diff --git a/docs/en/datasets/detect/open-images-v7.md b/docs/en/datasets/detect/open-images-v7.md index 44d52b49eb..086d14345e 100644 --- a/docs/en/datasets/detect/open-images-v7.md +++ b/docs/en/datasets/detect/open-images-v7.md @@ -23,11 +23,11 @@ keywords: Open Images V7, Google dataset, computer vision, YOLO11 models, object | Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) | | ----------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | -| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 | -| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 | -| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 | -| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 | -| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 | +| [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 | You can use these pretrained models for inference or fine-tuning as follows. @@ -218,11 +218,11 @@ Ultralytics provides several YOLOv8 pretrained models for the Open Images V7 dat | Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) | | ----------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | -| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 | -| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 | -| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 | -| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 | -| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 | +| [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 | ### What applications can the Open Images V7 dataset be used for? diff --git a/docs/en/models/fast-sam.md b/docs/en/models/fast-sam.md index 59699dc4a5..6982a93325 100644 --- a/docs/en/models/fast-sam.md +++ b/docs/en/models/fast-sam.md @@ -49,8 +49,8 @@ This table presents the available models with their specific pre-trained weights | Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | | ---------- | ------------------------------------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ | -| FastSAM-s | [FastSAM-s.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/FastSAM-s.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ | -| FastSAM-x | [FastSAM-x.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/FastSAM-x.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ | +| FastSAM-s | [FastSAM-s.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/FastSAM-s.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ | +| FastSAM-x | [FastSAM-x.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/FastSAM-x.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ | ## FastSAM Comparison vs YOLO diff --git a/docs/en/models/mobile-sam.md b/docs/en/models/mobile-sam.md index 9aaf9be43e..386e2abb0e 100644 --- a/docs/en/models/mobile-sam.md +++ b/docs/en/models/mobile-sam.md @@ -33,7 +33,7 @@ This table presents the available models with their specific pre-trained weights | Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | | ---------- | --------------------------------------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ | -| MobileSAM | [mobile_sam.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/mobile_sam.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ | +| MobileSAM | [mobile_sam.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/mobile_sam.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ | ## MobileSAM Comparison vs YOLO @@ -234,7 +234,7 @@ MobileSAM is ideal for mobile applications due to its lightweight architecture a ### How was MobileSAM trained, and is the training code available? -MobileSAM was trained on a single GPU with a 100k dataset, which is 1% of the original images, in less than a day. While the training code will be made available in the future, you can currently explore other aspects of MobileSAM in the [MobileSAM GitHub repository](https://github.com/ultralytics/assets/releases/download/v8.2.0/mobile_sam.pt). This repository includes pre-trained weights and implementation details for various applications. +MobileSAM was trained on a single GPU with a 100k dataset, which is 1% of the original images, in less than a day. While the training code will be made available in the future, you can currently explore other aspects of MobileSAM in the [MobileSAM GitHub repository](https://github.com/ultralytics/assets/releases/download/v8.3.0/mobile_sam.pt). This repository includes pre-trained weights and implementation details for various applications. ### What are the primary use cases for MobileSAM? diff --git a/docs/en/models/rtdetr.md b/docs/en/models/rtdetr.md index 90adad86ad..6c729f63f4 100644 --- a/docs/en/models/rtdetr.md +++ b/docs/en/models/rtdetr.md @@ -85,8 +85,8 @@ This table presents the model types, the specific pre-trained weights, the tasks | Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | | ------------------- | ----------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ | -| RT-DETR Large | [rtdetr-l.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/rtdetr-l.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| RT-DETR Extra-Large | [rtdetr-x.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/rtdetr-x.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| RT-DETR Large | [rtdetr-l.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/rtdetr-l.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| RT-DETR Extra-Large | [rtdetr-x.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/rtdetr-x.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | ## Ideal Use Cases diff --git a/docs/en/models/sam.md b/docs/en/models/sam.md index 49cfe04b74..7ce6987ba2 100644 --- a/docs/en/models/sam.md +++ b/docs/en/models/sam.md @@ -33,8 +33,8 @@ This table presents the available models with their specific pre-trained weights | Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | | ---------- | ----------------------------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ | -| SAM base | [sam_b.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/sam_b.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ | -| SAM large | [sam_l.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/sam_l.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ | +| SAM base | [sam_b.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/sam_b.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ | +| SAM large | [sam_l.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/sam_l.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ | ## How to Use SAM: Versatility and Power in Image Segmentation diff --git a/docs/en/models/yolo-nas.md b/docs/en/models/yolo-nas.md index 6e6f09c05a..ed6b09214d 100644 --- a/docs/en/models/yolo-nas.md +++ b/docs/en/models/yolo-nas.md @@ -99,9 +99,9 @@ Below is a detailed overview of each model, including links to their pre-trained | Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | | ---------- | --------------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ | -| YOLO-NAS-s | [yolo_nas_s.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolo_nas_s.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ | -| YOLO-NAS-m | [yolo_nas_m.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolo_nas_m.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ | -| YOLO-NAS-l | [yolo_nas_l.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolo_nas_l.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ | +| YOLO-NAS-s | [yolo_nas_s.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo_nas_s.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ | +| YOLO-NAS-m | [yolo_nas_m.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo_nas_m.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ | +| YOLO-NAS-l | [yolo_nas_l.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo_nas_l.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ | ## Citations and Acknowledgements @@ -168,6 +168,6 @@ YOLO-NAS models support various object detection tasks and modes such as inferen Yes, Ultralytics provides pre-trained YOLO-NAS models that you can access directly. These models are pre-trained on datasets like COCO, ensuring high performance in terms of both speed and accuracy. You can download these models using the links provided in the [Pre-trained Models](#pre-trained-models) section. Here are some examples: -- [YOLO-NAS-s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolo_nas_s.pt) -- [YOLO-NAS-m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolo_nas_m.pt) -- [YOLO-NAS-l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolo_nas_l.pt) +- [YOLO-NAS-s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo_nas_s.pt) +- [YOLO-NAS-m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo_nas_m.pt) +- [YOLO-NAS-l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo_nas_l.pt) diff --git a/docs/en/models/yolo-world.md b/docs/en/models/yolo-world.md index 799d0d9cea..e55dc51c16 100644 --- a/docs/en/models/yolo-world.md +++ b/docs/en/models/yolo-world.md @@ -49,14 +49,14 @@ This section details the models available with their specific pre-trained weight | Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | | --------------- | ------------------------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ | -| YOLOv8s-world | [yolov8s-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | -| YOLOv8s-worldv2 | [yolov8s-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOv8m-world | [yolov8m-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | -| YOLOv8m-worldv2 | [yolov8m-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOv8l-world | [yolov8l-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | -| YOLOv8l-worldv2 | [yolov8l-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOv8x-world | [yolov8x-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | -| YOLOv8x-worldv2 | [yolov8x-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv8s-world | [yolov8s-world.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8s-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | +| YOLOv8s-worldv2 | [yolov8s-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8s-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv8m-world | [yolov8m-world.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | +| YOLOv8m-worldv2 | [yolov8m-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv8l-world | [yolov8l-world.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | +| YOLOv8l-worldv2 | [yolov8l-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv8x-world | [yolov8x-world.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8x-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | +| YOLOv8x-worldv2 | [yolov8x-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8x-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | ## Zero-shot Transfer on COCO Dataset @@ -402,14 +402,14 @@ Ultralytics offers multiple pre-trained YOLO-World models supporting various tas | Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | | --------------- | ------------------------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ | -| YOLOv8s-world | [yolov8s-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | -| YOLOv8s-worldv2 | [yolov8s-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOv8m-world | [yolov8m-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | -| YOLOv8m-worldv2 | [yolov8m-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOv8l-world | [yolov8l-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | -| YOLOv8l-worldv2 | [yolov8l-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOv8x-world | [yolov8x-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | -| YOLOv8x-worldv2 | [yolov8x-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv8s-world | [yolov8s-world.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8s-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | +| YOLOv8s-worldv2 | [yolov8s-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8s-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv8m-world | [yolov8m-world.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | +| YOLOv8m-worldv2 | [yolov8m-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv8l-world | [yolov8l-world.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | +| YOLOv8l-worldv2 | [yolov8l-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv8x-world | [yolov8x-world.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8x-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ | +| YOLOv8x-worldv2 | [yolov8x-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8x-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | ### How do I reproduce the official results of YOLO-World from scratch? diff --git a/docs/en/models/yoloe.md b/docs/en/models/yoloe.md index 171dfca6a2..a062e8bfc9 100644 --- a/docs/en/models/yoloe.md +++ b/docs/en/models/yoloe.md @@ -44,23 +44,23 @@ This section details the models available with their specific pre-trained weight | Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | | ---------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ | -| YOLOE-11S | [yoloe-11s-seg.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yoloe-11s-seg.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOE-11M | [yoloe-11m-seg.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yoloe-11m-seg.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOE-11L | [yoloe-11l-seg.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yoloe-11l-seg.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOE-v8S | [yoloe-v8s-seg.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yoloe-v8s-seg.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOE-v8M | [yoloe-v8m-seg.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yoloe-v8m-seg.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOE-v8L | [yoloe-v8l-seg.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yoloe-v8l-seg.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOE-11S | [yoloe-11s-seg.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yoloe-11s-seg.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOE-11M | [yoloe-11m-seg.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yoloe-11m-seg.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOE-11L | [yoloe-11l-seg.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yoloe-11l-seg.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOE-v8S | [yoloe-v8s-seg.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yoloe-v8s-seg.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOE-v8M | [yoloe-v8m-seg.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yoloe-v8m-seg.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOE-v8L | [yoloe-v8l-seg.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yoloe-v8l-seg.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | ### Prompt Free models | Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | | ------------ | --------------------------------------------------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ | -| YOLOE-11S-PF | [yoloe-11s-seg-pf.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yoloe-11s-seg-pf.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOE-11M-PF | [yoloe-11m-seg-pf.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yoloe-11m-seg-pf.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOE-11L-PF | [yoloe-11l-seg-pf.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yoloe-11l-seg-pf.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOE-v8S-PF | [yoloe-v8s-seg-pf.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yoloe-v8s-seg-pf.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOE-v8M-PF | [yoloe-v8m-seg-pf.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yoloe-v8m-seg-pf.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOE-v8L-PF | [yoloe-v8l-seg-pf.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yoloe-v8l-seg-pf.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOE-11S-PF | [yoloe-11s-seg-pf.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yoloe-11s-seg-pf.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOE-11M-PF | [yoloe-11m-seg-pf.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yoloe-11m-seg-pf.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOE-11L-PF | [yoloe-11l-seg-pf.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yoloe-11l-seg-pf.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOE-v8S-PF | [yoloe-v8s-seg-pf.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yoloe-v8s-seg-pf.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOE-v8M-PF | [yoloe-v8m-seg-pf.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yoloe-v8m-seg-pf.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOE-v8L-PF | [yoloe-v8l-seg-pf.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yoloe-v8l-seg-pf.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | ## Usage Examples diff --git a/docs/en/models/yolov10.md b/docs/en/models/yolov10.md index de56e530cf..5b4dbcc997 100644 --- a/docs/en/models/yolov10.md +++ b/docs/en/models/yolov10.md @@ -142,12 +142,12 @@ Compared to other state-of-the-art detectors: | RT-DETR-R101 | 76.0 | 259.0 | 54.3 | 13.71 | 13.58 | | **[YOLOv10x][6]** | **29.5** | **160.4** | **54.4** | **10.70** | **10.60** | - [1]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10n.pt - [2]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10s.pt - [3]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10m.pt - [4]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10b.pt - [5]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10l.pt - [6]: https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10x.pt + [1]: https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov10n.pt + [2]: https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov10s.pt + [3]: https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov10m.pt + [4]: https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov10b.pt + [5]: https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov10l.pt + [6]: https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov10x.pt ## Usage Examples diff --git a/docs/en/models/yolov5.md b/docs/en/models/yolov5.md index 1b2a5a0357..76652462dc 100644 --- a/docs/en/models/yolov5.md +++ b/docs/en/models/yolov5.md @@ -45,17 +45,17 @@ This table provides a detailed overview of the YOLOv5u model variants, highlight | Model | YAML | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) | |---------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|-----------------------|----------------------|--------------------------------|-------------------------------------|--------------------|-------------------| - | [yolov5nu.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5nu.pt) | [yolov5n.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 34.3 | 73.6 | 1.06 | 2.6 | 7.7 | - | [yolov5su.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5su.pt) | [yolov5s.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 43.0 | 120.7 | 1.27 | 9.1 | 24.0 | - | [yolov5mu.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5mu.pt) | [yolov5m.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 49.0 | 233.9 | 1.86 | 25.1 | 64.2 | - | [yolov5lu.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5lu.pt) | [yolov5l.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 52.2 | 408.4 | 2.50 | 53.2 | 135.0 | - | [yolov5xu.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5xu.pt) | [yolov5x.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 53.2 | 763.2 | 3.81 | 97.2 | 246.4 | + | [yolov5nu.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov5nu.pt) | [yolov5n.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 34.3 | 73.6 | 1.06 | 2.6 | 7.7 | + | [yolov5su.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov5su.pt) | [yolov5s.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 43.0 | 120.7 | 1.27 | 9.1 | 24.0 | + | [yolov5mu.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov5mu.pt) | [yolov5m.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 49.0 | 233.9 | 1.86 | 25.1 | 64.2 | + | [yolov5lu.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov5lu.pt) | [yolov5l.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 52.2 | 408.4 | 2.50 | 53.2 | 135.0 | + | [yolov5xu.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov5xu.pt) | [yolov5x.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml) | 640 | 53.2 | 763.2 | 3.81 | 97.2 | 246.4 | | | | | | | | | | - | [yolov5n6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5n6u.pt) | [yolov5n6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 42.1 | 211.0 | 1.83 | 4.3 | 7.8 | - | [yolov5s6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5s6u.pt) | [yolov5s6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 48.6 | 422.6 | 2.34 | 15.3 | 24.6 | - | [yolov5m6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5m6u.pt) | [yolov5m6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 53.6 | 810.9 | 4.36 | 41.2 | 65.7 | - | [yolov5l6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5l6u.pt) | [yolov5l6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 55.7 | 1470.9 | 5.47 | 86.1 | 137.4 | - | [yolov5x6u.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5x6u.pt) | [yolov5x6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 56.8 | 2436.5 | 8.98 | 155.4 | 250.7 | + | [yolov5n6u.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov5n6u.pt) | [yolov5n6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 42.1 | 211.0 | 1.83 | 4.3 | 7.8 | + | [yolov5s6u.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov5s6u.pt) | [yolov5s6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 48.6 | 422.6 | 2.34 | 15.3 | 24.6 | + | [yolov5m6u.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov5m6u.pt) | [yolov5m6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 53.6 | 810.9 | 4.36 | 41.2 | 65.7 | + | [yolov5l6u.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov5l6u.pt) | [yolov5l6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 55.7 | 1470.9 | 5.47 | 86.1 | 137.4 | + | [yolov5x6u.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov5x6u.pt) | [yolov5x6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280 | 56.8 | 2436.5 | 8.98 | 155.4 | 250.7 | ## Usage Examples diff --git a/docs/en/models/yolov8.md b/docs/en/models/yolov8.md index dc85e69fe5..6f6688cc0b 100644 --- a/docs/en/models/yolov8.md +++ b/docs/en/models/yolov8.md @@ -61,11 +61,11 @@ This table provides an overview of the YOLOv8 model variants, highlighting their | Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) | | ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | - | [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 | - | [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 | - | [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 | - | [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 | - | [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 | + | [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 | + | [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 | + | [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 | + | [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 | + | [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 | === "Detection (Open Images V7)" @@ -73,11 +73,11 @@ This table provides an overview of the YOLOv8 model variants, highlighting their | Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) | | ----------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | - | [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 | - | [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 | - | [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 | - | [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 | - | [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 | + | [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 | === "Segmentation (COCO)" @@ -85,11 +85,11 @@ This table provides an overview of the YOLOv8 model variants, highlighting their | Model | size
(pixels) | mAPbox
50-95 | mAPmask
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) | | -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | - | [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 | - | [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 | - | [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 | - | [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 | - | [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 | + | [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 | + | [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 | + | [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 | + | [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 | + | [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 | === "Classification (ImageNet)" @@ -97,11 +97,11 @@ This table provides an overview of the YOLOv8 model variants, highlighting their | Model | size
(pixels) | acc
top1 | acc
top5 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) at 224 | | -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ | - | [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-cls.pt) | 224 | 69.0 | 88.3 | 12.9 | 0.31 | 2.7 | 0.5 | - | [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-cls.pt) | 224 | 73.8 | 91.7 | 23.4 | 0.35 | 6.4 | 1.7 | - | [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-cls.pt) | 224 | 76.8 | 93.5 | 85.4 | 0.62 | 17.0 | 5.3 | - | [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-cls.pt) | 224 | 76.8 | 93.5 | 163.0 | 0.87 | 37.5 | 12.3 | - | [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-cls.pt) | 224 | 79.0 | 94.6 | 232.0 | 1.01 | 57.4 | 19.0 | + | [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n-cls.pt) | 224 | 69.0 | 88.3 | 12.9 | 0.31 | 2.7 | 0.5 | + | [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8s-cls.pt) | 224 | 73.8 | 91.7 | 23.4 | 0.35 | 6.4 | 1.7 | + | [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m-cls.pt) | 224 | 76.8 | 93.5 | 85.4 | 0.62 | 17.0 | 5.3 | + | [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l-cls.pt) | 224 | 76.8 | 93.5 | 163.0 | 0.87 | 37.5 | 12.3 | + | [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8x-cls.pt) | 224 | 79.0 | 94.6 | 232.0 | 1.01 | 57.4 | 19.0 | === "Pose (COCO)" @@ -109,12 +109,12 @@ This table provides an overview of the YOLOv8 model variants, highlighting their | Model | size
(pixels) | mAPpose
50-95 | mAPpose
50 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) | | ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | - | [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-pose.pt) | 640 | 50.4 | 80.1 | 131.8 | 1.18 | 3.3 | 9.2 | - | [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-pose.pt) | 640 | 60.0 | 86.2 | 233.2 | 1.42 | 11.6 | 30.2 | - | [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-pose.pt) | 640 | 65.0 | 88.8 | 456.3 | 2.00 | 26.4 | 81.0 | - | [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-pose.pt) | 640 | 67.6 | 90.0 | 784.5 | 2.59 | 44.4 | 168.6 | - | [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 | - | [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 | + | [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n-pose.pt) | 640 | 50.4 | 80.1 | 131.8 | 1.18 | 3.3 | 9.2 | + | [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8s-pose.pt) | 640 | 60.0 | 86.2 | 233.2 | 1.42 | 11.6 | 30.2 | + | [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m-pose.pt) | 640 | 65.0 | 88.8 | 456.3 | 2.00 | 26.4 | 81.0 | + | [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l-pose.pt) | 640 | 67.6 | 90.0 | 784.5 | 2.59 | 44.4 | 168.6 | + | [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 | + | [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 | === "OBB (DOTAv1)" @@ -122,11 +122,11 @@ This table provides an overview of the YOLOv8 model variants, highlighting their | Model | size
(pixels) | mAPtest
50 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) | |----------------------------------------------------------------------------------------------|-----------------------| -------------------- | -------------------------------- | ------------------------------------- | -------------------- | ----------------- | - | [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-obb.pt) | 1024 | 78.0 | 204.77 | 3.57 | 3.1 | 23.3 | - | [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-obb.pt) | 1024 | 79.5 | 424.88 | 4.07 | 11.4 | 76.3 | - | [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-obb.pt) | 1024 | 80.5 | 763.48 | 7.61 | 26.4 | 208.6 | - | [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-obb.pt) | 1024 | 80.7 | 1278.42 | 11.83 | 44.5 | 433.8 | - | [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-obb.pt) | 1024 | 81.36 | 1759.10 | 13.23 | 69.5 | 676.7 | + | [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n-obb.pt) | 1024 | 78.0 | 204.77 | 3.57 | 3.1 | 23.3 | + | [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8s-obb.pt) | 1024 | 79.5 | 424.88 | 4.07 | 11.4 | 76.3 | + | [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m-obb.pt) | 1024 | 80.5 | 763.48 | 7.61 | 26.4 | 208.6 | + | [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l-obb.pt) | 1024 | 80.7 | 1278.42 | 11.83 | 44.5 | 433.8 | + | [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8x-obb.pt) | 1024 | 81.36 | 1759.10 | 13.23 | 69.5 | 676.7 | ## YOLOv8 Usage Examples diff --git a/docs/en/models/yolov9.md b/docs/en/models/yolov9.md index cd02407e0a..abcbb1a2e0 100644 --- a/docs/en/models/yolov9.md +++ b/docs/en/models/yolov9.md @@ -99,18 +99,18 @@ The performance of YOLOv9 on the [COCO dataset](../datasets/detect/coco.md) exem | Model | size
(pixels) | mAPval
50-95 | mAPval
50 | params
(M) | FLOPs
(B) | |---------------------------------------------------------------------------------------|-----------------------|----------------------|-------------------|--------------------|-------------------| - | [YOLOv9t](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9t.pt) | 640 | 38.3 | 53.1 | 2.0 | 7.7 | - | [YOLOv9s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9s.pt) | 640 | 46.8 | 63.4 | 7.2 | 26.7 | - | [YOLOv9m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9m.pt) | 640 | 51.4 | 68.1 | 20.1 | 76.8 | - | [YOLOv9c](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9c.pt) | 640 | 53.0 | 70.2 | 25.5 | 102.8 | - | [YOLOv9e](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9e.pt) | 640 | 55.6 | 72.8 | 58.1 | 192.5 | + | [YOLOv9t](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov9t.pt) | 640 | 38.3 | 53.1 | 2.0 | 7.7 | + | [YOLOv9s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov9s.pt) | 640 | 46.8 | 63.4 | 7.2 | 26.7 | + | [YOLOv9m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov9m.pt) | 640 | 51.4 | 68.1 | 20.1 | 76.8 | + | [YOLOv9c](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov9c.pt) | 640 | 53.0 | 70.2 | 25.5 | 102.8 | + | [YOLOv9e](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov9e.pt) | 640 | 55.6 | 72.8 | 58.1 | 192.5 | === "Segmentation (COCO)" | Model | size
(pixels) | mAPbox
50-95 | mAPmask
50-95 | params
(M) | FLOPs
(B) | |-----------------------------------------------------------------------------------------------|-----------------------|----------------------|-----------------------|--------------------|-------------------| - | [YOLOv9c-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9c-seg.pt) | 640 | 52.4 | 42.2 | 27.9 | 159.4 | - | [YOLOv9e-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9e-seg.pt) | 640 | 55.1 | 44.3 | 60.5 | 248.4 | + | [YOLOv9c-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov9c-seg.pt) | 640 | 52.4 | 42.2 | 27.9 | 159.4 | + | [YOLOv9e-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov9e-seg.pt) | 640 | 55.1 | 44.3 | 60.5 | 248.4 | YOLOv9's iterations, ranging from the tiny `t` variant to the extensive `e` model, demonstrate improvements not only in accuracy (mAP metrics) but also in efficiency with a reduced number of parameters and computational needs (FLOPs). This table underscores YOLOv9's ability to deliver high [precision](https://www.ultralytics.com/glossary/precision) while maintaining or reducing the computational overhead compared to prior versions and competing models.