You'll Easily Log Everything if you combine W&B with YOLOv5.
Weights & Biases 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 installbefore you run your YOLO experiments!
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!
But that's not all! By passing a few simple command line arguments to YOLO, you can take advantage of even more W&B features.
- Passing a number to
--save_periodwill turn on model versioning. At the end of every
save_periodepochs, the model weights will be saved to W&B. The best-performing model on the validation set will be tagged automatically.
Model Versioning Only
Model Versioning and Data Visualization
python yolov5/train.py --epochs 20 --save_period 1
python yolov5/train.py --epochs 20 --save_period 1 \
--upload_dataset --bbox_interval 1
Here's what that looks like.
Model Versioning: the latest and the best versions of the model are identified.
Data Visualization: compare the input image to the model's outputs and example-wise metrics.