compatibility and Integration | Community Support and Ecosystem | Case Studies | Maintenance and Updates | Security Considerations | Hardware Acceleration |
|
||||
| ----------------- | ----------------------------------------------- | ---------------------------------------------- | --------------------------------------------- | ------------------------------------------ | ---------------------------------------------- | ------------------------------------------------- | ---------------------------------- |
|
||||
|
|
@ -232,30 +232,30 @@ This comparative analysis gives you a high-level overview. For deployment, it's
|
|||
|
||||
## Community and Support
|
||||
|
||||
When you're getting started with YOLO11, having a helpful community and support can make a significant impact. Here's how to connect with others who share your interests and get the assistance you need.
|
||||
When you're getting started with YOLO26, having a helpful community and support can make a significant impact. Here's how to connect with others who share your interests and get the assistance you need.
|
||||
|
||||
### Engage with the Broader Community
|
||||
|
||||
- **GitHub Discussions:** The [YOLO11 repository on GitHub](https://github.com/ultralytics/ultralytics) has a "Discussions" section where you can ask questions, report issues, and suggest improvements.
|
||||
- **GitHub Discussions:** The [YOLO26 repository on GitHub](https://github.com/ultralytics/ultralytics) has a "Discussions" section where you can ask questions, report issues, and suggest improvements.
|
||||
- **Ultralytics Discord Server:** Ultralytics has a [Discord server](https://discord.com/invite/ultralytics) where you can interact with other users and developers.
|
||||
|
||||
### Official Documentation and Resources
|
||||
|
||||
- **Ultralytics YOLO11 Docs:** The [official documentation](../index.md) provides a comprehensive overview of YOLO11, along with guides on installation, usage, and troubleshooting.
|
||||
- **Ultralytics YOLO26 Docs:** The [official documentation](../index.md) provides a comprehensive overview of YOLO26, along with guides on installation, usage, and troubleshooting.
|
||||
|
||||
These resources will help you tackle challenges and stay updated on the latest trends and best practices in the YOLO11 community.
|
||||
These resources will help you tackle challenges and stay updated on the latest trends and best practices in the YOLO26 community.
|
||||
|
||||
## Conclusion
|
||||
|
||||
In this guide, we've explored the different deployment options for YOLO11. We've also discussed the important factors to consider when making your choice. These options allow you to customize your model for various environments and performance requirements, making it suitable for real-world applications.
|
||||
In this guide, we've explored the different deployment options for YOLO26. We've also discussed the important factors to consider when making your choice. These options allow you to customize your model for various environments and performance requirements, making it suitable for real-world applications.
|
||||
|
||||
Don't forget that the YOLO11 and [Ultralytics community](https://github.com/orgs/ultralytics/discussions) is a valuable source of help. Connect with other developers and experts to learn unique tips and solutions you might not find in regular documentation. Keep seeking knowledge, exploring new ideas, and sharing your experiences.
|
||||
Don't forget that the YOLO26 and [Ultralytics community](https://github.com/orgs/ultralytics/discussions) is a valuable source of help. Connect with other developers and experts to learn unique tips and solutions you might not find in regular documentation. Keep seeking knowledge, exploring new ideas, and sharing your experiences.
|
||||
|
||||
## FAQ
|
||||
|
||||
### What are the deployment options available for YOLO11 on different hardware platforms?
|
||||
### What are the deployment options available for YOLO26 on different hardware platforms?
|
||||
|
||||
Ultralytics YOLO11 supports various deployment formats, each designed for specific environments and hardware platforms. Key formats include:
|
||||
Ultralytics YOLO26 supports various deployment formats, each designed for specific environments and hardware platforms. Key formats include:
|
||||
|
||||
- **PyTorch** for research and prototyping, with excellent Python integration.
|
||||
- **TorchScript** for production environments where Python is unavailable.
|
||||
|
|
@ -265,18 +265,18 @@ Ultralytics YOLO11 supports various deployment formats, each designed for specif
|
|||
|
||||
Each format has unique advantages. For a detailed walkthrough, see our [export process documentation](../modes/export.md#usage-examples).
|
||||
|
||||
### How do I improve the inference speed of my YOLO11 model on an Intel CPU?
|
||||
### How do I improve the inference speed of my YOLO26 model on an Intel CPU?
|
||||
|
||||
To enhance inference speed on Intel CPUs, you can deploy your YOLO11 model using Intel's OpenVINO toolkit. OpenVINO offers significant performance boosts by optimizing models to leverage Intel hardware efficiently.
|
||||
To enhance inference speed on Intel CPUs, you can deploy your YOLO26 model using Intel's OpenVINO toolkit. OpenVINO offers significant performance boosts by optimizing models to leverage Intel hardware efficiently.
|
||||
|
||||
1. Convert your YOLO11 model to the OpenVINO format using the `model.export()` function.
|
||||
1. Convert your YOLO26 model to the OpenVINO format using the `model.export()` function.
|
||||
2. Follow the detailed setup guide in the [Intel OpenVINO Export documentation](../integrations/openvino.md).
|
||||
|
||||
For more insights, check out our [blog post](https://www.ultralytics.com/blog/achieve-faster-inference-speeds-ultralytics-yolov8-openvino).
|
||||
|
||||
### Can I deploy YOLO11 models on mobile devices?
|
||||
### Can I deploy YOLO26 models on mobile devices?
|
||||
|
||||
Yes, YOLO11 models can be deployed on mobile devices using [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) Lite (TF Lite) for both Android and iOS platforms. TF Lite is designed for mobile and embedded devices, providing efficient on-device inference.
|
||||
Yes, YOLO26 models can be deployed on mobile devices using [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) Lite (TF Lite) for both Android and iOS platforms. TF Lite is designed for mobile and embedded devices, providing efficient on-device inference.
|
||||
|
||||
!!! example
|
||||
|
||||
|
|
@ -296,9 +296,9 @@ Yes, YOLO11 models can be deployed on mobile devices using [TensorFlow](https://
|
|||
|
||||
For more details on deploying models to mobile, refer to our [TF Lite integration guide](../integrations/tflite.md).
|
||||
|
||||
### What factors should I consider when choosing a deployment format for my YOLO11 model?
|
||||
### What factors should I consider when choosing a deployment format for my YOLO26 model?
|
||||
|
||||
When choosing a deployment format for YOLO11, consider the following factors:
|
||||
When choosing a deployment format for YOLO26, consider the following factors:
|
||||
|
||||
- **Performance**: Some formats like TensorRT provide exceptional speeds on NVIDIA GPUs, while OpenVINO is optimized for Intel hardware.
|
||||
- **Compatibility**: ONNX offers broad compatibility across different platforms.
|
||||
|
|
@ -307,11 +307,11 @@ When choosing a deployment format for YOLO11, consider the following factors:
|
|||
|
||||
For a comparative analysis, refer to our [export formats documentation](../modes/export.md#export-formats).
|
||||
|
||||
### How can I deploy YOLO11 models in a web application?
|
||||
### How can I deploy YOLO26 models in a web application?
|
||||
|
||||
To deploy YOLO11 models in a web application, you can use TensorFlow.js (TF.js), which allows for running [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models directly in the browser. This approach eliminates the need for backend infrastructure and provides real-time performance.
|
||||
To deploy YOLO26 models in a web application, you can use TensorFlow.js (TF.js), which allows for running [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models directly in the browser. This approach eliminates the need for backend infrastructure and provides real-time performance.
|
||||
|
||||
1. Export the YOLO11 model to the TF.js format.
|
||||
1. Export the YOLO26 model to the TF.js format.
|
||||
2. Integrate the exported model into your web application.
|
||||
|
||||
For step-by-step instructions, refer to our guide on [TensorFlow.js integration](../integrations/tfjs.md).
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ It's also important to follow best practices when deploying a model because depl
|
|||
|
||||
Oftentimes, once a model is [trained](./model-training-tips.md), [evaluated](./model-evaluation-insights.md), and [tested](./model-testing.md), it needs to be converted into specific formats to be deployed effectively in various environments, such as cloud, edge, or local devices.
|
||||
|
||||
With YOLO11, you can [export your model to various formats](../modes/export.md) depending on your deployment needs. For instance, [exporting YOLO11 to ONNX](../integrations/onnx.md) is straightforward and ideal for transferring models between frameworks. To explore more integration options and ensure a smooth deployment across different environments, visit our [model integration hub](../integrations/index.md).
|
||||
With YOLO26, you can [export your model to various formats](../modes/export.md) depending on your deployment needs. For instance, [exporting YOLO26 to ONNX](../integrations/onnx.md) is straightforward and ideal for transferring models between frameworks. To explore more integration options and ensure a smooth deployment across different environments, visit our [model integration hub](../integrations/index.md).
|
||||
|
||||
### Choosing a Deployment Environment
|
||||
|
||||
|
|
@ -65,9 +65,9 @@ Containerization is a powerful approach that packages your model and all its dep
|
|||
- **Scalability**: Containers can be easily scaled up or down based on demand, and orchestration tools like Kubernetes can automate this process.
|
||||
- **Version Control**: Docker images can be versioned, allowing you to track changes and roll back to previous versions if needed.
|
||||
|
||||
### Implementing Docker for YOLO11 Deployment
|
||||
### Implementing Docker for YOLO26 Deployment
|
||||
|
||||
To containerize your YOLO11 model, you can create a Dockerfile that specifies all the necessary dependencies and configurations. Here's a basic example:
|
||||
To containerize your YOLO26 model, you can create a Dockerfile that specifies all the necessary dependencies and configurations. Here's a basic example:
|
||||
|
||||
```dockerfile
|
||||
FROM ultralytics/ultralytics:latest
|
||||
|
|
@ -75,11 +75,11 @@ FROM ultralytics/ultralytics:latest
|
|||
WORKDIR /app
|
||||
|
||||
# Copy your model and any additional files
|
||||
COPY ./models/yolo11.pt /app/models/
|
||||
COPY ./models/yolo26.pt /app/models/
|
||||
COPY ./scripts /app/scripts/
|
||||
|
||||
# Set up any environment variables
|
||||
ENV MODEL_PATH=/app/models/yolo11.pt
|
||||
ENV MODEL_PATH=/app/models/yolo26.pt
|
||||
|
||||
# Command to run when the container starts
|
||||
CMD ["python", "/app/scripts/predict.py"]
|
||||
|
|
@ -130,7 +130,7 @@ Experiencing a drop in your model's accuracy after deployment can be frustrating
|
|||
- **Review Model Export and Conversion:** Re-export the model and make sure that the conversion process maintains the integrity of the model weights and architecture.
|
||||
- **Test with a Controlled Dataset:** Deploy the model in a test environment with a dataset you control and compare the results with the training phase. You can identify if the issue is with the deployment environment or the data.
|
||||
|
||||
When deploying YOLO11, several factors can affect model accuracy. Converting models to formats like [TensorRT](../integrations/tensorrt.md) involves optimizations such as weight quantization and layer fusion, which can cause minor precision losses. Using FP16 (half-precision) instead of FP32 (full-precision) can speed up inference but may introduce numerical precision errors. Also, hardware constraints, like those on the [Jetson Nano](./nvidia-jetson.md), with lower CUDA core counts and reduced memory bandwidth, can impact performance.
|
||||
When deploying YOLO26, several factors can affect model accuracy. Converting models to formats like [TensorRT](../integrations/tensorrt.md) involves optimizations such as weight quantization and layer fusion, which can cause minor precision losses. Using FP16 (half-precision) instead of FP32 (full-precision) can speed up inference but may introduce numerical precision errors. Also, hardware constraints, like those on the [Jetson Nano](./nvidia-jetson.md), with lower CUDA core counts and reduced memory bandwidth, can impact performance.
|
||||
|
||||
### Inferences Are Taking Longer Than You Expected
|
||||
|
||||
|
|
@ -142,7 +142,7 @@ When deploying [machine learning](https://www.ultralytics.com/glossary/machine-l
|
|||
- **Profile the Inference Pipeline:** Identifying bottlenecks in the inference pipeline can help pinpoint the source of delays. Use profiling tools to analyze each step of the inference process, identifying and addressing any stages that cause significant delays, such as inefficient layers or data transfer issues.
|
||||
- **Use Appropriate Precision:** Using higher precision than necessary can slow down inference times. Experiment with using lower precision, such as FP16 (half-precision), instead of FP32 (full-precision). While FP16 can reduce inference time, also keep in mind that it can impact model accuracy.
|
||||
|
||||
If you are facing this issue while deploying YOLO11, consider that YOLO11 offers [various model sizes](../models/yolo11.md), such as YOLO11n (nano) for devices with lower memory capacity and YOLO11x (extra-large) for more powerful GPUs. Choosing the right model variant for your hardware can help balance memory usage and processing time.
|
||||
If you are facing this issue while deploying YOLO26, consider that YOLO26 offers [various model sizes](../models/yolo26.md), such as YOLO26n (nano) for devices with lower memory capacity and YOLO26x (extra-large) for more powerful GPUs. Choosing the right model variant for your hardware can help balance memory usage and processing time.
|
||||
|
||||
Also keep in mind that the size of the input images directly impacts memory usage and processing time. Lower resolutions reduce memory usage and speed up inference, while higher resolutions improve accuracy but require more memory and processing power.
|
||||
|
||||
|
|
@ -168,12 +168,12 @@ Being part of a community of computer vision enthusiasts can help you solve prob
|
|||
|
||||
### Community Resources
|
||||
|
||||
- **GitHub Issues:** Explore the [YOLO11 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
|
||||
- **GitHub Issues:** Explore the [YOLO26 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
|
||||
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences.
|
||||
|
||||
### Official Documentation
|
||||
|
||||
- **Ultralytics YOLO11 Documentation:** Visit the [official YOLO11 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
|
||||
- **Ultralytics YOLO26 Documentation:** Visit the [official YOLO26 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
|
||||
|
||||
Using these resources will help you solve challenges and stay up-to-date with the latest trends and practices in the computer vision community.
|
||||
|
||||
|
|
@ -185,22 +185,22 @@ After deploying your model, the next step would be monitoring, maintaining, and
|
|||
|
||||
## FAQ
|
||||
|
||||
### What are the best practices for deploying a machine learning model using Ultralytics YOLO11?
|
||||
### What are the best practices for deploying a machine learning model using Ultralytics YOLO26?
|
||||
|
||||
Deploying a machine learning model, particularly with Ultralytics YOLO11, involves several best practices to ensure efficiency and reliability. First, choose the deployment environment that suits your needs—cloud, edge, or local. Optimize your model through techniques like [pruning, quantization, and knowledge distillation](#model-optimization-techniques) for efficient deployment in resource-constrained environments. Consider using [containerization with Docker](#containerization-for-streamlined-deployment) to ensure consistency across different environments. Lastly, ensure data consistency and preprocessing steps align with the training phase to maintain performance. You can also refer to [model deployment options](./model-deployment-options.md) for more detailed guidelines.
|
||||
Deploying a machine learning model, particularly with Ultralytics YOLO26, involves several best practices to ensure efficiency and reliability. First, choose the deployment environment that suits your needs—cloud, edge, or local. Optimize your model through techniques like [pruning, quantization, and knowledge distillation](#model-optimization-techniques) for efficient deployment in resource-constrained environments. Consider using [containerization with Docker](#containerization-for-streamlined-deployment) to ensure consistency across different environments. Lastly, ensure data consistency and preprocessing steps align with the training phase to maintain performance. You can also refer to [model deployment options](./model-deployment-options.md) for more detailed guidelines.
|
||||
|
||||
### How can I troubleshoot common deployment issues with Ultralytics YOLO11 models?
|
||||
### How can I troubleshoot common deployment issues with Ultralytics YOLO26 models?
|
||||
|
||||
Troubleshooting deployment issues can be broken down into a few key steps. If your model's accuracy drops after deployment, check for data consistency, validate preprocessing steps, and ensure the hardware/software environment matches what you used during training. For slow inference times, perform warm-up runs, optimize your inference engine, use asynchronous processing, and profile your inference pipeline. Refer to [troubleshooting deployment issues](#troubleshooting-deployment-issues) for a detailed guide on these best practices.
|
||||
|
||||
### How does Ultralytics YOLO11 optimization enhance model performance on edge devices?
|
||||
### How does Ultralytics YOLO26 optimization enhance model performance on edge devices?
|
||||
|
||||
Optimizing Ultralytics YOLO11 models for edge devices involves using techniques like pruning to reduce the model size, quantization to convert weights to lower precision, and knowledge distillation to train smaller models that mimic larger ones. These techniques ensure the model runs efficiently on devices with limited computational power. Tools like [TensorFlow Lite](../integrations/tflite.md) and [NVIDIA Jetson](./nvidia-jetson.md) are particularly useful for these optimizations. Learn more about these techniques in our section on [model optimization](#model-optimization-techniques).
|
||||
Optimizing Ultralytics YOLO26 models for edge devices involves using techniques like pruning to reduce the model size, quantization to convert weights to lower precision, and knowledge distillation to train smaller models that mimic larger ones. These techniques ensure the model runs efficiently on devices with limited computational power. Tools like [TensorFlow Lite](../integrations/tflite.md) and [NVIDIA Jetson](./nvidia-jetson.md) are particularly useful for these optimizations. Learn more about these techniques in our section on [model optimization](#model-optimization-techniques).
|
||||
|
||||
### What are the security considerations for deploying machine learning models with Ultralytics YOLO11?
|
||||
### What are the security considerations for deploying machine learning models with Ultralytics YOLO26?
|
||||
|
||||
Security is paramount when deploying machine learning models. Ensure secure data transmission using encryption protocols like TLS. Implement robust access controls, including strong authentication and role-based access control (RBAC). Model obfuscation techniques, such as encrypting model parameters and serving models in a secure environment like a trusted execution environment (TEE), offer additional protection. For detailed practices, refer to [security considerations](#security-considerations-in-model-deployment).
|
||||
|
||||
### How do I choose the right deployment environment for my Ultralytics YOLO11 model?
|
||||
### How do I choose the right deployment environment for my Ultralytics YOLO26 model?
|
||||
|
||||
Selecting the optimal deployment environment for your Ultralytics YOLO11 model depends on your application's specific needs. Cloud deployment offers scalability and ease of access, making it ideal for applications with high data volumes. Edge deployment is best for low-latency applications requiring real-time responses, using tools like [TensorFlow Lite](../integrations/tflite.md). Local deployment suits scenarios needing stringent data privacy and control. For a comprehensive overview of each environment, check out our section on [choosing a deployment environment](#choosing-a-deployment-environment).
|
||||
Selecting the optimal deployment environment for your Ultralytics YOLO26 model depends on your application's specific needs. Cloud deployment offers scalability and ease of access, making it ideal for applications with high data volumes. Edge deployment is best for low-latency applications requiring real-time responses, using tools like [TensorFlow Lite](../integrations/tflite.md). Local deployment suits scenarios needing stringent data privacy and control. For a comprehensive overview of each environment, check out our section on [choosing a deployment environment](#choosing-a-deployment-environment).
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore the most effective ways to assess and refine YOLO11 models for better performance. Learn about evaluation metrics, fine-tuning processes, and how to customize your model for specific needs.
|
||||
description: Explore the most effective ways to assess and refine YOLO26 models for better performance. Learn about evaluation metrics, fine-tuning processes, and how to customize your model for specific needs.
|
||||
keywords: Model Evaluation, Machine Learning Model Evaluation, Fine Tuning Machine Learning, Fine Tune Model, Evaluating Models, Model Fine Tuning, How to Fine Tune a Model
|
||||
---
|
||||
|
||||
|
|
@ -56,23 +56,23 @@ Other mAP metrics include mAP@0.75, which uses a stricter IoU threshold of 0.75,
|
|||
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/mean-average-precision-overview.avif" alt="Mean Average Precision Overview">
|
||||
</p>
|
||||
|
||||
## Evaluating YOLO11 Model Performance
|
||||
## Evaluating YOLO26 Model Performance
|
||||
|
||||
With respect to YOLO11, you can use the [validation mode](../modes/val.md) to evaluate the model. Also, be sure to take a look at our guide that goes in-depth into [YOLO11 performance metrics](./yolo-performance-metrics.md) and how they can be interpreted.
|
||||
With respect to YOLO26, you can use the [validation mode](../modes/val.md) to evaluate the model. Also, be sure to take a look at our guide that goes in-depth into [YOLO26 performance metrics](./yolo-performance-metrics.md) and how they can be interpreted.
|
||||
|
||||
### Common Community Questions
|
||||
|
||||
When evaluating your YOLO11 model, you might run into a few hiccups. Based on common community questions, here are some tips to help you get the most out of your YOLO11 model:
|
||||
When evaluating your YOLO26 model, you might run into a few hiccups. Based on common community questions, here are some tips to help you get the most out of your YOLO26 model:
|
||||
|
||||
#### Handling Variable Image Sizes
|
||||
|
||||
Evaluating your YOLO11 model with images of different sizes can help you understand its performance on diverse datasets. Using the `rect=true` validation parameter, YOLO11 adjusts the network's stride for each batch based on the image sizes, allowing the model to handle rectangular images without forcing them to a single size.
|
||||
Evaluating your YOLO26 model with images of different sizes can help you understand its performance on diverse datasets. Using the `rect=true` validation parameter, YOLO26 adjusts the network's stride for each batch based on the image sizes, allowing the model to handle rectangular images without forcing them to a single size.
|
||||
|
||||
The `imgsz` validation parameter sets the maximum dimension for image resizing, which is 640 by default. You can adjust this based on your dataset's maximum dimensions and the GPU memory available. Even with `imgsz` set, `rect=true` lets the model manage varying image sizes effectively by dynamically adjusting the stride.
|
||||
|
||||
#### Accessing YOLO11 Metrics
|
||||
#### Accessing YOLO26 Metrics
|
||||
|
||||
If you want to get a deeper understanding of your YOLO11 model's performance, you can easily access specific evaluation metrics with a few lines of Python code. The code snippet below will let you load your model, run an evaluation, and print out various metrics that show how well your model is doing.
|
||||
If you want to get a deeper understanding of your YOLO26 model's performance, you can easily access specific evaluation metrics with a few lines of Python code. The code snippet below will let you load your model, run an evaluation, and print out various metrics that show how well your model is doing.
|
||||
|
||||
!!! example "Usage"
|
||||
|
||||
|
|
@ -82,7 +82,7 @@ If you want to get a deeper understanding of your YOLO11 model's performance, yo
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load the model
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Run the evaluation
|
||||
results = model.val(data="coco8.yaml")
|
||||
|
|
@ -112,7 +112,7 @@ If you want to get a deeper understanding of your YOLO11 model's performance, yo
|
|||
print("Recall curve:", results.box.r_curve)
|
||||
```
|
||||
|
||||
The results object also includes speed metrics like preprocess time, inference time, loss, and postprocess time. By analyzing these metrics, you can fine-tune and optimize your YOLO11 model for better performance, making it more effective for your specific use case.
|
||||
The results object also includes speed metrics like preprocess time, inference time, loss, and postprocess time. By analyzing these metrics, you can fine-tune and optimize your YOLO26 model for better performance, making it more effective for your specific use case.
|
||||
|
||||
## How Does Fine-Tuning Work?
|
||||
|
||||
|
|
@ -126,11 +126,11 @@ Fine-tuning a model means paying close attention to several vital parameters and
|
|||
|
||||
Usually, during the initial training [epochs](https://www.ultralytics.com/glossary/epoch), the learning rate starts low and gradually increases to stabilize the training process. However, since your model has already learned some features from the previous dataset, starting with a higher [learning rate](https://www.ultralytics.com/glossary/learning-rate) right away can be more beneficial.
|
||||
|
||||
When evaluating your YOLO11 model, you can set the `warmup_epochs` validation parameter to `warmup_epochs=0` to prevent the learning rate from starting too low. By following this process, the training will continue from the provided weights, adjusting to the nuances of your new data.
|
||||
When evaluating your YOLO26 model, you can set the `warmup_epochs` validation parameter to `warmup_epochs=0` to prevent the learning rate from starting too low. By following this process, the training will continue from the provided weights, adjusting to the nuances of your new data.
|
||||
|
||||
### Image Tiling for Small Objects
|
||||
|
||||
Image tiling can improve detection accuracy for small objects. By dividing larger images into smaller segments, such as splitting 1280x1280 images into multiple 640x640 segments, you maintain the original resolution, and the model can learn from high-resolution fragments. When using YOLO11, make sure to adjust your labels for these new segments correctly.
|
||||
Image tiling can improve detection accuracy for small objects. By dividing larger images into smaller segments, such as splitting 1280x1280 images into multiple 640x640 segments, you maintain the original resolution, and the model can learn from high-resolution fragments. When using YOLO26, make sure to adjust your labels for these new segments correctly.
|
||||
|
||||
## Engage with the Community
|
||||
|
||||
|
|
@ -138,12 +138,12 @@ Sharing your ideas and questions with other [computer vision](https://www.ultral
|
|||
|
||||
### Finding Help and Support
|
||||
|
||||
- **GitHub Issues:** Explore the YOLO11 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to ask questions, report bugs, and suggest features. The community and maintainers are available to assist with any issues you encounter.
|
||||
- **GitHub Issues:** Explore the YOLO26 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to ask questions, report bugs, and suggest features. The community and maintainers are available to assist with any issues you encounter.
|
||||
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to connect with other users and developers, get support, share knowledge, and brainstorm ideas.
|
||||
|
||||
### Official Documentation
|
||||
|
||||
- **Ultralytics YOLO11 Documentation:** Check out the [official YOLO11 documentation](./index.md) for comprehensive guides and valuable insights on various computer vision tasks and projects.
|
||||
- **Ultralytics YOLO26 Documentation:** Check out the [official YOLO26 documentation](./index.md) for comprehensive guides and valuable insights on various computer vision tasks and projects.
|
||||
|
||||
## Final Thoughts
|
||||
|
||||
|
|
@ -151,30 +151,30 @@ Evaluating and fine-tuning your computer vision model are important steps for su
|
|||
|
||||
## FAQ
|
||||
|
||||
### What are the key metrics for evaluating YOLO11 model performance?
|
||||
### What are the key metrics for evaluating YOLO26 model performance?
|
||||
|
||||
To evaluate YOLO11 model performance, important metrics include Confidence Score, Intersection over Union (IoU), and Mean Average Precision (mAP). Confidence Score measures the model's certainty for each detected object class. IoU evaluates how well the predicted bounding box overlaps with the ground truth. Mean Average Precision (mAP) aggregates precision scores across classes, with mAP@.5 and mAP@.5:.95 being two common types for varying IoU thresholds. Learn more about these metrics in our [YOLO11 performance metrics guide](./yolo-performance-metrics.md).
|
||||
To evaluate YOLO26 model performance, important metrics include Confidence Score, Intersection over Union (IoU), and Mean Average Precision (mAP). Confidence Score measures the model's certainty for each detected object class. IoU evaluates how well the predicted bounding box overlaps with the ground truth. Mean Average Precision (mAP) aggregates precision scores across classes, with mAP@.5 and mAP@.5:.95 being two common types for varying IoU thresholds. Learn more about these metrics in our [YOLO26 performance metrics guide](./yolo-performance-metrics.md).
|
||||
|
||||
### How can I fine-tune a pretrained YOLO11 model for my specific dataset?
|
||||
### How can I fine-tune a pretrained YOLO26 model for my specific dataset?
|
||||
|
||||
Fine-tuning a pretrained YOLO11 model involves adjusting its parameters to improve performance on a specific task or dataset. Start by evaluating your model using metrics, then set a higher initial learning rate by adjusting the `warmup_epochs` parameter to 0 for immediate stability. Use parameters like `rect=true` for handling varied image sizes effectively. For more detailed guidance, refer to our section on [fine-tuning YOLO11 models](#how-does-fine-tuning-work).
|
||||
Fine-tuning a pretrained YOLO26 model involves adjusting its parameters to improve performance on a specific task or dataset. Start by evaluating your model using metrics, then set a higher initial learning rate by adjusting the `warmup_epochs` parameter to 0 for immediate stability. Use parameters like `rect=true` for handling varied image sizes effectively. For more detailed guidance, refer to our section on [fine-tuning YOLO26 models](#how-does-fine-tuning-work).
|
||||
|
||||
### How can I handle variable image sizes when evaluating my YOLO11 model?
|
||||
### How can I handle variable image sizes when evaluating my YOLO26 model?
|
||||
|
||||
To handle variable image sizes during evaluation, use the `rect=true` parameter in YOLO11, which adjusts the network's stride for each batch based on image sizes. The `imgsz` parameter sets the maximum dimension for image resizing, defaulting to 640. Adjust `imgsz` to suit your dataset and GPU memory. For more details, visit our [section on handling variable image sizes](#handling-variable-image-sizes).
|
||||
To handle variable image sizes during evaluation, use the `rect=true` parameter in YOLO26, which adjusts the network's stride for each batch based on image sizes. The `imgsz` parameter sets the maximum dimension for image resizing, defaulting to 640. Adjust `imgsz` to suit your dataset and GPU memory. For more details, visit our [section on handling variable image sizes](#handling-variable-image-sizes).
|
||||
|
||||
### What practical steps can I take to improve mean average precision for my YOLO11 model?
|
||||
### What practical steps can I take to improve mean average precision for my YOLO26 model?
|
||||
|
||||
Improving mean average precision (mAP) for a YOLO11 model involves several steps:
|
||||
Improving mean average precision (mAP) for a YOLO26 model involves several steps:
|
||||
|
||||
1. **Tuning Hyperparameters**: Experiment with different learning rates, [batch sizes](https://www.ultralytics.com/glossary/batch-size), and image augmentations.
|
||||
2. **[Data Augmentation](https://www.ultralytics.com/glossary/data-augmentation)**: Use techniques like Mosaic and MixUp to create diverse training samples.
|
||||
3. **Image Tiling**: Split larger images into smaller tiles to improve detection accuracy for small objects.
|
||||
Refer to our detailed guide on [model fine-tuning](#tips-for-fine-tuning-your-model) for specific strategies.
|
||||
|
||||
### How do I access YOLO11 model evaluation metrics in Python?
|
||||
### How do I access YOLO26 model evaluation metrics in Python?
|
||||
|
||||
You can access YOLO11 model evaluation metrics using Python with the following steps:
|
||||
You can access YOLO26 model evaluation metrics using Python with the following steps:
|
||||
|
||||
!!! example "Usage"
|
||||
|
||||
|
|
@ -184,7 +184,7 @@ You can access YOLO11 model evaluation metrics using Python with the following s
|
|||
from ultralytics import YOLO
|
||||
|
||||
# Load the model
|
||||
model = YOLO("yolo11n.pt")
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Run the evaluation
|
||||
results = model.val(data="coco8.yaml")
|
||||
|
|
@ -196,4 +196,4 @@ You can access YOLO11 model evaluation metrics using Python with the following s
|
|||
print("Mean recall:", results.box.mr)
|
||||
```
|
||||
|
||||
Analyzing these metrics helps fine-tune and optimize your YOLO11 model. For a deeper dive, check out our guide on [YOLO11 metrics](../modes/val.md).
|
||||
Analyzing these metrics helps fine-tune and optimize your YOLO26 model. For a deeper dive, check out our guide on [YOLO26 metrics](../modes/val.md).
|
||||
|
|
|
|||
|
|
@ -134,12 +134,12 @@ Joining a community of computer vision enthusiasts can help you solve problems a
|
|||
|
||||
### Community Resources
|
||||
|
||||
- **GitHub Issues:** Check out the [YOLO11 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are highly active and supportive.
|
||||
- **GitHub Issues:** Check out the [YOLO26 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are highly active and supportive.
|
||||
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences.
|
||||
|
||||
### Official Documentation
|
||||
|
||||
- **Ultralytics YOLO11 Documentation:** Visit the [official YOLO11 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
|
||||
- **Ultralytics YOLO26 Documentation:** Visit the [official YOLO26 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
|
||||
|
||||
Using these resources will help you solve challenges and stay up-to-date with the latest trends and practices in the computer vision community.
|
||||
|
||||
|
|
|
|||
|
|
@ -55,22 +55,22 @@ Next, the testing results can be analyzed:
|
|||
- **Error Analysis:** Perform a thorough error analysis to understand the types of errors (e.g., false positives vs. false negatives) and their potential causes.
|
||||
- **Bias and Fairness:** Check for any biases in the model's predictions. Ensure that the model performs equally well across different subsets of the data, especially if it includes sensitive attributes like race, gender, or age.
|
||||
|
||||
## Testing Your YOLO11 Model
|
||||
## Testing Your YOLO26 Model
|
||||
|
||||
To test your YOLO11 model, you can use the validation mode. It's a straightforward way to understand the model's strengths and areas that need improvement. Also, you'll need to format your test dataset correctly for YOLO11. For more details on how to use the validation mode, check out the [Model Validation](../modes/val.md) docs page.
|
||||
To test your YOLO26 model, you can use the validation mode. It's a straightforward way to understand the model's strengths and areas that need improvement. Also, you'll need to format your test dataset correctly for YOLO26. For more details on how to use the validation mode, check out the [Model Validation](../modes/val.md) docs page.
|
||||
|
||||
## Using YOLO11 to Predict on Multiple Test Images
|
||||
## Using YOLO26 to Predict on Multiple Test Images
|
||||
|
||||
If you want to test your trained YOLO11 model on multiple images stored in a folder, you can easily do so in one go. Instead of using the validation mode, which is typically used to evaluate model performance on a validation set and provide detailed metrics, you might just want to see predictions on all images in your test set. For this, you can use the [prediction mode](../modes/predict.md).
|
||||
If you want to test your trained YOLO26 model on multiple images stored in a folder, you can easily do so in one go. Instead of using the validation mode, which is typically used to evaluate model performance on a validation set and provide detailed metrics, you might just want to see predictions on all images in your test set. For this, you can use the [prediction mode](../modes/predict.md).
|
||||
|
||||
### Difference Between Validation and Prediction Modes
|
||||
|
||||
- **[Validation Mode](../modes/val.md):** Used to evaluate the model's performance by comparing predictions against known labels (ground truth). It provides detailed metrics such as accuracy, precision, recall, and F1 score.
|
||||
- **[Prediction Mode](../modes/predict.md):** Used to run the model on new, unseen data to generate predictions. It does not provide detailed performance metrics but allows you to see how the model performs on real-world images.
|
||||
|
||||
## Running YOLO11 Predictions Without Custom Training
|
||||
## Running YOLO26 Predictions Without Custom Training
|
||||
|
||||
If you are interested in testing the basic YOLO11 model to understand whether it can be used for your application without custom training, you can use the prediction mode. While the model is pretrained on datasets like COCO, running predictions on your own dataset can give you a quick sense of how well it might perform in your specific context.
|
||||
If you are interested in testing the basic YOLO26 model to understand whether it can be used for your application without custom training, you can use the prediction mode. While the model is pretrained on datasets like COCO, running predictions on your own dataset can give you a quick sense of how well it might perform in your specific context.
|
||||
|
||||
## Overfitting and [Underfitting](https://www.ultralytics.com/glossary/underfitting) in [Machine Learning](https://www.ultralytics.com/glossary/machine-learning-ml)
|
||||
|
||||
|
|
@ -139,12 +139,12 @@ Becoming part of a community of computer vision enthusiasts can aid in solving p
|
|||
|
||||
### Community Resources
|
||||
|
||||
- **GitHub Issues:** Explore the [YOLO11 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
|
||||
- **GitHub Issues:** Explore the [YOLO26 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
|
||||
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences.
|
||||
|
||||
### Official Documentation
|
||||
|
||||
- **Ultralytics YOLO11 Documentation:** Check out the [official YOLO11 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
|
||||
- **Ultralytics YOLO26 Documentation:** Check out the [official YOLO26 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
|
||||
|
||||
These resources will help you navigate challenges and remain updated on the latest trends and practices within the computer vision community.
|
||||
|
||||
|
|
@ -158,9 +158,9 @@ Building trustworthy computer vision models relies on rigorous model testing. By
|
|||
|
||||
Model evaluation and model testing are distinct steps in a computer vision project. Model evaluation involves using a labeled dataset to compute metrics such as [accuracy](https://www.ultralytics.com/glossary/accuracy), precision, recall, and [F1 score](https://www.ultralytics.com/glossary/f1-score), providing insights into the model's performance with a controlled dataset. Model testing, on the other hand, assesses the model's performance in real-world scenarios by applying it to new, unseen data, ensuring the model's learned behavior aligns with expectations outside the evaluation environment. For a detailed guide, refer to the [steps in a computer vision project](./steps-of-a-cv-project.md).
|
||||
|
||||
### How can I test my Ultralytics YOLO11 model on multiple images?
|
||||
### How can I test my Ultralytics YOLO26 model on multiple images?
|
||||
|
||||
To test your Ultralytics YOLO11 model on multiple images, you can use the [prediction mode](../modes/predict.md). This mode allows you to run the model on new, unseen data to generate predictions without providing detailed metrics. This is ideal for real-world performance testing on larger image sets stored in a folder. For evaluating performance metrics, use the [validation mode](../modes/val.md) instead.
|
||||
To test your Ultralytics YOLO26 model on multiple images, you can use the [prediction mode](../modes/predict.md). This mode allows you to run the model on new, unseen data to generate predictions without providing detailed metrics. This is ideal for real-world performance testing on larger image sets stored in a folder. For evaluating performance metrics, use the [validation mode](../modes/val.md) instead.
|
||||
|
||||
### What should I do if my computer vision model shows signs of overfitting or underfitting?
|
||||
|
||||
|
|
@ -206,6 +206,6 @@ Post-testing, if the model performance meets the project goals, proceed with dep
|
|||
|
||||
Gain insights from the [Model Testing Vs. Model Evaluation](#model-testing-vs-model-evaluation) section to refine and enhance model effectiveness in real-world applications.
|
||||
|
||||
### How do I run YOLO11 predictions without custom training?
|
||||
### How do I run YOLO26 predictions without custom training?
|
||||
|
||||
You can run predictions using the pretrained YOLO11 model on your dataset to see if it suits your application needs. Utilize the [prediction mode](../modes/predict.md) to get a quick sense of performance results without diving into custom training.
|
||||
You can run predictions using the pretrained YOLO26 model on your dataset to see if it suits your application needs. Utilize the [prediction mode](../modes/predict.md) to get a quick sense of performance results without diving into custom training.
|
||||
|
|
|
|||
|
|
@ -54,28 +54,28 @@ Using the maximum batch size supported by your GPU, you can fully take advantage
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Use Batch Inference with Ultralytics YOLO11 | Speed Up Object Detection in Python 🎉
|
||||
<strong>Watch:</strong> How to Use Batch Inference with Ultralytics YOLO26 | Speed Up Object Detection in Python 🎉
|
||||
</p>
|
||||
|
||||
With respect to YOLO11, you can set the `batch_size` parameter in the [training configuration](../modes/train.md) to match your GPU capacity. Also, setting `batch=-1` in your training script will automatically determine the [batch size](https://www.ultralytics.com/glossary/batch-size) that can be efficiently processed based on your device's capabilities. By fine-tuning the batch size, you can make the most of your GPU resources and improve the overall training process.
|
||||
With respect to YOLO26, you can set the `batch_size` parameter in the [training configuration](../modes/train.md) to match your GPU capacity. Also, setting `batch=-1` in your training script will automatically determine the [batch size](https://www.ultralytics.com/glossary/batch-size) that can be efficiently processed based on your device's capabilities. By fine-tuning the batch size, you can make the most of your GPU resources and improve the overall training process.
|
||||
|
||||
### Subset Training
|
||||
|
||||
Subset training is a smart strategy that involves training your model on a smaller set of data that represents the larger dataset. It can save time and resources, especially during initial model development and testing. If you are running short on time or experimenting with different model configurations, subset training is a good option.
|
||||
|
||||
When it comes to YOLO11, you can easily implement subset training by using the `fraction` parameter. This parameter lets you specify what fraction of your dataset to use for training. For example, setting `fraction=0.1` will train your model on 10% of the data. You can use this technique for quick iterations and tuning your model before committing to training a model using a full dataset. Subset training helps you make rapid progress and identify potential issues early on.
|
||||
When it comes to YOLO26, you can easily implement subset training by using the `fraction` parameter. This parameter lets you specify what fraction of your dataset to use for training. For example, setting `fraction=0.1` will train your model on 10% of the data. You can use this technique for quick iterations and tuning your model before committing to training a model using a full dataset. Subset training helps you make rapid progress and identify potential issues early on.
|
||||
|
||||
### Multi-scale Training
|
||||
|
||||
Multiscale training is a technique that improves your model's ability to generalize by training it on images of varying sizes. Your model can learn to detect objects at different scales and distances and become more robust.
|
||||
|
||||
For example, when you train YOLO11, you can enable multiscale training by setting the `scale` parameter. This parameter adjusts the size of training images by a specified factor, simulating objects at different distances. For example, setting `scale=0.5` randomly zooms training images by a factor between 0.5 and 1.5 during training. Configuring this parameter allows your model to experience a variety of image scales and improve its detection capabilities across different object sizes and scenarios.
|
||||
For example, when you train YOLO26, you can enable multiscale training by setting the `scale` parameter. This parameter adjusts the size of training images by a specified factor, simulating objects at different distances. For example, setting `scale=0.5` randomly zooms training images by a factor between 0.5 and 1.5 during training. Configuring this parameter allows your model to experience a variety of image scales and improve its detection capabilities across different object sizes and scenarios.
|
||||
|
||||
### Caching
|
||||
|
||||
Caching is an important technique to improve the efficiency of training machine learning models. By storing preprocessed images in memory, caching reduces the time the GPU spends waiting for data to be loaded from the disk. The model can continuously receive data without delays caused by disk I/O operations.
|
||||
|
||||
Caching can be controlled when training YOLO11 using the `cache` parameter:
|
||||
Caching can be controlled when training YOLO26 using the `cache` parameter:
|
||||
|
||||
- _`cache=True`_: Stores dataset images in RAM, providing the fastest access speed but at the cost of increased memory usage.
|
||||
- _`cache='disk'`_: Stores the images on disk, slower than RAM but faster than loading fresh data each time.
|
||||
|
|
@ -91,19 +91,19 @@ Mixed precision training uses both 16-bit (FP16) and 32-bit (FP32) floating-poin
|
|||
|
||||
To implement mixed precision training, you'll need to modify your training scripts and ensure your hardware (like GPUs) supports it. Many modern [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) frameworks, such as [PyTorch](https://www.ultralytics.com/glossary/pytorch) and [TensorFlow](https://www.ultralytics.com/glossary/tensorflow), offer built-in support for mixed precision.
|
||||
|
||||
Mixed precision training is straightforward when working with YOLO11. You can use the `amp` flag in your training configuration. Setting `amp=True` enables Automatic Mixed Precision (AMP) training. Mixed precision training is a simple yet effective way to optimize your model training process.
|
||||
Mixed precision training is straightforward when working with YOLO26. You can use the `amp` flag in your training configuration. Setting `amp=True` enables Automatic Mixed Precision (AMP) training. Mixed precision training is a simple yet effective way to optimize your model training process.
|
||||
|
||||
### Pretrained Weights
|
||||
|
||||
Using pretrained weights is a smart way to speed up your model's training process. Pretrained weights come from models already trained on large datasets, giving your model a head start. [Transfer learning](https://www.ultralytics.com/glossary/transfer-learning) adapts pretrained models to new, related tasks. Fine-tuning a pretrained model involves starting with these weights and then continuing training on your specific dataset. This method of training results in faster training times and often better performance because the model starts with a solid understanding of basic features.
|
||||
|
||||
The `pretrained` parameter makes transfer learning easy with YOLO11. Setting `pretrained=True` will use default pretrained weights, or you can specify a path to a custom pretrained model. Using pretrained weights and transfer learning effectively boosts your model's capabilities and reduces training costs.
|
||||
The `pretrained` parameter makes transfer learning easy with YOLO26. Setting `pretrained=True` will use default pretrained weights, or you can specify a path to a custom pretrained model. Using pretrained weights and transfer learning effectively boosts your model's capabilities and reduces training costs.
|
||||
|
||||
### Other Techniques to Consider When Handling a Large Dataset
|
||||
|
||||
There are a couple of other techniques to consider when handling a large dataset:
|
||||
|
||||
- **[Learning Rate](https://www.ultralytics.com/glossary/learning-rate) Schedulers**: Implementing learning rate schedulers dynamically adjusts the learning rate during training. A well-tuned learning rate can prevent the model from overshooting minima and improve stability. When training YOLO11, the `lrf` parameter helps manage learning rate scheduling by setting the final learning rate as a fraction of the initial rate.
|
||||
- **[Learning Rate](https://www.ultralytics.com/glossary/learning-rate) Schedulers**: Implementing learning rate schedulers dynamically adjusts the learning rate during training. A well-tuned learning rate can prevent the model from overshooting minima and improve stability. When training YOLO26, the `lrf` parameter helps manage learning rate scheduling by setting the final learning rate as a fraction of the initial rate.
|
||||
- **Distributed Training**: For handling large datasets, distributed training can be a game-changer. You can reduce the training time by spreading the training workload across multiple GPUs or machines. This approach is particularly valuable for enterprise-scale projects with substantial computational resources.
|
||||
|
||||
## The Number of Epochs To Train For
|
||||
|
|
@ -112,7 +112,7 @@ When training a model, an [epoch](https://www.ultralytics.com/glossary/epoch) re
|
|||
|
||||
A common question that comes up is how to determine the number of epochs to train the model for. A good starting point is 300 epochs. If the model overfits early, you can reduce the number of epochs. If [overfitting](https://www.ultralytics.com/glossary/overfitting) does not occur after 300 epochs, you can extend the training to 600, 1200, or more epochs.
|
||||
|
||||
However, the ideal number of epochs can vary based on your dataset's size and project goals. Larger datasets might require more epochs for the model to learn effectively, while smaller datasets might need fewer epochs to avoid overfitting. With respect to YOLO11, you can set the `epochs` parameter in your training script.
|
||||
However, the ideal number of epochs can vary based on your dataset's size and project goals. Larger datasets might require more epochs for the model to learn effectively, while smaller datasets might need fewer epochs to avoid overfitting. With respect to YOLO26, you can set the `epochs` parameter in your training script.
|
||||
|
||||
## Early Stopping
|
||||
|
||||
|
|
@ -124,7 +124,7 @@ The process involves setting a patience parameter that determines how many epoch
|
|||
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/early-stopping-overview.avif" alt="Early Stopping Overview">
|
||||
</p>
|
||||
|
||||
For YOLO11, you can enable early stopping by setting the patience parameter in your training configuration. For example, `patience=5` means training will stop if there's no improvement in validation metrics for 5 consecutive epochs. Using this method ensures the training process remains efficient and achieves optimal performance without excessive computation.
|
||||
For YOLO26, you can enable early stopping by setting the patience parameter in your training configuration. For example, `patience=5` means training will stop if there's no improvement in validation metrics for 5 consecutive epochs. Using this method ensures the training process remains efficient and achieves optimal performance without excessive computation.
|
||||
|
||||
## Choosing Between Cloud and Local Training
|
||||
|
||||
|
|
@ -152,13 +152,13 @@ Different optimizers have various strengths and weaknesses. Let's take a glimpse
|
|||
- Combines the benefits of both SGD with momentum and RMSProp.
|
||||
- Adjusts the learning rate for each parameter based on estimates of the first and second moments of the gradients.
|
||||
- Well-suited for noisy data and sparse gradients.
|
||||
- Efficient and generally requires less tuning, making it a recommended optimizer for YOLO11.
|
||||
- Efficient and generally requires less tuning, making it a recommended optimizer for YOLO26.
|
||||
|
||||
- **RMSProp (Root Mean Square Propagation)**:
|
||||
- Adjusts the learning rate for each parameter by dividing the gradient by a running average of the magnitudes of recent gradients.
|
||||
- Helps in handling the vanishing gradient problem and is effective for [recurrent neural networks](https://www.ultralytics.com/glossary/recurrent-neural-network-rnn).
|
||||
|
||||
For YOLO11, the `optimizer` parameter lets you choose from various optimizers, including SGD, Adam, AdamW, NAdam, RAdam, and RMSProp, or you can set it to `auto` for automatic selection based on model configuration.
|
||||
For YOLO26, the `optimizer` parameter lets you choose from various optimizers, including SGD, Adam, AdamW, NAdam, RAdam, and RMSProp, or you can set it to `auto` for automatic selection based on model configuration.
|
||||
|
||||
## Connecting with the Community
|
||||
|
||||
|
|
@ -166,12 +166,12 @@ Being part of a community of computer vision enthusiasts can help you solve prob
|
|||
|
||||
### Community Resources
|
||||
|
||||
- **GitHub Issues:** Visit the [YOLO11 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
|
||||
- **GitHub Issues:** Visit the [YOLO26 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
|
||||
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences.
|
||||
|
||||
### Official Documentation
|
||||
|
||||
- **Ultralytics YOLO11 Documentation:** Check out the [official YOLO11 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
|
||||
- **Ultralytics YOLO26 Documentation:** Check out the [official YOLO26 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
|
||||
|
||||
Using these resources will help you solve challenges and stay up-to-date with the latest trends and practices in the computer vision community.
|
||||
|
||||
|
|
@ -183,20 +183,20 @@ Training computer vision models involves following good practices, optimizing yo
|
|||
|
||||
### How can I improve GPU utilization when training a large dataset with Ultralytics YOLO?
|
||||
|
||||
To improve GPU utilization, set the `batch_size` parameter in your training configuration to the maximum size supported by your GPU. This ensures that you make full use of the GPU's capabilities, reducing training time. If you encounter memory errors, incrementally reduce the batch size until training runs smoothly. For YOLO11, setting `batch=-1` in your training script will automatically determine the optimal batch size for efficient processing. For further information, refer to the [training configuration](../modes/train.md).
|
||||
To improve GPU utilization, set the `batch_size` parameter in your training configuration to the maximum size supported by your GPU. This ensures that you make full use of the GPU's capabilities, reducing training time. If you encounter memory errors, incrementally reduce the batch size until training runs smoothly. For YOLO26, setting `batch=-1` in your training script will automatically determine the optimal batch size for efficient processing. For further information, refer to the [training configuration](../modes/train.md).
|
||||
|
||||
### What is mixed precision training, and how do I enable it in YOLO11?
|
||||
### What is mixed precision training, and how do I enable it in YOLO26?
|
||||
|
||||
Mixed precision training utilizes both 16-bit (FP16) and 32-bit (FP32) floating-point types to balance computational speed and precision. This approach speeds up training and reduces memory usage without sacrificing model [accuracy](https://www.ultralytics.com/glossary/accuracy). To enable mixed precision training in YOLO11, set the `amp` parameter to `True` in your training configuration. This activates Automatic Mixed Precision (AMP) training. For more details on this optimization technique, see the [training configuration](../modes/train.md).
|
||||
Mixed precision training utilizes both 16-bit (FP16) and 32-bit (FP32) floating-point types to balance computational speed and precision. This approach speeds up training and reduces memory usage without sacrificing model [accuracy](https://www.ultralytics.com/glossary/accuracy). To enable mixed precision training in YOLO26, set the `amp` parameter to `True` in your training configuration. This activates Automatic Mixed Precision (AMP) training. For more details on this optimization technique, see the [training configuration](../modes/train.md).
|
||||
|
||||
### How does multiscale training enhance YOLO11 model performance?
|
||||
### How does multiscale training enhance YOLO26 model performance?
|
||||
|
||||
Multiscale training enhances model performance by training on images of varying sizes, allowing the model to better generalize across different scales and distances. In YOLO11, you can enable multiscale training by setting the `scale` parameter in the training configuration. For example, `scale=0.5` reduces the image size by half, while `scale=2.0` doubles it. This technique simulates objects at different distances, making the model more robust across various scenarios. For settings and more details, check out the [training configuration](../modes/train.md).
|
||||
Multiscale training enhances model performance by training on images of varying sizes, allowing the model to better generalize across different scales and distances. In YOLO26, you can enable multiscale training by setting the `scale` parameter in the training configuration. For example, `scale=0.5` reduces the image size by half, while `scale=2.0` doubles it. This technique simulates objects at different distances, making the model more robust across various scenarios. For settings and more details, check out the [training configuration](../modes/train.md).
|
||||
|
||||
### How can I use pretrained weights to speed up training in YOLO11?
|
||||
### How can I use pretrained weights to speed up training in YOLO26?
|
||||
|
||||
Using pretrained weights can greatly accelerate training and enhance model accuracy by leveraging a model already familiar with foundational visual features. In YOLO11, simply set the `pretrained` parameter to `True` or provide a path to your custom pretrained weights in the training configuration. This method, called transfer learning, allows models trained on large datasets to be effectively adapted to your specific application. Learn more about how to use pretrained weights and their benefits in the [training configuration guide](../modes/train.md).
|
||||
Using pretrained weights can greatly accelerate training and enhance model accuracy by leveraging a model already familiar with foundational visual features. In YOLO26, simply set the `pretrained` parameter to `True` or provide a path to your custom pretrained weights in the training configuration. This method, called transfer learning, allows models trained on large datasets to be effectively adapted to your specific application. Learn more about how to use pretrained weights and their benefits in the [training configuration guide](../modes/train.md).
|
||||
|
||||
### What is the recommended number of epochs for training a model, and how do I set this in YOLO11?
|
||||
### What is the recommended number of epochs for training a model, and how do I set this in YOLO26?
|
||||
|
||||
The number of epochs refers to the complete passes through the training dataset during model training. A typical starting point is 300 epochs. If your model overfits early, you can reduce the number. Alternatively, if overfitting isn't observed, you might extend training to 600, 1200, or more epochs. To set this in YOLO11, use the `epochs` parameter in your training script. For additional advice on determining the ideal number of epochs, refer to this section on [number of epochs](#the-number-of-epochs-to-train-for).
|
||||
The number of epochs refers to the complete passes through the training dataset during model training. A typical starting point is 300 epochs. If your model overfits early, you can reduce the number. Alternatively, if overfitting isn't observed, you might extend training to 600, 1200, or more epochs. To set this in YOLO26, use the `epochs` parameter in your training script. For additional advice on determining the ideal number of epochs, refer to this section on [number of epochs](#the-number-of-epochs-to-train-for).
|
||||
|
|
|
|||
|
|
@ -36,7 +36,7 @@ kpt_shape: [17, 3] # pose models only
|
|||
|
||||
!!! tip "Reduce redundancy with `scales`"
|
||||
|
||||
The `scales` parameter lets you generate multiple model sizes from a single base YAML. For instance, when you load `yolo11n.yaml`, Ultralytics reads the base `yolo11.yaml` and applies the `n` scaling factors (`depth=0.50`, `width=0.25`) to build the nano variant.
|
||||
The `scales` parameter lets you generate multiple model sizes from a single base YAML. For instance, when you load `yolo26n.yaml`, Ultralytics reads the base `yolo26.yaml` and applies the `n` scaling factors (`depth=0.50`, `width=0.25`) to build the nano variant.
|
||||
|
||||
!!! note "`nc` and `kpt_shape` are dataset-dependent"
|
||||
|
||||
|
|
@ -451,7 +451,7 @@ Yes. You can use any supported module, including TorchVision backbones, or defin
|
|||
|
||||
### How do I scale my model for different sizes (nano, small, medium, etc.)?
|
||||
|
||||
Use the [`scales` section](#parameters-section) in your YAML to define scaling factors for depth, width, and max channels. The model will automatically apply these when you load the base YAML file with the scale appended to the filename (e.g., `yolo11n.yaml`).
|
||||
Use the [`scales` section](#parameters-section) in your YAML to define scaling factors for depth, width, and max channels. The model will automatically apply these when you load the base YAML file with the scale appended to the filename (e.g., `yolo26n.yaml`).
|
||||
|
||||
### What does the `[from, repeats, module, args]` format mean?
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,12 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn to deploy Ultralytics YOLO11 on NVIDIA DGX Spark with our detailed guide. Explore performance benchmarks and maximize AI capabilities on this compact desktop AI supercomputer.
|
||||
keywords: Ultralytics, YOLO11, NVIDIA DGX Spark, AI deployment, performance benchmarks, deep learning, TensorRT, computer vision, GB10 Grace Blackwell
|
||||
description: Learn to deploy Ultralytics YOLO26 on NVIDIA DGX Spark with our detailed guide. Explore performance benchmarks and maximize AI capabilities on this compact desktop AI supercomputer.
|
||||
keywords: Ultralytics, YOLO26, NVIDIA DGX Spark, AI deployment, performance benchmarks, deep learning, TensorRT, computer vision, GB10 Grace Blackwell
|
||||
---
|
||||
|
||||
# Quick Start Guide: NVIDIA DGX Spark with Ultralytics YOLO11
|
||||
# Quick Start Guide: NVIDIA DGX Spark with Ultralytics YOLO26
|
||||
|
||||
This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLO11 on [NVIDIA DGX Spark](https://www.nvidia.com/en-us/products/workstations/dgx-spark/), NVIDIA's compact desktop AI supercomputer. Additionally, it showcases performance benchmarks to demonstrate the capabilities of YOLO11 on this powerful system.
|
||||
This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLO26 on [NVIDIA DGX Spark](https://www.nvidia.com/en-us/products/workstations/dgx-spark/), NVIDIA's compact desktop AI supercomputer. Additionally, it showcases performance benchmarks to demonstrate the capabilities of YOLO26 on this powerful system.
|
||||
|
||||
<p align="center">
|
||||
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/nvidia-dgx-spark.avif" alt="NVIDIA DGX Spark">
|
||||
|
|
@ -67,7 +67,7 @@ DGX Spark comes with a built-in [DGX Dashboard](https://docs.nvidia.com/dgx/dgx-
|
|||
|
||||
```bash
|
||||
# Open an SSH tunnel
|
||||
ssh -L 11000:localhost:11000 <username>@<IP or spark-abcd.local>
|
||||
ssh -L 11000:localhost:11000 username@spark-abcd.local
|
||||
|
||||
# Then open in browser
|
||||
# http://localhost:11000
|
||||
|
|
@ -83,7 +83,7 @@ DGX Spark comes with a built-in [DGX Dashboard](https://docs.nvidia.com/dgx/dgx-
|
|||
|
||||
## Quick Start with Docker
|
||||
|
||||
The fastest way to get started with Ultralytics YOLO11 on NVIDIA DGX Spark is to run with pre-built docker images. The same Docker image that supports Jetson AGX Thor (JetPack 7.0) works on DGX Spark with DGX OS.
|
||||
The fastest way to get started with Ultralytics YOLO26 on NVIDIA DGX Spark is to run with pre-built docker images. The same Docker image that supports Jetson AGX Thor (JetPack 7.0) works on DGX Spark with DGX OS.
|
||||
|
||||
```bash
|
||||
t=ultralytics/ultralytics:latest-nvidia-arm64
|
||||
|
|
@ -155,7 +155,7 @@ Among all the model export formats supported by Ultralytics, TensorRT offers the
|
|||
|
||||
### Convert Model to TensorRT and Run Inference
|
||||
|
||||
The YOLO11n model in PyTorch format is converted to TensorRT to run inference with the exported model.
|
||||
The YOLO26n model in PyTorch format is converted to TensorRT to run inference with the exported model.
|
||||
|
||||
!!! example
|
||||
|
||||
|
|
@ -164,14 +164,14 @@ The YOLO11n model in PyTorch format is converted to TensorRT to run inference wi
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a YOLO11n PyTorch model
|
||||
model = YOLO("yolo11n.pt")
|
||||
# Load a YOLO26n PyTorch model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Export the model to TensorRT
|
||||
model.export(format="engine") # creates 'yolo11n.engine'
|
||||
model.export(format="engine") # creates 'yolo26n.engine'
|
||||
|
||||
# Load the exported TensorRT model
|
||||
trt_model = YOLO("yolo11n.engine")
|
||||
trt_model = YOLO("yolo26n.engine")
|
||||
|
||||
# Run inference
|
||||
results = trt_model("https://ultralytics.com/images/bus.jpg")
|
||||
|
|
@ -180,11 +180,11 @@ The YOLO11n model in PyTorch format is converted to TensorRT to run inference wi
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Export a YOLO11n PyTorch model to TensorRT format
|
||||
yolo export model=yolo11n.pt format=engine # creates 'yolo11n.engine'
|
||||
# Export a YOLO26n PyTorch model to TensorRT format
|
||||
yolo export model=yolo26n.pt format=engine # creates 'yolo26n.engine'
|
||||
|
||||
# Run inference with the exported model
|
||||
yolo predict model=yolo11n.engine source='https://ultralytics.com/images/bus.jpg'
|
||||
yolo predict model=yolo26n.engine source='https://ultralytics.com/images/bus.jpg'
|
||||
```
|
||||
|
||||
!!! note
|
||||
|
|
@ -304,25 +304,25 @@ To reproduce the above Ultralytics benchmarks on all export [formats](../modes/e
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a YOLO11n PyTorch model
|
||||
model = YOLO("yolo11n.pt")
|
||||
# Load a YOLO26n PyTorch model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Benchmark YOLO11n speed and accuracy on the COCO128 dataset for all export formats
|
||||
# Benchmark YOLO26n speed and accuracy on the COCO128 dataset for all export formats
|
||||
results = model.benchmark(data="coco128.yaml", imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Benchmark YOLO11n speed and accuracy on the COCO128 dataset for all export formats
|
||||
yolo benchmark model=yolo11n.pt data=coco128.yaml imgsz=640
|
||||
# Benchmark YOLO26n speed and accuracy on the COCO128 dataset for all export formats
|
||||
yolo benchmark model=yolo26n.pt data=coco128.yaml imgsz=640
|
||||
```
|
||||
|
||||
Note that benchmarking results might vary based on the exact hardware and software configuration of a system, as well as the current workload of the system at the time the benchmarks are run. For the most reliable results, use a dataset with a large number of images, e.g., `data='coco.yaml'` (5000 val images).
|
||||
|
||||
## Best Practices for NVIDIA DGX Spark
|
||||
|
||||
When using NVIDIA DGX Spark, there are a couple of best practices to follow in order to enable maximum performance running YOLO11.
|
||||
When using NVIDIA DGX Spark, there are a couple of best practices to follow in order to enable maximum performance running YOLO26.
|
||||
|
||||
1. **Monitor System Performance**
|
||||
|
||||
|
|
@ -339,7 +339,7 @@ When using NVIDIA DGX Spark, there are a couple of best practices to follow in o
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo11n.engine")
|
||||
model = YOLO("yolo26n.engine")
|
||||
results = model.predict(source="path/to/images", batch=16)
|
||||
```
|
||||
|
||||
|
|
@ -348,8 +348,8 @@ When using NVIDIA DGX Spark, there are a couple of best practices to follow in o
|
|||
For best performance, export models with FP16 or INT8 precision:
|
||||
|
||||
```bash
|
||||
yolo export model=yolo11n.pt format=engine half=True # FP16
|
||||
yolo export model=yolo11n.pt format=engine int8=True # INT8
|
||||
yolo export model=yolo26n.pt format=engine half=True # FP16
|
||||
yolo export model=yolo26n.pt format=engine int8=True # INT8
|
||||
```
|
||||
|
||||
## System Updates (Founders Edition)
|
||||
|
|
@ -382,23 +382,23 @@ sudo reboot
|
|||
|
||||
## Next Steps
|
||||
|
||||
For further learning and support, see the [Ultralytics YOLO11 Docs](../index.md).
|
||||
For further learning and support, see the [Ultralytics YOLO26 Docs](../index.md).
|
||||
|
||||
## FAQ
|
||||
|
||||
### How do I deploy Ultralytics YOLO11 on NVIDIA DGX Spark?
|
||||
### How do I deploy Ultralytics YOLO26 on NVIDIA DGX Spark?
|
||||
|
||||
Deploying Ultralytics YOLO11 on NVIDIA DGX Spark is straightforward. You can use the pre-built Docker image for quick setup or manually install the required packages. Detailed steps for each approach can be found in sections [Quick Start with Docker](#quick-start-with-docker) and [Start with Native Installation](#start-with-native-installation).
|
||||
Deploying Ultralytics YOLO26 on NVIDIA DGX Spark is straightforward. You can use the pre-built Docker image for quick setup or manually install the required packages. Detailed steps for each approach can be found in sections [Quick Start with Docker](#quick-start-with-docker) and [Start with Native Installation](#start-with-native-installation).
|
||||
|
||||
### What performance can I expect from YOLO11 on NVIDIA DGX Spark?
|
||||
### What performance can I expect from YOLO26 on NVIDIA DGX Spark?
|
||||
|
||||
YOLO11 models deliver excellent performance on DGX Spark thanks to the GB10 Grace Blackwell Superchip. The TensorRT format provides the best inference performance. Check the [Detailed Comparison Table](#detailed-comparison-table) section for specific benchmark results across different model sizes and formats.
|
||||
YOLO26 models deliver excellent performance on DGX Spark thanks to the GB10 Grace Blackwell Superchip. The TensorRT format provides the best inference performance. Check the [Detailed Comparison Table](#detailed-comparison-table) section for specific benchmark results across different model sizes and formats.
|
||||
|
||||
### Why should I use TensorRT for YOLO11 on DGX Spark?
|
||||
### Why should I use TensorRT for YOLO26 on DGX Spark?
|
||||
|
||||
TensorRT is highly recommended for deploying YOLO11 models on DGX Spark due to its optimal performance. It accelerates inference by leveraging the Blackwell GPU capabilities, ensuring maximum efficiency and speed. Learn more in the [Use TensorRT on NVIDIA DGX Spark](#use-tensorrt-on-nvidia-dgx-spark) section.
|
||||
TensorRT is highly recommended for deploying YOLO26 models on DGX Spark due to its optimal performance. It accelerates inference by leveraging the Blackwell GPU capabilities, ensuring maximum efficiency and speed. Learn more in the [Use TensorRT on NVIDIA DGX Spark](#use-tensorrt-on-nvidia-dgx-spark) section.
|
||||
|
||||
### How does DGX Spark compare to Jetson devices for YOLO11?
|
||||
### How does DGX Spark compare to Jetson devices for YOLO26?
|
||||
|
||||
DGX Spark offers significantly more compute power than Jetson devices with up to 1 PFLOP of AI performance and 128GB unified memory, compared to Jetson AGX Thor's 2070 TFLOPS and 128GB memory. DGX Spark is designed as a desktop AI supercomputer, while Jetson devices are embedded systems optimized for edge deployment.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,12 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn to deploy Ultralytics YOLO11 on NVIDIA Jetson devices with our detailed guide. Explore performance benchmarks and maximize AI capabilities.
|
||||
keywords: Ultralytics, YOLO11, NVIDIA Jetson, JetPack, AI deployment, performance benchmarks, embedded systems, deep learning, TensorRT, computer vision
|
||||
description: Learn to deploy Ultralytics YOLO26 on NVIDIA Jetson devices with our detailed guide. Explore performance benchmarks and maximize AI capabilities.
|
||||
keywords: Ultralytics, YOLO26, NVIDIA Jetson, JetPack, AI deployment, performance benchmarks, embedded systems, deep learning, TensorRT, computer vision
|
||||
---
|
||||
|
||||
# Quick Start Guide: NVIDIA Jetson with Ultralytics YOLO11
|
||||
# Quick Start Guide: NVIDIA Jetson with Ultralytics YOLO26
|
||||
|
||||
This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLO11 on [NVIDIA Jetson](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) devices. Additionally, it showcases performance benchmarks to demonstrate the capabilities of YOLO11 on these small and powerful devices.
|
||||
This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLO26 on [NVIDIA Jetson](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) devices. Additionally, it showcases performance benchmarks to demonstrate the capabilities of YOLO26 on these small and powerful devices.
|
||||
|
||||
!!! tip "New product support"
|
||||
|
||||
|
|
@ -20,7 +20,7 @@ This comprehensive guide provides a detailed walkthrough for deploying Ultralyti
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to use Ultralytics YOLO11 on NVIDIA Jetson Devices
|
||||
<strong>Watch:</strong> How to use Ultralytics YOLO26 on NVIDIA Jetson Devices
|
||||
</p>
|
||||
|
||||
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/nvidia-jetson-ecosystem.avif" alt="NVIDIA Jetson Ecosystem">
|
||||
|
|
@ -83,7 +83,7 @@ The below table highlights NVIDIA JetPack versions supported by different NVIDIA
|
|||
|
||||
## Quick Start with Docker
|
||||
|
||||
The fastest way to get started with Ultralytics YOLO11 on NVIDIA Jetson is to run with pre-built docker images for Jetson. Refer to the table above and choose the JetPack version according to the Jetson device you own.
|
||||
The fastest way to get started with Ultralytics YOLO26 on NVIDIA Jetson is to run with pre-built docker images for Jetson. Refer to the table above and choose the JetPack version according to the Jetson device you own.
|
||||
|
||||
=== "JetPack 4"
|
||||
|
||||
|
|
@ -303,7 +303,7 @@ Among all the model export formats supported by Ultralytics, TensorRT offers the
|
|||
|
||||
### Convert Model to TensorRT and Run Inference
|
||||
|
||||
The YOLO11n model in PyTorch format is converted to TensorRT to run inference with the exported model.
|
||||
The YOLO26n model in PyTorch format is converted to TensorRT to run inference with the exported model.
|
||||
|
||||
!!! example
|
||||
|
||||
|
|
@ -312,14 +312,14 @@ The YOLO11n model in PyTorch format is converted to TensorRT to run inference wi
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a YOLO11n PyTorch model
|
||||
model = YOLO("yolo11n.pt")
|
||||
# Load a YOLO26n PyTorch model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Export the model to TensorRT
|
||||
model.export(format="engine") # creates 'yolo11n.engine'
|
||||
model.export(format="engine") # creates 'yolo26n.engine'
|
||||
|
||||
# Load the exported TensorRT model
|
||||
trt_model = YOLO("yolo11n.engine")
|
||||
trt_model = YOLO("yolo26n.engine")
|
||||
|
||||
# Run inference
|
||||
results = trt_model("https://ultralytics.com/images/bus.jpg")
|
||||
|
|
@ -328,11 +328,11 @@ The YOLO11n model in PyTorch format is converted to TensorRT to run inference wi
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Export a YOLO11n PyTorch model to TensorRT format
|
||||
yolo export model=yolo11n.pt format=engine # creates 'yolo11n.engine'
|
||||
# Export a YOLO26n PyTorch model to TensorRT format
|
||||
yolo export model=yolo26n.pt format=engine # creates 'yolo26n.engine'
|
||||
|
||||
# Run inference with the exported model
|
||||
yolo predict model=yolo11n.engine source='https://ultralytics.com/images/bus.jpg'
|
||||
yolo predict model=yolo26n.engine source='https://ultralytics.com/images/bus.jpg'
|
||||
```
|
||||
|
||||
!!! note
|
||||
|
|
@ -360,14 +360,14 @@ The following Jetson devices are equipped with DLA hardware:
|
|||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a YOLO11n PyTorch model
|
||||
model = YOLO("yolo11n.pt")
|
||||
# Load a YOLO26n PyTorch model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Export the model to TensorRT with DLA enabled (only works with FP16 or INT8)
|
||||
model.export(format="engine", device="dla:0", half=True) # dla:0 or dla:1 corresponds to the DLA cores
|
||||
|
||||
# Load the exported TensorRT model
|
||||
trt_model = YOLO("yolo11n.engine")
|
||||
trt_model = YOLO("yolo26n.engine")
|
||||
|
||||
# Run inference
|
||||
results = trt_model("https://ultralytics.com/images/bus.jpg")
|
||||
|
|
@ -376,12 +376,12 @@ The following Jetson devices are equipped with DLA hardware:
|
|||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Export a YOLO11n PyTorch model to TensorRT format with DLA enabled (only works with FP16 or INT8)
|
||||
# Export a YOLO26n PyTorch model to TensorRT format with DLA enabled (only works with FP16 or INT8)
|
||||
# Once DLA core number is specified at export, it will use the same core at inference
|
||||
yolo export model=yolo11n.pt format=engine device="dla:0" half=True # dla:0 or dla:1 corresponds to the DLA cores
|
||||
yolo export model=yolo26n.pt format=engine device="dla:0" half=True # dla:0 or dla:1 corresponds to the DLA cores
|
||||
|
||||
# Run inference with the exported model on the DLA
|
||||
yolo predict model=yolo11n.engine source='https://ultralytics.com/images/bus.jpg'
|
||||
yolo predict model=yolo26n.engine source='https://ultralytics.com/images/bus.jpg'
|
||||
```
|
||||
|
||||
!!! note
|
||||
|
|
@ -844,7 +844,7 @@ To reproduce the above Ultralytics benchmarks on all export [formats](../modes/e
|
|||
|
||||
## Best Practices when using NVIDIA Jetson
|
||||
|
||||
When using NVIDIA Jetson, there are a couple of best practices to follow in order to enable maximum performance on the NVIDIA Jetson running YOLO11.
|
||||
When using NVIDIA Jetson, there are a couple of best practices to follow in order to enable maximum performance on the NVIDIA Jetson running YOLO26.
|
||||
|
||||
1. Enable MAX Power Mode
|
||||
|
||||
|
|
@ -877,29 +877,29 @@ When using NVIDIA Jetson, there are a couple of best practices to follow in orde
|
|||
|
||||
## Next Steps
|
||||
|
||||
For further learning and support, see the [Ultralytics YOLO11 Docs](../index.md).
|
||||
For further learning and support, see the [Ultralytics YOLO26 Docs](../index.md).
|
||||
|
||||
## FAQ
|
||||
|
||||
### How do I deploy Ultralytics YOLO11 on NVIDIA Jetson devices?
|
||||
### How do I deploy Ultralytics YOLO26 on NVIDIA Jetson devices?
|
||||
|
||||
Deploying Ultralytics YOLO11 on NVIDIA Jetson devices is a straightforward process. First, flash your Jetson device with the NVIDIA JetPack SDK. Then, either use a pre-built Docker image for quick setup or manually install the required packages. Detailed steps for each approach can be found in sections [Quick Start with Docker](#quick-start-with-docker) and [Start with Native Installation](#start-with-native-installation).
|
||||
Deploying Ultralytics YOLO26 on NVIDIA Jetson devices is a straightforward process. First, flash your Jetson device with the NVIDIA JetPack SDK. Then, either use a pre-built Docker image for quick setup or manually install the required packages. Detailed steps for each approach can be found in sections [Quick Start with Docker](#quick-start-with-docker) and [Start with Native Installation](#start-with-native-installation).
|
||||
|
||||
### What performance benchmarks can I expect from YOLO11 models on NVIDIA Jetson devices?
|
||||
|
||||
YOLO11 models have been benchmarked on various NVIDIA Jetson devices showing significant performance improvements. For example, the TensorRT format delivers the best inference performance. The table in the [Detailed Comparison Tables](#detailed-comparison-tables) section provides a comprehensive view of performance metrics like mAP50-95 and inference time across different model formats.
|
||||
|
||||
### Why should I use TensorRT for deploying YOLO11 on NVIDIA Jetson?
|
||||
### Why should I use TensorRT for deploying YOLO26 on NVIDIA Jetson?
|
||||
|
||||
TensorRT is highly recommended for deploying YOLO11 models on NVIDIA Jetson due to its optimal performance. It accelerates inference by leveraging the Jetson's GPU capabilities, ensuring maximum efficiency and speed. Learn more about how to convert to TensorRT and run inference in the [Use TensorRT on NVIDIA Jetson](#use-tensorrt-on-nvidia-jetson) section.
|
||||
TensorRT is highly recommended for deploying YOLO26 models on NVIDIA Jetson due to its optimal performance. It accelerates inference by leveraging the Jetson's GPU capabilities, ensuring maximum efficiency and speed. Learn more about how to convert to TensorRT and run inference in the [Use TensorRT on NVIDIA Jetson](#use-tensorrt-on-nvidia-jetson) section.
|
||||
|
||||
### How can I install PyTorch and Torchvision on NVIDIA Jetson?
|
||||
|
||||
To install PyTorch and Torchvision on NVIDIA Jetson, first uninstall any existing versions that may have been installed via pip. Then, manually install the compatible PyTorch and Torchvision versions for the Jetson's ARM64 architecture. Detailed instructions for this process are provided in the [Install PyTorch and Torchvision](#install-pytorch-and-torchvision) section.
|
||||
|
||||
### What are the best practices for maximizing performance on NVIDIA Jetson when using YOLO11?
|
||||
### What are the best practices for maximizing performance on NVIDIA Jetson when using YOLO26?
|
||||
|
||||
To maximize performance on NVIDIA Jetson with YOLO11, follow these best practices:
|
||||
To maximize performance on NVIDIA Jetson with YOLO26, follow these best practices:
|
||||
|
||||
1. Enable MAX Power Mode to utilize all CPU and GPU cores.
|
||||
2. Enable Jetson Clocks to run all cores at their maximum frequency.
|
||||
|
|
|
|||
|
|
@ -1,14 +1,14 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn how to use Ultralytics YOLO11 for real-time object blurring to enhance privacy and focus in your images and videos.
|
||||
keywords: YOLO11, object blurring, real-time processing, privacy protection, image manipulation, video editing, Ultralytics
|
||||
description: Learn how to use Ultralytics YOLO26 for real-time object blurring to enhance privacy and focus in your images and videos.
|
||||
keywords: YOLO26, object blurring, real-time processing, privacy protection, image manipulation, video editing, Ultralytics
|
||||
---
|
||||
|
||||
# Object Blurring using Ultralytics YOLO11 🚀
|
||||
# Object Blurring using Ultralytics YOLO26 🚀
|
||||
|
||||
## What is Object Blurring?
|
||||
|
||||
Object blurring with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) involves applying a blurring effect to specific detected objects in an image or video. This can be achieved using the YOLO11 model capabilities to identify and manipulate objects within a given scene.
|
||||
Object blurring with [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics/) involves applying a blurring effect to specific detected objects in an image or video. This can be achieved using the YOLO26 model capabilities to identify and manipulate objects within a given scene.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
|
|
@ -18,14 +18,14 @@ Object blurring with [Ultralytics YOLO11](https://github.com/ultralytics/ultraly
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Object Blurring using Ultralytics YOLO11
|
||||
<strong>Watch:</strong> Object Blurring using Ultralytics YOLO26
|
||||
</p>
|
||||
|
||||
## Advantages of Object Blurring
|
||||
|
||||
- **Privacy Protection**: Object blurring is an effective tool for safeguarding privacy by concealing sensitive or personally identifiable information in images or videos.
|
||||
- **Selective Focus**: YOLO11 allows for selective blurring, enabling users to target specific objects, ensuring a balance between privacy and retaining relevant visual information.
|
||||
- **Real-time Processing**: YOLO11's efficiency enables object blurring in real-time, making it suitable for applications requiring on-the-fly privacy enhancements in dynamic environments.
|
||||
- **Selective Focus**: YOLO26 allows for selective blurring, enabling users to target specific objects, ensuring a balance between privacy and retaining relevant visual information.
|
||||
- **Real-time Processing**: YOLO26's efficiency enables object blurring in real-time, making it suitable for applications requiring on-the-fly privacy enhancements in dynamic environments.
|
||||
- **Regulatory Compliance**: Helps organizations comply with data protection regulations like GDPR by anonymizing identifiable information in visual content.
|
||||
- **Content Moderation**: Useful for blurring inappropriate or sensitive content in media platforms while preserving the overall context.
|
||||
|
||||
|
|
@ -61,7 +61,7 @@ Object blurring with [Ultralytics YOLO11](https://github.com/ultralytics/ultraly
|
|||
# Initialize object blurrer
|
||||
blurrer = solutions.ObjectBlurrer(
|
||||
show=True, # display the output
|
||||
model="yolo11n.pt", # model for object blurring, e.g., yolo11m.pt
|
||||
model="yolo26n.pt", # model for object blurring, e.g., yolo26m.pt
|
||||
# line_width=2, # width of bounding box.
|
||||
# classes=[0, 2], # blur specific classes, e.g., person and car with the COCO pretrained model.
|
||||
# blur_ratio=0.5, # adjust percentage of blur intensity, value in range 0.1 - 1.0
|
||||
|
|
@ -107,29 +107,29 @@ Moreover, the following visualization arguments can be used:
|
|||
|
||||
### Privacy Protection in Surveillance
|
||||
|
||||
[Security cameras](https://www.ultralytics.com/blog/the-cutting-edge-world-of-ai-security-cameras) and surveillance systems can use YOLO11 to automatically blur faces, license plates, or other identifying information while still capturing important activity. This helps maintain security while respecting privacy rights in public spaces.
|
||||
[Security cameras](https://www.ultralytics.com/blog/the-cutting-edge-world-of-ai-security-cameras) and surveillance systems can use YOLO26 to automatically blur faces, license plates, or other identifying information while still capturing important activity. This helps maintain security while respecting privacy rights in public spaces.
|
||||
|
||||
### Healthcare Data Anonymization
|
||||
|
||||
In [medical imaging](https://www.ultralytics.com/blog/ai-and-radiology-a-new-era-of-precision-and-efficiency), patient information often appears in scans or photos. YOLO11 can detect and blur this information to comply with regulations like HIPAA when sharing medical data for research or educational purposes.
|
||||
In [medical imaging](https://www.ultralytics.com/blog/ai-and-radiology-a-new-era-of-precision-and-efficiency), patient information often appears in scans or photos. YOLO26 can detect and blur this information to comply with regulations like HIPAA when sharing medical data for research or educational purposes.
|
||||
|
||||
### Document Redaction
|
||||
|
||||
When sharing documents that contain sensitive information, YOLO11 can automatically detect and blur specific elements like signatures, account numbers, or personal details, streamlining the redaction process while maintaining document integrity.
|
||||
When sharing documents that contain sensitive information, YOLO26 can automatically detect and blur specific elements like signatures, account numbers, or personal details, streamlining the redaction process while maintaining document integrity.
|
||||
|
||||
### Media and Content Creation
|
||||
|
||||
Content creators can use YOLO11 to blur brand logos, copyrighted material, or inappropriate content in videos and images, helping avoid legal issues while preserving the overall content quality.
|
||||
Content creators can use YOLO26 to blur brand logos, copyrighted material, or inappropriate content in videos and images, helping avoid legal issues while preserving the overall content quality.
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is object blurring with Ultralytics YOLO11?
|
||||
### What is object blurring with Ultralytics YOLO26?
|
||||
|
||||
Object blurring with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) involves automatically detecting and applying a blurring effect to specific objects in images or videos. This technique enhances privacy by concealing sensitive information while retaining relevant visual data. YOLO11's real-time processing capabilities make it suitable for applications requiring immediate privacy protection and selective focus adjustments.
|
||||
Object blurring with [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics/) involves automatically detecting and applying a blurring effect to specific objects in images or videos. This technique enhances privacy by concealing sensitive information while retaining relevant visual data. YOLO26's real-time processing capabilities make it suitable for applications requiring immediate privacy protection and selective focus adjustments.
|
||||
|
||||
### How can I implement real-time object blurring using YOLO11?
|
||||
### How can I implement real-time object blurring using YOLO26?
|
||||
|
||||
To implement real-time object blurring with YOLO11, follow the provided Python example. This involves using YOLO11 for [object detection](https://www.ultralytics.com/glossary/object-detection) and OpenCV for applying the blur effect. Here's a simplified version:
|
||||
To implement real-time object blurring with YOLO26, follow the provided Python example. This involves using YOLO26 for [object detection](https://www.ultralytics.com/glossary/object-detection) and OpenCV for applying the blur effect. Here's a simplified version:
|
||||
|
||||
```python
|
||||
import cv2
|
||||
|
|
@ -146,7 +146,7 @@ video_writer = cv2.VideoWriter("object_blurring_output.avi", cv2.VideoWriter_fou
|
|||
# Init ObjectBlurrer
|
||||
blurrer = solutions.ObjectBlurrer(
|
||||
show=True, # display the output
|
||||
model="yolo11n.pt", # model="yolo11n-obb.pt" for object blurring using YOLO11 OBB model.
|
||||
model="yolo26n.pt", # model="yolo26n-obb.pt" for object blurring using YOLO26 OBB model.
|
||||
blur_ratio=0.5, # set blur percentage, e.g., 0.7 for 70% blur on detected objects
|
||||
# line_width=2, # width of bounding box.
|
||||
# classes=[0, 2], # count specific classes, e.g., person and car with the COCO pretrained model.
|
||||
|
|
@ -166,9 +166,9 @@ video_writer.release()
|
|||
cv2.destroyAllWindows()
|
||||
```
|
||||
|
||||
### What are the benefits of using Ultralytics YOLO11 for object blurring?
|
||||
### What are the benefits of using Ultralytics YOLO26 for object blurring?
|
||||
|
||||
Ultralytics YOLO11 offers several advantages for object blurring:
|
||||
Ultralytics YOLO26 offers several advantages for object blurring:
|
||||
|
||||
- **Privacy Protection**: Effectively obscure sensitive or identifiable information.
|
||||
- **Selective Focus**: Target specific objects for blurring, maintaining essential visual content.
|
||||
|
|
@ -178,10 +178,10 @@ Ultralytics YOLO11 offers several advantages for object blurring:
|
|||
|
||||
For more detailed applications, check the [advantages of object blurring section](#advantages-of-object-blurring).
|
||||
|
||||
### Can I use Ultralytics YOLO11 to blur faces in a video for privacy reasons?
|
||||
### Can I use Ultralytics YOLO26 to blur faces in a video for privacy reasons?
|
||||
|
||||
Yes, Ultralytics YOLO11 can be configured to detect and blur faces in videos to protect privacy. By training or using a pretrained model to specifically recognize faces, the detection results can be processed with [OpenCV](https://www.ultralytics.com/glossary/opencv) to apply a blur effect. Refer to our guide on [object detection with YOLO11](https://docs.ultralytics.com/models/yolo11/) and modify the code to target face detection.
|
||||
Yes, Ultralytics YOLO26 can be configured to detect and blur faces in videos to protect privacy. By training or using a pretrained model to specifically recognize faces, the detection results can be processed with [OpenCV](https://www.ultralytics.com/glossary/opencv) to apply a blur effect. Refer to our guide on [object detection with YOLO26](https://docs.ultralytics.com/models/yolo26/) and modify the code to target face detection.
|
||||
|
||||
### How does YOLO11 compare to other object detection models like Faster R-CNN for object blurring?
|
||||
### How does YOLO26 compare to other object detection models like Faster R-CNN for object blurring?
|
||||
|
||||
Ultralytics YOLO11 typically outperforms models like Faster R-CNN in terms of speed, making it more suitable for real-time applications. While both models offer accurate detection, YOLO11's architecture is optimized for rapid inference, which is critical for tasks like real-time object blurring. Learn more about the technical differences and performance metrics in our [YOLO11 documentation](https://docs.ultralytics.com/models/yolo11/).
|
||||
Ultralytics YOLO26 typically outperforms models like Faster R-CNN in terms of speed, making it more suitable for real-time applications. While both models offer accurate detection, YOLO26's architecture is optimized for rapid inference, which is critical for tasks like real-time object blurring. Learn more about the technical differences and performance metrics in our [YOLO26 documentation](https://docs.ultralytics.com/models/yolo26/).
|
||||
|
|
|
|||
|
|
@ -1,16 +1,16 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn to accurately identify and count objects in real-time using Ultralytics YOLO11 for applications like crowd analysis and surveillance.
|
||||
keywords: object counting, YOLO11, Ultralytics, real-time object detection, AI, deep learning, object tracking, crowd analysis, surveillance, resource optimization
|
||||
description: Learn to accurately identify and count objects in real-time using Ultralytics YOLO26 for applications like crowd analysis and surveillance.
|
||||
keywords: object counting, YOLO26, Ultralytics, real-time object detection, AI, deep learning, object tracking, crowd analysis, surveillance, resource optimization
|
||||
---
|
||||
|
||||
# Object Counting using Ultralytics YOLO11
|
||||
# Object Counting using Ultralytics YOLO26
|
||||
|
||||
## What is Object Counting?
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-count-the-objects-using-ultralytics-yolo.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Object Counting In Colab"></a>
|
||||
|
||||
Object counting with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) involves accurate identification and counting of specific objects in videos and camera streams. YOLO11 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) capabilities.
|
||||
Object counting with [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics/) involves accurate identification and counting of specific objects in videos and camera streams. YOLO26 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) capabilities.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
|
|
@ -20,7 +20,7 @@ Object counting with [Ultralytics YOLO11](https://github.com/ultralytics/ultraly
|
|||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Perform Real-Time Object Counting with Ultralytics YOLO11 🍏
|
||||
<strong>Watch:</strong> How to Perform Real-Time Object Counting with Ultralytics YOLO26 🍏
|
||||
</p>
|
||||
|
||||
## Advantages of Object Counting
|
||||
|
|
@ -33,8 +33,8 @@ Object counting with [Ultralytics YOLO11](https://github.com/ultralytics/ultraly
|
|||
|
||||
| Logistics | Aquaculture |
|
||||
| :-----------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------: |
|
||||
|  |  |
|
||||
| Conveyor Belt Packets Counting Using Ultralytics YOLO11 | Fish Counting in Sea using Ultralytics YOLO11 |
|
||||
|  |  |
|
||||
| Conveyor Belt Packets Counting Using Ultralytics YOLO26 | Fish Counting in Sea using Ultralytics YOLO26 |
|
||||
|
||||
!!! example "Object Counting using Ultralytics YOLO"
|
||||
|
||||
|
|
@ -75,7 +75,7 @@ Object counting with [Ultralytics YOLO11](https://github.com/ultralytics/ultraly
|
|||
counter = solutions.ObjectCounter(
|
||||
show=True, # display the output
|
||||
region=region_points, # pass region points
|
||||
model="yolo11n.pt", # model="yolo11n-obb.pt" for object counting with OBB model.
|
||||
model="yolo26n.pt", # model="yolo26n-obb.pt" for object counting with OBB model.
|
||||
# classes=[0, 2], # count specific classes, e.g., person and car with the COCO pretrained model.
|
||||
# tracker="botsort.yaml", # choose trackers, e.g., "bytetrack.yaml"
|
||||
)
|
||||
|
|
@ -118,9 +118,9 @@ Additionally, the visualization arguments listed below are supported:
|
|||
|
||||
## FAQ
|
||||
|
||||
### How do I count objects in a video using Ultralytics YOLO11?
|
||||
### How do I count objects in a video using Ultralytics YOLO26?
|
||||
|
||||
To count objects in a video using Ultralytics YOLO11, you can follow these steps:
|
||||
To count objects in a video using Ultralytics YOLO26, you can follow these steps:
|
||||
|
||||
1. Import the necessary libraries (`cv2`, `ultralytics`).
|
||||
2. Define the counting region (e.g., a polygon, line, etc.).
|
||||
|
|
@ -158,25 +158,25 @@ def count_objects_in_region(video_path, output_video_path, model_path):
|
|||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
count_objects_in_region("path/to/video.mp4", "output_video.avi", "yolo11n.pt")
|
||||
count_objects_in_region("path/to/video.mp4", "output_video.avi", "yolo26n.pt")
|
||||
```
|
||||
|
||||
For more advanced configurations and options, check out the [RegionCounter solution](https://docs.ultralytics.com/guides/region-counting/) for counting objects in multiple regions simultaneously.
|
||||
|
||||
### What are the advantages of using Ultralytics YOLO11 for object counting?
|
||||
### What are the advantages of using Ultralytics YOLO26 for object counting?
|
||||
|
||||
Using Ultralytics YOLO11 for object counting offers several advantages:
|
||||
Using Ultralytics YOLO26 for object counting offers several advantages:
|
||||
|
||||
1. **Resource Optimization:** It facilitates efficient resource management by providing accurate counts, helping optimize resource allocation in industries like [inventory management](https://www.ultralytics.com/blog/ai-for-smarter-retail-inventory-management).
|
||||
2. **Enhanced Security:** It enhances security and surveillance by accurately tracking and counting entities, aiding in proactive threat detection and [security systems](https://docs.ultralytics.com/guides/security-alarm-system/).
|
||||
3. **Informed Decision-Making:** It offers valuable insights for decision-making, optimizing processes in domains like retail, traffic management, and more.
|
||||
4. **Real-time Processing:** YOLO11's architecture enables [real-time inference](https://www.ultralytics.com/glossary/real-time-inference), making it suitable for live video streams and time-sensitive applications.
|
||||
4. **Real-time Processing:** YOLO26's architecture enables [real-time inference](https://www.ultralytics.com/glossary/real-time-inference), making it suitable for live video streams and time-sensitive applications.
|
||||
|
||||
For implementation examples and practical applications, explore the [TrackZone solution](https://docs.ultralytics.com/guides/trackzone/) for tracking objects in specific zones.
|
||||
|
||||
### How can I count specific classes of objects using Ultralytics YOLO11?
|
||||
### How can I count specific classes of objects using Ultralytics YOLO26?
|
||||
|
||||
To count specific classes of objects using Ultralytics YOLO11, you need to specify the classes you are interested in during the tracking phase. Below is a Python example:
|
||||
To count specific classes of objects using Ultralytics YOLO26, you need to specify the classes you are interested in during the tracking phase. Below is a Python example:
|
||||
|
||||
```python
|
||||
import cv2
|
||||
|
|
@ -207,25 +207,25 @@ def count_specific_classes(video_path, output_video_path, model_path, classes_to
|
|||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
count_specific_classes("path/to/video.mp4", "output_specific_classes.avi", "yolo11n.pt", [0, 2])
|
||||
count_specific_classes("path/to/video.mp4", "output_specific_classes.avi", "yolo26n.pt", [0, 2])
|
||||
```
|
||||
|
||||
In this example, `classes_to_count=[0, 2]` means it counts objects of class `0` and `2` (e.g., person and car in the COCO dataset). You can find more information about class indices in the [COCO dataset documentation](https://docs.ultralytics.com/datasets/detect/coco/).
|
||||
|
||||
### Why should I use YOLO11 over other [object detection](https://www.ultralytics.com/glossary/object-detection) models for real-time applications?
|
||||
### Why should I use YOLO26 over other [object detection](https://www.ultralytics.com/glossary/object-detection) models for real-time applications?
|
||||
|
||||
Ultralytics YOLO11 provides several advantages over other object detection models like [Faster R-CNN](https://docs.ultralytics.com/compare/yolo11-vs-efficientdet/), SSD, and previous YOLO versions:
|
||||
Ultralytics YOLO26 provides several advantages over other object detection models like [Faster R-CNN](https://docs.ultralytics.com/compare/yolo26-vs-efficientdet/), SSD, and previous YOLO versions:
|
||||
|
||||
1. **Speed and Efficiency:** YOLO11 offers real-time processing capabilities, making it ideal for applications requiring high-speed inference, such as surveillance and [autonomous driving](https://www.ultralytics.com/blog/ai-in-self-driving-cars).
|
||||
1. **Speed and Efficiency:** YOLO26 offers real-time processing capabilities, making it ideal for applications requiring high-speed inference, such as surveillance and [autonomous driving](https://www.ultralytics.com/blog/ai-in-self-driving-cars).
|
||||
2. **[Accuracy](https://www.ultralytics.com/glossary/accuracy):** It provides state-of-the-art accuracy for object detection and tracking tasks, reducing the number of false positives and improving overall system reliability.
|
||||
3. **Ease of Integration:** YOLO11 offers seamless integration with various platforms and devices, including mobile and [edge devices](https://docs.ultralytics.com/guides/nvidia-jetson/), which is crucial for modern AI applications.
|
||||
3. **Ease of Integration:** YOLO26 offers seamless integration with various platforms and devices, including mobile and [edge devices](https://docs.ultralytics.com/guides/nvidia-jetson/), which is crucial for modern AI applications.
|
||||
4. **Flexibility:** Supports various tasks like object detection, [segmentation](https://docs.ultralytics.com/tasks/segment/), and tracking with configurable models to meet specific use-case requirements.
|
||||
|
||||
Check out Ultralytics [YOLO11 Documentation](https://docs.ultralytics.com/models/yolo11/) for a deeper dive into its features and performance comparisons.
|
||||
Check out Ultralytics [YOLO26 Documentation](https://docs.ultralytics.com/models/yolo26/) for a deeper dive into its features and performance comparisons.
|
||||
|
||||
### Can I use YOLO11 for advanced applications like crowd analysis and traffic management?
|
||||
### Can I use YOLO26 for advanced applications like crowd analysis and traffic management?
|
||||
|
||||
Yes, Ultralytics YOLO11 is perfectly suited for advanced applications like crowd analysis and traffic management due to its real-time detection capabilities, scalability, and integration flexibility. Its advanced features allow for high-accuracy object tracking, counting, and classification in dynamic environments. Example use cases include:
|
||||
Yes, Ultralytics YOLO26 is perfectly suited for advanced applications like crowd analysis and traffic management due to its real-time detection capabilities, scalability, and integration flexibility. Its advanced features allow for high-accuracy object tracking, counting, and classification in dynamic environments. Example use cases include:
|
||||
|
||||
- **Crowd Analysis:** Monitor and manage large gatherings, ensuring safety and optimizing crowd flow with [region-based counting](https://docs.ultralytics.com/guides/region-counting/).
|
||||
- **Traffic Management:** Track and count vehicles, analyze traffic patterns, and manage congestion in real-time with [speed estimation](https://docs.ultralytics.com/guides/speed-estimation/) capabilities.
|
||||
|
|
|
|||
|
|
@ -1,14 +1,14 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn how to crop and extract objects using Ultralytics YOLO11 for focused analysis, reduced data volume, and enhanced precision.
|
||||
keywords: Ultralytics, YOLO11, object cropping, object detection, image processing, video analysis, AI, machine learning
|
||||
description: Learn how to crop and extract objects using Ultralytics YOLO26 for focused analysis, reduced data volume, and enhanced precision.
|
||||
keywords: Ultralytics, YOLO26, object cropping, object detection, image processing, video analysis, AI, machine learning
|
||||
---
|
||||
|
||||
# Object Cropping using Ultralytics YOLO11
|
||||
# Object Cropping using Ultralytics YOLO26
|
||||
|
||||
## What is Object Cropping?
|
||||
|
||||
Object cropping with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) involves isolating and extracting specific detected objects from an image or video. The YOLO11 model capabilities are utilized to accurately identify and delineate objects, enabling precise cropping for further analysis or manipulation.
|
||||
Object cropping with [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics/) involves isolating and extracting specific detected objects from an image or video. The YOLO26 model capabilities are utilized to accurately identify and delineate objects, enabling precise cropping for further analysis or manipulation.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
|
|
@ -23,16 +23,16 @@ Object cropping with [Ultralytics YOLO11](https://github.com/ultralytics/ultraly
|
|||
|
||||
## Advantages of Object Cropping
|
||||
|
||||
- **Focused Analysis**: YOLO11 facilitates targeted object cropping, allowing for in-depth examination or processing of individual items within a scene.
|
||||
- **Focused Analysis**: YOLO26 facilitates targeted object cropping, allowing for in-depth examination or processing of individual items within a scene.
|
||||
- **Reduced Data Volume**: By extracting only relevant objects, object cropping helps in minimizing data size, making it efficient for storage, transmission, or subsequent computational tasks.
|
||||
- **Enhanced Precision**: YOLO11's [object detection](https://www.ultralytics.com/glossary/object-detection) [accuracy](https://www.ultralytics.com/glossary/accuracy) ensures that the cropped objects maintain their spatial relationships, preserving the integrity of the visual information for detailed analysis.
|
||||
- **Enhanced Precision**: YOLO26's [object detection](https://www.ultralytics.com/glossary/object-detection) [accuracy](https://www.ultralytics.com/glossary/accuracy) ensures that the cropped objects maintain their spatial relationships, preserving the integrity of the visual information for detailed analysis.
|
||||
|
||||
## Visuals
|
||||
|
||||
| Airport Luggage |
|
||||
| :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|
||||
|  |
|
||||
| Suitcases Cropping at airport conveyor belt using Ultralytics YOLO11 |
|
||||
|  |
|
||||
| Suitcases Cropping at airport conveyor belt using Ultralytics YOLO26 |
|
||||
|
||||
!!! example "Object Cropping using Ultralytics YOLO"
|
||||
|
||||
|
|
@ -62,7 +62,7 @@ Object cropping with [Ultralytics YOLO11](https://github.com/ultralytics/ultraly
|
|||
# Initialize object cropper
|
||||
cropper = solutions.ObjectCropper(
|
||||
show=True, # display the output
|
||||
model="yolo11n.pt", # model for object cropping, e.g., yolo11x.pt.
|
||||
model="yolo26n.pt", # model for object cropping, e.g., yolo26x.pt.
|
||||
classes=[0, 2], # crop specific classes such as person and car with the COCO pretrained model.
|
||||
# conf=0.5, # adjust confidence threshold for the objects.
|
||||
# crop_dir="cropped-detections", # set the directory name for cropped detections
|
||||
|
|
@ -100,22 +100,22 @@ Moreover, the following visualization arguments are available for use:
|
|||
|
||||
## FAQ
|
||||
|
||||
### What is object cropping in Ultralytics YOLO11 and how does it work?
|
||||
### What is object cropping in Ultralytics YOLO26 and how does it work?
|
||||
|
||||
Object cropping using [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) involves isolating and extracting specific objects from an image or video based on YOLO11's detection capabilities. This process allows for focused analysis, reduced data volume, and enhanced [precision](https://www.ultralytics.com/glossary/precision) by leveraging YOLO11 to identify objects with high accuracy and crop them accordingly. For an in-depth tutorial, refer to the [object cropping example](#object-cropping-using-ultralytics-yolo11).
|
||||
Object cropping using [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics) involves isolating and extracting specific objects from an image or video based on YOLO26's detection capabilities. This process allows for focused analysis, reduced data volume, and enhanced [precision](https://www.ultralytics.com/glossary/precision) by leveraging YOLO26 to identify objects with high accuracy and crop them accordingly. For an in-depth tutorial, refer to the [object cropping example](#object-cropping-using-ultralytics-yolo26).
|
||||
|
||||
### Why should I use Ultralytics YOLO11 for object cropping over other solutions?
|
||||
### Why should I use Ultralytics YOLO26 for object cropping over other solutions?
|
||||
|
||||
Ultralytics YOLO11 stands out due to its precision, speed, and ease of use. It allows detailed and accurate object detection and cropping, essential for [focused analysis](#advantages-of-object-cropping) and applications needing high data integrity. Moreover, YOLO11 integrates seamlessly with tools like [OpenVINO](../integrations/openvino.md) and [TensorRT](../integrations/tensorrt.md) for deployments requiring real-time capabilities and optimization on diverse hardware. Explore the benefits in the [guide on model export](../modes/export.md).
|
||||
Ultralytics YOLO26 stands out due to its precision, speed, and ease of use. It allows detailed and accurate object detection and cropping, essential for [focused analysis](#advantages-of-object-cropping) and applications needing high data integrity. Moreover, YOLO26 integrates seamlessly with tools like [OpenVINO](../integrations/openvino.md) and [TensorRT](../integrations/tensorrt.md) for deployments requiring real-time capabilities and optimization on diverse hardware. Explore the benefits in the [guide on model export](../modes/export.md).
|
||||
|
||||
### How can I reduce the data volume of my dataset using object cropping?
|
||||
|
||||
By using Ultralytics YOLO11 to crop only relevant objects from your images or videos, you can significantly reduce the data size, making it more efficient for storage and processing. This process involves training the model to detect specific objects and then using the results to crop and save these portions only. For more information on exploiting Ultralytics YOLO11's capabilities, visit our [quickstart guide](../quickstart.md).
|
||||
By using Ultralytics YOLO26 to crop only relevant objects from your images or videos, you can significantly reduce the data size, making it more efficient for storage and processing. This process involves training the model to detect specific objects and then using the results to crop and save these portions only. For more information on exploiting Ultralytics YOLO26's capabilities, visit our [quickstart guide](../quickstart.md).
|
||||
|
||||
### Can I use Ultralytics YOLO11 for real-time video analysis and object cropping?
|
||||
### Can I use Ultralytics YOLO26 for real-time video analysis and object cropping?
|
||||
|
||||
Yes, Ultralytics YOLO11 can process real-time video feeds to detect and crop objects dynamically. The model's high-speed inference capabilities make it ideal for real-time applications such as [surveillance](security-alarm-system.md), sports analysis, and automated inspection systems. Check out the [tracking](../modes/track.md) and [prediction modes](../modes/predict.md) to understand how to implement real-time processing.
|
||||
Yes, Ultralytics YOLO26 can process real-time video feeds to detect and crop objects dynamically. The model's high-speed inference capabilities make it ideal for real-time applications such as [surveillance](security-alarm-system.md), sports analysis, and automated inspection systems. Check out the [tracking](../modes/track.md) and [prediction modes](../modes/predict.md) to understand how to implement real-time processing.
|
||||
|
||||
### What are the hardware requirements for efficiently running YOLO11 for object cropping?
|
||||
### What are the hardware requirements for efficiently running YOLO26 for object cropping?
|
||||
|
||||
Ultralytics YOLO11 is optimized for both CPU and GPU environments, but to achieve optimal performance, especially for real-time or high-volume inference, a dedicated GPU (e.g., NVIDIA Tesla, RTX series) is recommended. For deployment on lightweight devices, consider using [CoreML](../integrations/coreml.md) for iOS or [TFLite](../integrations/tflite.md) for Android. More details on supported devices and formats can be found in our [model deployment options](../guides/model-deployment-options.md).
|
||||
Ultralytics YOLO26 is optimized for both CPU and GPU environments, but to achieve optimal performance, especially for real-time or high-volume inference, a dedicated GPU (e.g., NVIDIA Tesla, RTX series) is recommended. For deployment on lightweight devices, consider using [CoreML](../integrations/coreml.md) for iOS or [TFLite](../integrations/tflite.md) for Android. More details on supported devices and formats can be found in our [model deployment options](../guides/model-deployment-options.md).
|
||||
|
|
|
|||
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