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 various mainstream objects, segments instances, and tracks and detects keypoints using configurable modules such as network components, data augmentations, and losses.

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

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

Read a W&B blog post which 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

Command Line

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 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

`config.yml`

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.

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].