YOLOX ↗

Documentation Index#

Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt Use this file to discover all available pages before exploring further.

How to integrate W&B with YOLOX.

YOLOX is an anchor-free version of YOLO with strong performance for object detection. You can use the YOLOX W&B integration to turn on logging of metrics related to training, validation, and the system, and you can interactively validate predictions with a single command-line argument.

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:

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
       ```

    pip install wandb
    ```

```python
    import wandb
    wandb.login()
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Python notebook"></span>
```notebook
    !pip install wandb

    import wandb
    wandb.login()
    ```
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## Log metrics

Use the `--logger wandb` command line argument to turn on logging with wandb. Optionally you can also pass all of the arguments that [`wandb.init()`](/models/ref/python/functions/init) expects; prepend each argument with `wandb-`.

`num_eval_imges` controls the number of validation set images and predictions that are  logged to W\&B tables for model evaluation.

```shell
# login to wandb
wandb login

# call your yolox training script with the `wandb` logger argument
python tools/train.py .... --logger wandb \
                wandb-project <project-name> \
                wandb-entity <entity>
                wandb-name <run-name> \
                wandb-id <run-id> \
                wandb-save_dir <save-dir> \
                wandb-num_eval_imges <num-images> \
                wandb-log_checkpoints <bool>

Example#

Example dashboard with YOLOX training and validation metrics ->

Any questions or issues about this W&B integration? Open an issue in the YOLOX repository.