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"
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" ![](\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\")
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" \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",
"
![\"Ultralytics](\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg\")
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"
![\"Run](\"https://assets.paperspace.io/img/gradient-badge.svg\")
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"
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
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"
![\"Open](\"https://kaggle.com/static/images/open-in-kaggle.svg\")
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"\n",
"
![\"Discord\"](\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\")
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"
![\"Ultralytics](\"https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue\")
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"
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"
![](\"https://user-images.githubusercontent.com/26833433/212889447-69e5bdf1-5800-4e29-835e-2ed2336dede2.jpg\")
"
]
},
{
"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 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": "X58w8JLpMnjH"
},
"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": "ZY2VXXXu74w5",
"outputId": "1decfc78-03e0-4519-ac06-9596bd4dfcb9",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"# Validate YOLO26n on COCO8 val\n",
"!yolo val model=yolo26n.pt data=coco8.yaml"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics 8.4.4 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 15095MiB)\n",
"YOLO26n summary (fused): 122 layers, 2,408,932 parameters, 0 gradients, 5.4 GFLOPs\n",
"\n",
"WARNING ⚠️ Dataset 'coco8.yaml' images not found, missing path '/content/datasets/coco8/images/val'\n",
"\u001b[KDownloading https://ultralytics.com/assets/coco8.zip to '/content/datasets/coco8.zip': 100% ━━━━━━━━━━━━ 432.8KB 36.5MB/s 0.0s\n",
"\u001b[KUnzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% ━━━━━━━━━━━━ 25/25 5.1Kfiles/s 0.0s\n",
"Dataset download success ✅ (0.7s), saved to \u001b[1m/content/datasets\u001b[0m\n",
"\n",
"\u001b[KDownloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf': 100% ━━━━━━━━━━━━ 755.1KB 106.1MB/s 0.0s\n",
"\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1236.6±358.3 MB/s, size: 54.0 KB)\n",
"\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val... 4 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 4/4 103.0it/s 0.0s\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco8/labels/val.cache\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.1s/it 1.1s\n",
" all 4 17 0.849 0.654 0.907 0.667\n",
" person 3 10 1 0.427 0.766 0.378\n",
" dog 1 1 0.718 1 0.995 0.597\n",
" horse 1 2 0.8 1 0.995 0.798\n",
" elephant 1 2 0.854 0.5 0.695 0.438\n",
" umbrella 1 1 0.724 1 0.995 0.895\n",
" potted plant 1 1 1 0 0.995 0.895\n",
"Speed: 1.0ms preprocess, 16.3ms inference, 0.0ms loss, 0.1ms postprocess per image\n",
"Results saved to \u001b[1m/content/runs/detect/val\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/val\n"
]
}
],
"execution_count": 3
},
{
"cell_type": "markdown",
"source": [
"# 3. Train\n",
"\n",
"![](\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"/)
![](\"https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png\")
"
],
"metadata": {
"id": "yq26lwpYK1lq"
}
},
{
"cell_type": "markdown",
"source": [
"## 1. Detection\n",
"\n",
"YOLO26 _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": "8Go5qqS9LbC5"
}
},
{
"cell_type": "code",
"source": [
"# Load YOLO26n, train it on COCO128 for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo26n.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": "7ZW58jUzK66B"
},
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"## 2. Segmentation\n",
"\n",
"YOLO26 _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": "WFPJIQl_L5HT"
}
},
{
"cell_type": "code",
"source": [
"# Load YOLO26n-seg, train it on COCO128-seg for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo26n-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": "ax3p94VNK9zR"
},
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"## 3. Classification\n",
"\n",
"YOLO26 _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": "5q9Zu6zlL5rS"
}
},
{
"cell_type": "code",
"source": [
"# Load YOLO26n-cls, train it on mnist160 for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo26n-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": "SpIaFLiO11TG"
},
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"## 4. Pose\n",
"\n",
"YOLO26 _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": "si4aKFNg19vX"
}
},
{
"cell_type": "code",
"source": [
"# Load YOLO26n-pose, train it on COCO8-pose for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo26n-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": "cf5j_T9-B5F0"
},
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"## 5. Oriented Bounding Boxes (OBB)\n",
"\n",
"YOLO26 _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": "IJNKClOOB5YS"
}
},
{
"cell_type": "code",
"metadata": {
"id": "IEijrePND_2I"
},
"source": [
"# Load YOLO26n-obb, train it on DOTA8 for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo26n-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/boats.jpg') # predict on an image"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"# Appendix\n",
"\n",
"Additional content below."
],
"metadata": {
"id": "pIdE6i8C3LYp"
}
},
{
"cell_type": "code",
"source": [
"# Pip install from source\n",
"!uv pip install git+https://github.com/ultralytics/ultralytics@main"
],
"metadata": {
"id": "uRKlwxSJdhd1"
},
"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": "GtPlh7mcCGZX"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Run tests (Git clone only)\n",
"!pytest ultralytics/tests"
],
"metadata": {
"id": "Wdc6t_bfzDDk"
},
"execution_count": null,
"outputs": []
},
{
"metadata": {
"id": "_icJkqGnXFv8"
},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"# Validate multiple models\n",
"for x in 'nsmlx':\n",
" !yolo val model=yolo26{x}.pt data=coco.yaml"
]
}
]
}