PaddleDetection

Integrate W&B with PaddleDetection to track object detection model training, log metrics, and visualize results.

PaddleDetection is an end-to-end object-detection development kit based on PaddlePaddle. It detects mainstream objects, segments instances, and tracks and detects keypoints using configurable modules such as network components, data augmentations, and losses.

PaddleDetection includes a built-in W&B integration that logs all your training and validation metrics, as well as your model checkpoints and their corresponding metadata. By following this guide, you enable the PaddleDetection WandbLogger so that W&B automatically tracks your object detection experiments, making it easier to compare runs, monitor progress, and reproduce results.

The PaddleDetection WandbLogger logs your training and evaluation metrics to W&B as well as your model checkpoints while training.

For a worked example, read a W&B blog post that illustrates how to integrate a YOLOX model with PaddleDetection on a subset of the COCO2017 dataset.

Sign up and create an API key

An API key authenticates your machine to W&B. You can generate an API key from your user profile.

For a more streamlined approach, create an API key by going directly to User Settings. Copy the newly created API key immediately and save it in a secure location such as a password manager.

1. Click your user profile icon in the upper right corner.
2. Select User Settings, then scroll to the API Keys section.

Install the wandb library and log in

To install the wandb library locally and log in:

Command Line

1. Set the WANDB_API_KEY environment variable to your API key.

   export WANDB_API_KEY=[YOUR-API-KEY]
   

2. Install the wandb library and log in.

   pip install wandb
   
   wandb login
   

Python

pip install wandb

import wandb
wandb.login()

Python notebook

!pip install wandb

import wandb
wandb.login()

Activate the WandbLogger in your training script

With the wandb library installed and your machine authenticated, turn on the WandbLogger for your PaddleDetection training job. You can do this either through command-line arguments or by editing your config file.

Command Line

To use wandb through 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 argument must contain the prefix wandb-. For example, any argument to pass to wandb.init() gets the wandb- prefix.

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

`config.yml`

Alternatively, you can configure the integration declaratively. Add the wandb arguments to the config.yml file under the wandb key:

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

When you run your train.py file, it generates a link to your W&B Dashboard, where you can view your training and validation metrics, model checkpoints, and run metadata in real time.

Feedback or issues

If you have any feedback or issues about the W&B integration, open an issue on the PaddleDetection GitHub or email [e-mail].