YOLOv5 now comes with a native Weights & Biases integration that tracks your model pipelines – including model performance, hyperparameters, GPU usage, predictions, and datasets.
Your model metrics are automatically tracked by YOLOv5 if you have
pip install wandb.
wandbis installed, you can run any YOLOv5 training script as you normally would. For example:
python train.py --batch 16 --epochs 500 --data coco128.yaml --cfg yolov5s.yaml
Each W&B project has a dashboard that contains information about all the experiments in that project. Here's an example dashboard of a YOLOv5 project. You can sort, filter, group runs based on any of the logged metrics or configuration parameters.
Judging object detection models manually is painful. Are you tired of running inferences on model checkpoints at various stages of training? With YOLOv5, you get an interactive bounding box debugging plot where you can play around with confidence parameters to choose the optimal model and thresholds. Here's an example:
You can write a W&B Report(like this one) about your projects and share it with the world.