\n",
"\n",
"
\n",
" ![](\"https://raw.githubusercontent.com/ultralytics/assets/master/yolov5/v70/splash.png\")
\n",
"\n",
"\n",
"
\n",
"
![\"Run](\"https://assets.paperspace.io/img/gradient-badge.svg\")
\n",
"
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
\n",
"
![\"Open](\"https://kaggle.com/static/images/open-in-kaggle.svg\")
\n",
"
\n",
"\n",
"This
YOLOv5 🚀 notebook by
Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See
GitHub for community support or
contact us for professional support.\n",
"\n",
"
![](\"https://user-images.githubusercontent.com/26833433/127574988-6a558aa1-d268-44b9-bf6b-62d4c605cc72.jpg\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0eq1SMWl6Sfn"
},
"source": [
"# 2. Validate\n",
"Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
]
},
{
"cell_type": "code",
"metadata": {
"id": "WQPtK1QYVaD_",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"300b4d5355ef4967bd5246afeef6eef5",
"84e6829bb88845a8a4f42700b8496925",
"c038e52d41bf4d5b9602930c3d074087",
"2667604641764341b0bc8c6afea438fd",
"98b3a4806ed14102b0d75e6c571d6134",
"c66a77395e42424d904699edcbb67291",
"c4bbc15bf853439399dbcf1d40a5a407",
"0aaabfac395b43afbdd6d752c502bbf6",
"3786d970492b4aa38f886f2572fd958c",
"b86d0f2d7be74cebbcaa884b53123eeb",
"fa7b1497925a457f89286a71f073f416"
]
},
"outputId": "61ffec5e-90ea-44f6-c0ea-b006e6e7072f"
},
"source": [
"# Download COCO val\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": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0.00/780M [00:00![](\"https://github.com/ultralytics/assets/raw/master/im/integrations-loop.png\"/)
\n",
"Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n",
"![](\"https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a275ad4b4ac20cd2e21a_roboflow-annotate.gif\"/)
![\"yolo-ui\"](\"https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png\")
"
],
"metadata": {
"id": "nWOsI5wJR1o3"
}
},
{
"cell_type": "markdown",
"source": [
"## ClearML Logging and Automation 🌟 NEW\n",
"\n",
"[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
"\n",
"- `pip install clearml`\n",
"- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
"\n",
"You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
"\n",
"You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n",
"\n",
"\n",
"![\"ClearML](\"https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg\")
"
],
"metadata": {
"id": "Lay2WsTjNJzP"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "-WPvRbS5Swl6"
},
"source": [
"## Local Logging\n",
"\n",
"Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
"\n",
"This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n",
"\n",
"![\"Local](\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\")
\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zelyeqbyt3GD"
},
"source": [
"# Environments\n",
"\n",
"YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
"\n",
"- **Notebooks** with free GPU: ![\"Docker](\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\")
\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6Qu7Iesl0p54"
},
"source": [
"# Status\n",
"\n",
"\n",
"\n",
"If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IEijrePND_2I"
},
"source": [
"# Appendix\n",
"\n",
"Additional content below."
]
},
{
"cell_type": "code",
"metadata": {
"id": "GMusP4OAxFu6"
},
"source": [
"# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
"import torch\n",
"\n",
"model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # yolov5n - yolov5x6 or custom\n",
"im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n",
"results = model(im) # inference\n",
"results.print() # or .show(), .save(), .crop(), .pandas(), etc."
],
"execution_count": null,
"outputs": []
}
]
}