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ultralytics/examples/tutorial.ipynb
Glenn Jocher f2d3aed634
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>
2026-01-14 03:44:38 +00:00

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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "YOLO11 Tutorial",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "t6MPjfT5NrKQ"
},
"source": "<div align=\"center\">\n <a href=\"https://ultralytics.com/yolo\" target=\"_blank\">\n <img width=\"1024\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\">\n </a>\n\n [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n\n <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>\n <a href=\"https://console.paperspace.com/github/ultralytics/ultralytics\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"/></a>\n <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 In Colab\"></a>\n <a href=\"https://www.kaggle.com/models/ultralytics/yolo11\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n\n <a href=\"https://ultralytics.com/discord\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\"></a>\n <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>\n <a href=\"https://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>\n</div>\n\nThis **Ultralytics Colab Notebook** is the easiest way to get started with [YOLO models](https://www.ultralytics.com/yolo)—no installation needed. Built by [Ultralytics](https://www.ultralytics.com/), the creators of YOLO, this notebook walks you through running **state-of-the-art** models directly in your browser.\n\nUltralytics models are constantly updated for performance and flexibility. They're **fast**, **accurate**, and **easy to use**, and they excel at [object detection](https://docs.ultralytics.com/tasks/detect/), [tracking](https://docs.ultralytics.com/modes/track/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [image classification](https://docs.ultralytics.com/tasks/classify/), and [pose estimation](https://docs.ultralytics.com/tasks/pose/).\n\nFind detailed documentation in the [Ultralytics Docs](https://docs.ultralytics.com/). Get support via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues/new/choose). Join discussions on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/)!\n\nRequest an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).\n\n<br>\n<div>\n <a href=\"https://www.youtube.com/watch?v=ZN3nRZT7b24\" target=\"_blank\">\n <img src=\"https://img.youtube.com/vi/ZN3nRZT7b24/maxresdefault.jpg\" alt=\"Ultralytics Video\" width=\"640\" style=\"border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);\">\n </a>\n\n <p style=\"font-size: 16px; font-family: Arial, sans-serif; color: #555;\">\n <strong>Watch: </strong> How to Train\n <a href=\"https://github.com/ultralytics/ultralytics\">Ultralytics</a>\n <a href=\"https://docs.ultralytics.com/models/yolo26/\">YOLO26</a> Model on Custom Dataset using Google Colab Notebook 🚀\n </p>\n</div>"
},
{
"cell_type": "markdown",
"metadata": {
"id": "7mGmQbAO5pQb"
},
"source": [
"# Setup\n",
"\n",
"pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware.\n",
"\n",
"[![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://clickpy.clickhouse.com/dashboard/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics/)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "wbvMlHd_QwMG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4de5a5c9-d3b5-4e4e-a4e5-cd80f159e4ce"
},
"source": [
"!uv pip install ultralytics\n",
"import ultralytics\n",
"ultralytics.checks()"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics 8.3.187 🚀 Python-3.12.11 torch-2.8.0+cu126 CUDA:0 (Tesla T4, 15095MiB)\n",
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 39.0/112.6 GB disk)\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4JnkELT0cIJg"
},
"source": "# 1. Predict\n\nYOLO26 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/usage/cfg/) and other details in the [YOLO26 Predict Docs](https://docs.ultralytics.com/modes/train/)."
},
{
"cell_type": "code",
"metadata": {
"id": "zR9ZbuQCH7FX",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "1d438406-032a-4ea9-9ad5-dd17078850d3"
},
"source": "# Run inference on an image with YOLO26n\n!yolo predict model=yolo26n.pt source='https://ultralytics.com/images/zidane.jpg'",
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "hkAzDWJ7cWTr"
},
"source": [
"&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n",
"<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/212889447-69e5bdf1-5800-4e29-835e-2ed2336dede2.jpg\" width=\"600\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0eq1SMWl6Sfn"
},
"source": "# 2. Val\nValidate a model's accuracy on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset's `val` or `test` splits. The latest YOLO26 [models](https://github.com/ultralytics/ultralytics#models) are downloaded automatically the first time they are used. See [YOLO26 Val Docs](https://docs.ultralytics.com/modes/val/) for more information."
},
{
"cell_type": "code",
"metadata": {
"id": "WQPtK1QYVaD_"
},
"source": [
"# Download COCO val\n",
"from ultralytics.utils.downloads import download\n",
"\n",
"download('https://ultralytics.com/assets/coco2017val.zip', unzip=True, dir='datasets') # download (780MB - 5000 images)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "X58w8JLpMnjH",
"outputId": "27364fde-3aff-47ea-9458-18e4a044e27b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": "# Validate YOLO26n on COCO8 val\n!yolo val model=yolo26n.pt data=coco8.yaml",
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZY2VXXXu74w5"
},
"source": "# 3. Train\n\n<p align=\"\"><a href=\"https://ultralytics.com/hub\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"/></a></p>\n\nTrain YOLO26 on [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/) datasets. See [YOLO26 Train Docs](https://docs.ultralytics.com/modes/train/) for more information."
},
{
"cell_type": "code",
"source": "#@title Select YOLO26 🚀 logger {run: 'auto'}\nlogger = 'TensorBoard' #@param ['TensorBoard', 'Weights & Biases']\n\nif logger == 'TensorBoard':\n !yolo settings tensorboard=True\n %load_ext tensorboard\n %tensorboard --logdir .\nelif logger == 'Weights & Biases':\n !yolo settings wandb=True",
"metadata": {
"id": "ktegpM42AooT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1NcFxRcFdJ_O",
"outputId": "849c2875-cd29-4a93-a7c7-e464a9b84dc6",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": "# Train YOLO26n on COCO8 for 3 epochs\n!yolo train model=yolo26n.pt data=coco8.yaml epochs=3 imgsz=640",
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": "# 4. Export\n\nExport a YOLO model to any supported format below with the `format` argument, i.e. `format=onnx`. See [Export Docs](https://docs.ultralytics.com/modes/export/) for more information.\n\n- 💡 ProTip: Export to [ONNX](https://docs.ultralytics.com/integrations/onnx/) or [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) for up to 3x CPU speedup.\n- 💡 ProTip: Export to [TensorRT](https://docs.ultralytics.com/integrations/tensorrt/) for up to 5x GPU speedup.\n\n| Format | `format` Argument | Model | Metadata | Arguments |\n|--------|-----------------|-------|----------|------------|\n| [PyTorch](https://pytorch.org/) | - | `yolo26n.pt` | ✅ | - |\n| [TorchScript](https://docs.ultralytics.com/integrations/torchscript) | `torchscript` | `yolo26n.torchscript` | ✅ | `imgsz`, `batch`, `dynamic`, `optimize`, `half`, `nms`, `device` |\n| [ONNX](https://docs.ultralytics.com/integrations/onnx) | `onnx` | `yolo26n.onnx` | ✅ | `imgsz`, `batch`, `dynamic`, `half`, `opset`, `simplify`, `nms`, `device` |\n| [OpenVINO](https://docs.ultralytics.com/integrations/openvino) | `openvino` | `yolo26n_openvino_model/` | ✅ | `imgsz`, `batch`, `dynamic`, `half`, `int8`, `nms`, `fraction`, `device`, `data` |\n| [TensorRT](https://docs.ultralytics.com/integrations/tensorrt) | `engine` | `yolo26n.engine` | ✅ | `imgsz`, `batch`, `dynamic`, `half`, `int8`, `simplify`, `nms`, `fraction`, `device`, `data`, `workspace` |\n| [CoreML](https://docs.ultralytics.com/integrations/coreml) | `coreml` | `yolo26n.mlpackage` | ✅ | `imgsz`, `batch`, `half`, `int8`, `nms`, `device` |\n| [TF SavedModel](https://docs.ultralytics.com/integrations/tf-savedmodel) | `saved_model` | `yolo26n_saved_model/` | ✅ | `imgsz`, `batch`, `int8`, `keras`, `nms`, `device` |\n| [TF GraphDef](https://docs.ultralytics.com/integrations/tf-graphdef) | `pb` | `yolo26n.pb` | ❌ | `imgsz`, `batch`, `device` |\n| [TF Lite](https://docs.ultralytics.com/integrations/tflite) | `tflite` | `yolo26n.tflite` | ✅ | `imgsz`, `batch`, `half`, `int8`, `nms`, `fraction`, `device`, `data` |\n| [TF Edge TPU](https://docs.ultralytics.com/integrations/edge-tpu) | `edgetpu` | `yolo26n_edgetpu.tflite` | ✅ | `imgsz`, `device` |\n| [TF.js](https://docs.ultralytics.com/integrations/tfjs) | `tfjs` | `yolo26n_web_model/` | ✅ | `imgsz`, `batch`, `half`, `int8`, `nms`, `device` |\n| [PaddlePaddle](https://docs.ultralytics.com/integrations/paddlepaddle) | `paddle` | `yolo26n_paddle_model/` | ✅ | `imgsz`, `batch`, `device` |\n| [MNN](https://docs.ultralytics.com/integrations/mnn) | `mnn` | `yolo26n.mnn` | ✅ | `imgsz`, `batch`, `half`, `int8`, `device` |\n| [NCNN](https://docs.ultralytics.com/integrations/ncnn) | `ncnn` | `yolo26n_ncnn_model/` | ✅ | `imgsz`, `batch`, `half`, `device` |\n| [IMX500](https://docs.ultralytics.com/integrations/sony-imx500) | `imx` | `yolo26n_imx_model/` | ✅ | `imgsz`, `int8`, `fraction`, `device`, `data` |\n| [RKNN](https://docs.ultralytics.com/integrations/rockchip-rknn) | `rknn` | `yolo26n_rknn_model/` | ✅ | `imgsz`, `batch`, `name`, `device` || [ExecuTorch](https://docs.ultralytics.com/integrations/executorch) | `executorch` | `executorch_model/` | ✅ | `imgsz`, `device` || [Axelera](https://docs.ultralytics.com/integrations/axelera) | `axelera` | `axelera_model/` | ✅ | `imgsz`, `int8`, `fraction`, `device`, `data` |",
"metadata": {
"id": "nPZZeNrLCQG6"
}
},
{
"cell_type": "code",
"source": "!yolo export model=yolo26n.pt format=torchscript",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CYIjW4igCjqD",
"outputId": "f06e7d97-01d9-45f4-b24b-e90b08291675"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": "# 5. Python Usage\n\nYOLO26 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLO26 models can be loaded from a trained checkpoint or created from scratch. Then methods are used to train, val, predict, and export the model. See detailed Python usage examples in the [YOLO26 Python Docs](https://docs.ultralytics.com/usage/python/).",
"metadata": {
"id": "kUMOQ0OeDBJG"
}
},
{
"cell_type": "code",
"source": "from ultralytics import YOLO\n\n# Load a model\nmodel = YOLO('yolo26n.yaml') # build a new model from scratch\nmodel = YOLO('yolo26n.pt') # load a pretrained model (recommended for training)\n\n# Use the model\nresults = model.train(data='coco8.yaml', epochs=3) # train the model\nresults = model.val() # evaluate model performance on the validation set\nresults = model('https://ultralytics.com/images/bus.jpg') # predict on an image\nresults = model.export(format='onnx') # export the model to ONNX format",
"metadata": {
"id": "bpF9-vS_DAaf"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": "# 6. Tasks\n\nYOLO26 can train, val, predict and export models for the most common tasks in vision AI: [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/). See [YOLO26 Tasks Docs](https://docs.ultralytics.com/tasks/) for more information.\n\n<br><img width=\"1024\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png\">",
"metadata": {
"id": "Phm9ccmOKye5"
}
},
{
"cell_type": "markdown",
"source": "## 1. Detection\n\nYOLO26 _detection_ models have no suffix and are the default YOLO26 models, i.e. `yolo26n.pt` and are pretrained on COCO. See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for full details.",
"metadata": {
"id": "yq26lwpYK1lq"
}
},
{
"cell_type": "code",
"source": "# Load YOLO26n, train it on COCO128 for 3 epochs and predict an image with it\nfrom ultralytics import YOLO\n\nmodel = YOLO('yolo26n.pt') # load a pretrained YOLO detection model\nmodel.train(data='coco8.yaml', epochs=3) # train the model\nmodel('https://ultralytics.com/images/bus.jpg') # predict on an image",
"metadata": {
"id": "8Go5qqS9LbC5"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": "## 2. Segmentation\n\nYOLO26 _segmentation_ models use the `-seg` suffix, i.e. `yolo26n-seg.pt` and are pretrained on COCO. See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for full details.",
"metadata": {
"id": "7ZW58jUzK66B"
}
},
{
"cell_type": "code",
"source": "# Load YOLO26n-seg, train it on COCO128-seg for 3 epochs and predict an image with it\nfrom ultralytics import YOLO\n\nmodel = YOLO('yolo26n-seg.pt') # load a pretrained YOLO segmentation model\nmodel.train(data='coco8-seg.yaml', epochs=3) # train the model\nmodel('https://ultralytics.com/images/bus.jpg') # predict on an image",
"metadata": {
"id": "WFPJIQl_L5HT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": "## 3. Classification\n\nYOLO26 _classification_ models use the `-cls` suffix, i.e. `yolo26n-cls.pt` and are pretrained on ImageNet. See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for full details.",
"metadata": {
"id": "ax3p94VNK9zR"
}
},
{
"cell_type": "code",
"source": "# Load YOLO26n-cls, train it on mnist160 for 3 epochs and predict an image with it\nfrom ultralytics import YOLO\n\nmodel = YOLO('yolo26n-cls.pt') # load a pretrained YOLO classification model\nmodel.train(data='mnist160', epochs=3) # train the model\nmodel('https://ultralytics.com/images/bus.jpg') # predict on an image",
"metadata": {
"id": "5q9Zu6zlL5rS"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": "## 4. Pose\n\nYOLO26 _pose_ models use the `-pose` suffix, i.e. `yolo26n-pose.pt` and are pretrained on COCO Keypoints. See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for full details.",
"metadata": {
"id": "SpIaFLiO11TG"
}
},
{
"cell_type": "code",
"source": "# Load YOLO26n-pose, train it on COCO8-pose for 3 epochs and predict an image with it\nfrom ultralytics import YOLO\n\nmodel = YOLO('yolo26n-pose.pt') # load a pretrained YOLO pose model\nmodel.train(data='coco8-pose.yaml', epochs=3) # train the model\nmodel('https://ultralytics.com/images/bus.jpg') # predict on an image",
"metadata": {
"id": "si4aKFNg19vX"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": "## 4. Oriented Bounding Boxes (OBB)\n\nYOLO26 _OBB_ models use the `-obb` suffix, i.e. `yolo26n-obb.pt` and are pretrained on the DOTA dataset. See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for full details.",
"metadata": {
"id": "cf5j_T9-B5F0"
}
},
{
"cell_type": "code",
"source": "# Load YOLO26n-obb, train it on DOTA8 for 3 epochs and predict an image with it\nfrom ultralytics import YOLO\n\nmodel = YOLO('yolo26n-obb.pt') # load a pretrained YOLO OBB model\nmodel.train(data='dota8.yaml', epochs=3) # train the model\nmodel('https://ultralytics.com/images/boats.jpg') # predict on an image",
"metadata": {
"id": "IJNKClOOB5YS"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "IEijrePND_2I"
},
"source": [
"# Appendix\n",
"\n",
"Additional content below."
]
},
{
"cell_type": "code",
"source": [
"# Pip install from source\n",
"!uv pip install git+https://github.com/ultralytics/ultralytics@main"
],
"metadata": {
"id": "pIdE6i8C3LYp"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Git clone and run tests on 'main' branch\n",
"!git clone https://github.com/ultralytics/ultralytics -b main\n",
"!uv pip install -qe ultralytics"
],
"metadata": {
"id": "uRKlwxSJdhd1"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Run tests (Git clone only)\n",
"!pytest ultralytics/tests"
],
"metadata": {
"id": "GtPlh7mcCGZX"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": "# Validate multiple models\nfor x in 'nsmlx':\n !yolo val model=yolo26{x}.pt data=coco.yaml",
"metadata": {
"id": "Wdc6t_bfzDDk"
},
"execution_count": null,
"outputs": []
}
]
}
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