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ultralytics/examples/tutorial.ipynb

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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",
"\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\"></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.yaml\"><img src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/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/ultralytics/yolov8\"><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",
"\n",
"Welcome to the Ultralytics YOLO11 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLO11</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n",
"\n",
"YOLO11 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
"\n",
"We hope that the resources in this notebook will help you get the most out of YOLO11. Please browse the YOLO11 <a href=\"https://docs.ultralytics.com/\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
"\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://pepy.tech/project/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": "2e992f9f-90bb-4668-de12-fed629975285"
},
"source": [
"%pip install ultralytics\n",
"import ultralytics\n",
"ultralytics.checks()"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 41.1/112.6 GB disk)\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4JnkELT0cIJg"
},
"source": [
"# 1. Predict\n",
"\n",
"YOLO11 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 [YOLO11 Predict Docs](https://docs.ultralytics.com/modes/train/).\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zR9ZbuQCH7FX",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e3ebec6f-658a-4803-d80c-e07d12908767"
},
"source": [
"# Run inference on an image with YOLO11n\n",
"!yolo predict model=yolo11n.pt source='https://ultralytics.com/images/zidane.jpg'"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n",
"100% 5.35M/5.35M [00:00<00:00, 72.7MB/s]\n",
"Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n",
"\n",
"Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...\n",
"100% 49.2k/49.2k [00:00<00:00, 5.37MB/s]\n",
"image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 63.4ms\n",
"Speed: 14.5ms preprocess, 63.4ms inference, 820.9ms postprocess per image at shape (1, 3, 384, 640)\n",
"Results saved to \u001b[1mruns/detect/predict\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
]
}
]
},
{
"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\n",
"Validate a model's accuracy on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset's `val` or `test` splits. The latest YOLO11 [models](https://github.com/ultralytics/ultralytics#models) are downloaded automatically the first time they are used. See [YOLO11 Val Docs](https://docs.ultralytics.com/modes/val/) for more information."
]
},
{
"cell_type": "code",
"metadata": {
"id": "WQPtK1QYVaD_"
},
"source": [
"# Download COCO val\n",
"import torch\n",
"torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
"!unzip -q tmp.zip -d datasets && rm tmp.zip # unzip"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "X58w8JLpMnjH",
"outputId": "af2a5deb-029b-466d-96a4-bd3e406987fa",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"# Validate YOLO11n on COCO8 val\n",
"!yolo val model=yolo11n.pt data=coco8.yaml"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n",
"\n",
"Dataset 'coco8.yaml' images not found ⚠️, missing path '/content/datasets/coco8/images/val'\n",
"Downloading https://ultralytics.com/assets/coco8.zip to '/content/datasets/coco8.zip'...\n",
"100% 433k/433k [00:00<00:00, 15.8MB/s]\n",
"Unzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% 25/25 [00:00<00:00, 1188.35file/s]\n",
"Dataset download success ✅ (1.4s), saved to \u001b[1m/content/datasets\u001b[0m\n",
"\n",
"Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n",
"100% 755k/755k [00:00<00:00, 17.7MB/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 142.04it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco8/labels/val.cache\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:04<00:00, 4.75s/it]\n",
" all 4 17 0.57 0.85 0.847 0.632\n",
" person 3 10 0.557 0.6 0.585 0.272\n",
" dog 1 1 0.548 1 0.995 0.697\n",
" horse 1 2 0.531 1 0.995 0.674\n",
" elephant 1 2 0.371 0.5 0.516 0.256\n",
" umbrella 1 1 0.569 1 0.995 0.995\n",
" potted plant 1 1 0.847 1 0.995 0.895\n",
"Speed: 1.0ms preprocess, 73.8ms inference, 0.0ms loss, 561.4ms postprocess per image\n",
"Results saved to \u001b[1mruns/detect/val\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/val\n"
]
}
]
},
{
"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",
"\n",
"Train YOLO11 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 [YOLO11 Train Docs](https://docs.ultralytics.com/modes/train/) for more information."
]
},
{
"cell_type": "code",
"source": [
"#@title Select YOLO11 🚀 logger {run: 'auto'}\n",
"logger = 'Comet' #@param ['Comet', 'TensorBoard']\n",
"\n",
"if logger == 'Comet':\n",
" %pip install -q comet_ml\n",
" import comet_ml; comet_ml.init()\n",
"elif logger == 'TensorBoard':\n",
" %load_ext tensorboard\n",
" %tensorboard --logdir ."
],
"metadata": {
"id": "ktegpM42AooT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1NcFxRcFdJ_O",
"outputId": "952f35f7-666f-4121-fbdf-2b3a33b28081",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"# Train YOLO11n on COCO8 for 3 epochs\n",
"!yolo train model=yolo11n.pt data=coco8.yaml epochs=3 imgsz=640"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolo11n.pt, data=coco8.yaml, epochs=3, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train3, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train3\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n",
" 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] \n",
" 2 -1 1 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25] \n",
" 3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n",
" 4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] \n",
" 5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n",
" 6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True] \n",
" 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n",
" 8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True] \n",
" 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] \n",
" 10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1] \n",
" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False] \n",
" 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False] \n",
" 17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n",
" 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 19 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False] \n",
" 20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n",
" 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 22 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True] \n",
" 23 [16, 19, 22] 1 464912 ultralytics.nn.modules.head.Detect [80, [64, 128, 256]] \n",
"YOLO11n summary: 319 layers, 2,624,080 parameters, 2,624,064 gradients, 6.6 GFLOPs\n",
"\n",
"Transferred 499/499 items from pretrained weights\n",
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n",
"Freezing layer 'model.23.dfl.conv.weight'\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLO11n...\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco8/labels/train.cache... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<?, ?it/s]\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val.cache... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<?, ?it/s]\n",
"Plotting labels to runs/detect/train/labels.jpg... \n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000119, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)\n",
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
"Image sizes 640 train, 640 val\n",
"Using 2 dataloader workers\n",
"Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
"Starting training for 3 epochs...\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 1/3 0.719G 1.004 3.249 1.367 30 640: 100% 1/1 [00:00<00:00, 1.16it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.07it/s]\n",
" all 4 17 0.58 0.85 0.849 0.631\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 2/3 0.715G 1.31 4.043 1.603 35 640: 100% 1/1 [00:00<00:00, 6.88it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 9.08it/s]\n",
" all 4 17 0.581 0.85 0.851 0.63\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 3/3 0.692G 1.134 3.174 1.599 18 640: 100% 1/1 [00:00<00:00, 6.75it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 11.60it/s]\n",
" all 4 17 0.582 0.85 0.855 0.632\n",
"\n",
"3 epochs completed in 0.003 hours.\n",
"Optimizer stripped from runs/detect/train/weights/last.pt, 5.5MB\n",
"Optimizer stripped from runs/detect/train/weights/best.pt, 5.5MB\n",
"\n",
"Validating runs/detect/train/weights/best.pt...\n",
"Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 23.42it/s]\n",
" all 4 17 0.579 0.85 0.855 0.615\n",
" person 3 10 0.579 0.6 0.623 0.268\n",
" dog 1 1 0.549 1 0.995 0.697\n",
" horse 1 2 0.553 1 0.995 0.675\n",
" elephant 1 2 0.364 0.5 0.528 0.261\n",
" umbrella 1 1 0.571 1 0.995 0.895\n",
" potted plant 1 1 0.857 1 0.995 0.895\n",
"Speed: 0.2ms preprocess, 4.3ms inference, 0.0ms loss, 1.2ms postprocess per image\n",
"Results saved to \u001b[1mruns/detect/train\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/train\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# 4. Export\n",
"\n",
"Export a YOLO11 model to any supported format below with the `format` argument, i.e. `format=onnx`. See [YOLO11 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/) | - | `yolo11n.pt` | ✅ | - |\n",
"| [TorchScript](https://docs.ultralytics.com/integrations/torchscript) | `torchscript` | `yolo11n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |\n",
"| [ONNX](https://docs.ultralytics.com/integrations/onnx) | `onnx` | `yolo11n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |\n",
"| [OpenVINO](https://docs.ultralytics.com/integrations/openvino) | `openvino` | `yolo11n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
"| [TensorRT](https://docs.ultralytics.com/integrations/tensorrt) | `engine` | `yolo11n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |\n",
"| [CoreML](https://docs.ultralytics.com/integrations/coreml) | `coreml` | `yolo11n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |\n",
"| [TF SavedModel](https://docs.ultralytics.com/integrations/tf-savedmodel) | `saved_model` | `yolo11n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |\n",
"| [TF GraphDef](https://docs.ultralytics.com/integrations/tf-graphdef) | `pb` | `yolo11n.pb` | ❌ | `imgsz`, `batch` |\n",
"| [TF Lite](https://docs.ultralytics.com/integrations/tflite) | `tflite` | `yolo11n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
"| [TF Edge TPU](https://docs.ultralytics.com/integrations/edge-tpu) | `edgetpu` | `yolo11n_edgetpu.tflite` | ✅ | `imgsz` |\n",
"| [TF.js](https://docs.ultralytics.com/integrations/tfjs) | `tfjs` | `yolo11n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
"| [PaddlePaddle](https://docs.ultralytics.com/integrations/paddlepaddle) | `paddle` | `yolo11n_paddle_model/` | ✅ | `imgsz`, `batch` |\n",
"| [NCNN](https://docs.ultralytics.com/integrations/ncnn) | `ncnn` | `yolo11n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |"
],
"metadata": {
"id": "nPZZeNrLCQG6"
}
},
{
"cell_type": "code",
"source": [
"!yolo export model=yolo11n.pt format=torchscript"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CYIjW4igCjqD",
"outputId": "5357fa04-6749-4508-effe-8d4078533539"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CPU (Intel Xeon 2.20GHz)\n",
"YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n",
"\n",
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'yolo11n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (5.4 MB)\n",
"\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 2.4.1+cu121...\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.4s, saved as 'yolo11n.torchscript' (10.5 MB)\n",
"\n",
"Export complete (4.2s)\n",
"Results saved to \u001b[1m/content\u001b[0m\n",
"Predict: yolo predict task=detect model=yolo11n.torchscript imgsz=640 \n",
"Validate: yolo val task=detect model=yolo11n.torchscript imgsz=640 data=coco.yaml \n",
"Visualize: https://netron.app\n",
"💡 Learn more at https://docs.ultralytics.com/modes/export\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# 5. Python Usage\n",
"\n",
"YOLO11 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLO11 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 [YOLO11 Python Docs](https://docs.ultralytics.com/usage/python/)."
],
"metadata": {
"id": "kUMOQ0OeDBJG"
}
},
{
"cell_type": "code",
"source": [
"from ultralytics import YOLO\n",
"\n",
"# Load a model\n",
"model = YOLO('yolo11n.yaml') # build a new model from scratch\n",
"model = YOLO('yolo11n.pt') # load a pretrained model (recommended for training)\n",
"\n",
"# Use the model\n",
"results = model.train(data='coco8.yaml', epochs=3) # train the model\n",
"results = model.val() # evaluate model performance on the validation set\n",
"results = model('https://ultralytics.com/images/bus.jpg') # predict on an image\n",
"results = 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",
"\n",
"YOLO11 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 [YOLO11 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\">\n"
],
"metadata": {
"id": "Phm9ccmOKye5"
}
},
{
"cell_type": "markdown",
"source": [
"## 1. Detection\n",
"\n",
"YOLO11 _detection_ models have no suffix and are the default YOLO11 models, i.e. `yolo11n.pt` and are pretrained on COCO. See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for full details.\n"
],
"metadata": {
"id": "yq26lwpYK1lq"
}
},
{
"cell_type": "code",
"source": [
"# Load YOLO11n, train it on COCO128 for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo11n.pt') # load a pretrained YOLO detection model\n",
"model.train(data='coco8.yaml', epochs=3) # train the model\n",
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
],
"metadata": {
"id": "8Go5qqS9LbC5"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## 2. Segmentation\n",
"\n",
"YOLO11 _segmentation_ models use the `-seg` suffix, i.e. `yolo11n-seg.pt` and are pretrained on COCO. See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for full details.\n"
],
"metadata": {
"id": "7ZW58jUzK66B"
}
},
{
"cell_type": "code",
"source": [
"# Load YOLO11n-seg, train it on COCO128-seg for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo11n-seg.pt') # load a pretrained YOLO segmentation model\n",
"model.train(data='coco8-seg.yaml', epochs=3) # train the model\n",
"model('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",
"\n",
"YOLO11 _classification_ models use the `-cls` suffix, i.e. `yolo11n-cls.pt` and are pretrained on ImageNet. See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for full details.\n"
],
"metadata": {
"id": "ax3p94VNK9zR"
}
},
{
"cell_type": "code",
"source": [
"# Load YOLO11n-cls, train it on mnist160 for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo11n-cls.pt') # load a pretrained YOLO classification model\n",
"model.train(data='mnist160', epochs=3) # train the model\n",
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
],
"metadata": {
"id": "5q9Zu6zlL5rS"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## 4. Pose\n",
"\n",
"YOLO11 _pose_ models use the `-pose` suffix, i.e. `yolo11n-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 YOLO11n-pose, train it on COCO8-pose for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo11n-pose.pt') # load a pretrained YOLO pose model\n",
"model.train(data='coco8-pose.yaml', epochs=3) # train the model\n",
"model('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",
"\n",
"YOLO11 _OBB_ models use the `-obb` suffix, i.e. `yolo11n-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 YOLO11n-obb, train it on DOTA8 for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo11n-obb.pt') # load a pretrained YOLO OBB model\n",
"model.train(data='dota8.yaml', epochs=3) # train the model\n",
"model('https://ultralytics.com/images/bus.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",
"!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 updates branch\n",
"!git clone https://github.com/ultralytics/ultralytics -b main\n",
"%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\n",
"for x in 'nsmlx':\n",
" !yolo val model=yolo11{x}.pt data=coco.yaml"
],
"metadata": {
"id": "Wdc6t_bfzDDk"
},
"execution_count": null,
"outputs": []
}
]
}