"This **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",
"\n",
"Ultralytics 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",
"\n",
"Find 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",
"\n",
"Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license)."
]
},
{
"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",
"YOLO26 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/predict/)."
"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."
"Train 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."
"Plotting labels to /content/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 MuSGD(lr=0.000119, momentum=0.9) with parameter groups 0 weight(decay=0.0), 0 weight(decay=0.0005), 24 bias(decay=0.0)\n",
"Image sizes 640 train, 640 val\n",
"Using 2 dataloader workers\n",
"Logging results to \u001b[1m/content/runs/detect/train\u001b[0m\n",
"Results saved to \u001b[1m/content/runs/detect/train\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/train\n"
]
}
],
"execution_count": 4
},
{
"cell_type": "markdown",
"source": [
"# 4. Export\n",
"\n",
"Export 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",
"Validate: yolo val task=detect model=yolo26n.torchscript imgsz=640 data=/home/lq/codes/ultralytics/ultralytics/cfg/datasets/coco.yaml \n",
"Visualize: https://netron.app\n",
"💡 Learn more at https://docs.ultralytics.com/modes/export\n"
]
}
],
"execution_count": 5
},
{
"cell_type": "markdown",
"source": [
"# 5. Python Usage\n",
"\n",
"YOLO26 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": "bpF9-vS_DAaf"
}
},
{
"cell_type": "code",
"source": [
"from ultralytics import YOLO\n",
"\n",
"# Load a model\n",
"model = YOLO('yolo26n.yaml') # build a new model from scratch\n",
"model = YOLO('yolo26n.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": "Phm9ccmOKye5"
},
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"# 6. Tasks\n",
"\n",
"YOLO26 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",
"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"
