YOLOX

YOLOX is an anchor-free version of YOLO for object detection. You can use the YOLOX Weights & Biases 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 Weights & Biases, install the integration, and enable Weights & Biases logging when you train a YOLOX object detection model so you can track metrics and inspect predictions in the W&B UI. An API key authenticates your machine to Weights & Biases. You can generate an API key from your user profile.

For a more streamlined approach, go to User settings and create an API key. Copy the API key immediately and save it in a secure location such as a password manager.

Click the account selector in the upper-right corner.
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
Python
Python notebook

Set the WANDB_API_KEY environment variable to your API key. Replace values enclosed in <> with your own:
export WANDB_API_KEY=<your_api_key>
Install the wandb library and log in.
pip install wandb wandb login

pip install wandb

import wandb
wandb.login()

!pip install wandb

import wandb
wandb.login()

Log metrics

With the wandb library installed and your machine authenticated, you can enable Weights & Biases 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() expects. Prepend each argument with wandb-. num_eval_imges controls the number of validation set images and predictions that Weights & Biases logs to tables for model evaluation. Replace the following placeholders before you run the command:

<project-name>: The name of your Weights & Biases project.
<entity>: Your Forge team name.
<run-name>: A name for this training run.
<run-id>: A unique identifier for this run.
<save-dir>: The directory where YOLOX saves checkpoints and logs.
<num-images>: The number of validation images to log.
<bool>: Whether to log checkpoints (true or false).

# 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 <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

After your training run starts, YOLOX streams training, validation, and system metrics to your Weights & Biases 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.

If you have questions or issues about this Weights & Biases integration, open an issue in the YOLOX repository.

Weights & Biases

Guides

Integrations

Reference

Support

Install the `wandb` library and log in

Log metrics

Example

​Sign up and create an API key

​Install the wandb library and log in

​Log metrics

​Example

Sign up and create an API key

Install the `wandb` library and log in

Log metrics

Example