> ## 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.

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

# YOLOX

[YOLOX](https://github.com/Megvii-BaseDetection/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.

<Note>
  For a more streamlined approach, create an API key by going directly to [User Settings](https://wandb.ai/settings). Copy the newly created API key immediately and save it in a secure location such as a password manager.
</Note>

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:

<Tabs>
  <Tab title="Command Line">
    1. Set the `WANDB_API_KEY` [environment variable](/models/track/environment-variables/) to your API key.

       ```bash theme={null}
       export WANDB_API_KEY=<your_api_key>
       ```

    2. Install the `wandb` library and log in.

       ```shell theme={null}
       pip install wandb

       wandb login
       ```
  </Tab>

  <Tab title="Python">
    ```bash theme={null}
    pip install wandb
    ```

    ```python theme={null}
    import wandb
    wandb.login()
    ```
  </Tab>

  <Tab title="Python notebook">
    ```notebook theme={null}
    !pip install wandb

    import wandb
    wandb.login()
    ```
  </Tab>
</Tabs>

## 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 theme={null}
# 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 ->](https://wandb.ai/manan-goel/yolox-nano/runs/3pzfeom)

<Frame>
  <img src="https://mintcdn.com/wb-21fd5541/w-lBKSCruauC3-2f/images/integrations/yolox_example_dashboard.png?fit=max&auto=format&n=w-lBKSCruauC3-2f&q=85&s=399c16b358ab9aa993c45517b5759875" alt="YOLOX training dashboard" width="3114" height="2394" data-path="images/integrations/yolox_example_dashboard.png" />
</Frame>

Any questions or issues about this W\&B integration? Open an issue in the [YOLOX repository](https://github.com/Megvii-BaseDetection/YOLOX).