Weights & Biases Logging 🆕
📚 This guide explains how to use Weights & Biases (W&B) with YOLOv5 🚀.
About Weights & Biases
Think of W&B like GitHub for machine learning models. With a few lines of code, save everything you need to debug, compare and reproduce your models — architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions.
W&B’s lightweight integrations work with any Python script, and you can sign up for a free account and start tracking and visualizing models in 5 minutes.
Used by top researchers including teams at OpenAI, Lyft, Github, and MILA, W&B is part of the new standard of best practices for machine learning. How W&B can help you optimize your machine learning workflows:
- Debug model performance in real time
- GPU usage, visualized automatically
- Custom charts for powerful, extensible visualization
- Share insights interactively with collaborators
- Optimize hyperparameters efficiently
- Track datasets, pipelines, and production models
Before You Start
Clone this repo and install requirements.txt dependencies, including Python>=3.8 and PyTorch>=1.7. Also install the W&B pip package
$ git clone https://github.com/ultralytics/yolov5 # clone repo $ cd yolov5 $ pip install -r requirements.txt wandb # install
When you first train, W&B will prompt you to create a new account and will generate an API key for you. If you are an existing user you can retrieve your key from https://wandb.ai/authorize. This key is used to tell W&B where to log your data. You only need to supply your key once, and then it is remembered on the same device.
W&B will create a cloud project (default is 'YOLOv5') for your training runs, and each new training run will be provided a unique run name within that project as project/name. You can also manually set your project and run name as:
$ python train.py --project ... --name ...
Run information streams from your environment to the W&B cloud console as you train. This allows you to monitor and even cancel runs in realtime. All important information is logged:
- Training losses
- Validation losses
- Metrics: Precision, Recall, mAP@0.5, mAP@0.5:0.95
- Learning Rate over time
- GPU: Type, GPU Utilization, power, temperature, CUDA memory usage
- System: Disk I/0, CPU utilization, RAM memory usage
- Environment: OS and Python types, Git repository and state, training command
W&B Reports can be created from your saved runs for sharing online. Once a report is created you will receive a link you can use to publically share your results. Here is an example report created from the COCO128 tutorial trainings of all four YOLOv5 models (link).
- Google Colab and Kaggle notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), testing (test.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.