> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-docs-2626.mintlify.site/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 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 to interactively validate predictions with a single command-line argument.
This guide shows you how to authenticate with W\&B, install the integration, and enable W\&B logging when you train a YOLOX object detection model so you can track metrics and inspect predictions in the W\&B UI.
## 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, go to [User Settings](https://wandb.ai/settings) and create an API key. Copy the 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](/models/track/environment-variables/) to your API key. Replace values enclosed in `<>` with your own:
```bash theme={null}
export WANDB_API_KEY=
```
2. Install the `wandb` library and log in.
```shell theme={null}
pip install wandb
wandb login
```
```bash theme={null}
pip install wandb
```
```python theme={null}
import wandb
wandb.login()
```
```notebook theme={null}
!pip install wandb
import wandb
wandb.login()
```
## Log metrics
With the `wandb` library installed and your machine authenticated, you can enable W\&B logging from the YOLOX training script.
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 W\&B logs to tables for model evaluation.
Replace the following placeholders before you run the command:
* ``: The name of your W\&B project.
* ``: Your W\&B entity (username or team name).
* ``: A name for this training run.
* ``: A unique identifier for this run.
* ``: The directory where YOLOX saves checkpoints and logs.
* ``: The number of validation images to log.
* ``: Whether to log checkpoints (`true` or `false`).
```shell theme={null}
# Log in to W&B
wandb login
# Call your YOLOX training script with the wandb logger argument
python tools/train.py .... --logger wandb \
wandb-project \
wandb-entity \
wandb-name \
wandb-id \
wandb-save_dir \
wandb-num_eval_imges \
wandb-log_checkpoints
```
## Example
After your training run starts, YOLOX streams training, validation, and system metrics to your W\&B project, where you can compare runs and inspect predictions. See the following example for what a populated dashboard looks like.
See the [example dashboard with YOLOX training and validation metrics](https://wandb.ai/manan-goel/yolox-nano/runs/3pzfeom).
If you have questions or issues about this W\&B integration, open an issue in the [YOLOX repository](https://github.com/Megvii-BaseDetection/YOLOX).