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Try in Colab

 Ultralytics’ YOLOv5 (“You Only Look Once”) model family enables real-time object detection with convolutional neural networks without all the agonizing pain. W&B is directly integrated into YOLOv5, providing experiment metric tracking, model and dataset versioning, rich model prediction visualization, and more. It’s as easy as running a single pip install before you run your YOLO experiments.
All W&B logging features are compatible with data-parallel multi-GPU training, such as with PyTorch DDP.

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Track core experiments

Simply by installing wandb, you’ll activate the built-in W&B logging features: system metrics, model metrics, and media logged to interactive Dashboards. 
pip install wandb git clone https://github.com/ultralytics/yolov5.git python yolov5/train.py # train a small network on a small dataset
Just follow the links printed to the standard out by wandb.
All these charts and more.

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Customize the integration

By passing a few simple command line arguments to YOLO, you can take advantage of even more W&B features.
  • If you pass a number to --save_period, W&B saves a model version at the end of every save_period epochs. The model version includes the model weights and tags the best-performing model in the validation set.
  • Turning on the --upload_dataset flag will also upload the dataset for data versioning.
  • Passing a number to --bbox_interval will turn on data visualization. At the end of every bbox_interval epochs, the outputs of the model on the validation set will be uploaded to W&B.
  • Model Versioning Only
  • Model Versioning and Data Visualization
python yolov5/train.py --epochs 20 --save_period 1
Every W&B account comes with 100 GB of free storage for datasets and models.
Here’s what that looks like.
Model versioning
Data visualization
With data and model versioning, you can resume paused or crashed experiments from any device, no setup necessary. Check out the Colab for details.