PaddleDetection

PaddleDetection is an end-to-end object-detection development kit based on PaddlePaddle. It implements varied mainstream object detection, instance segmentation, tracking and keypoint detection algorithms in modular design with configurable modules such as network components, data augmentations and losses.

PaddleDetection now comes with a built in W&B integration which logs all your training and validation metrics, as well as your model checkpoints and their corresponding metadata.

Example Blog and Colab

Read our blog here to see how to train a YOLOX model with PaddleDetection on a subset of the COCO2017 dataset. This also comes with a Google Colab and the corresponding live W&B dashboard is available here

The PaddleDetection WandbLogger

The PaddleDetection WandbLogger will log your training and evaluation metrics to Weights & Biases as well as your model checkpoints while training.

Using PaddleDetection with Weights & Biases

Sign up and log in to W&B

Sign up for a free Weights & Biases account, then pip install the wandb library. To login, you'll need to be signed in to you account at www.wandb.ai. Once signed in you will find your API key on the Authorize page.

Command Line

pip install wandb

wandb login

Notebook

!pip install wandb

wandb.login()

Activating the WandbLogger in your Training Script

Using the CLI

To use wandb via arguments to train.py in PaddleDetection:

• Add the --use_wandb flag
• The first wandb arguments must be preceded by -o (you only need to pass this once)
• Each individual wandb argument must contain the prefix wandb- . For example any argument to be passed to wandb.init would get the wandb- prefix

python tools/train.py 
    -c config.yml \ 
    --use_wandb \
    -o \ 
    wandb-project=MyDetector \
    wandb-entity=MyTeam \
    wandb-save_dir=./logs

Using a config.yml file

You can also activate wandb via the config file. Add the wandb arguments to the config.yml file under the wandb header like so:

wandb:
  project: MyProject
  entity: MyTeam
  save_dir: ./logs

Once you run your train.py file with Weights & Biases turned on, a link will be generated to bring you to your W&B dashboard:

Feedback or Issues

If you have any feedback or issues about the Weights & Biases integration please open an issue on the PaddleDetection GitHub or email support@wandb.com