Seeking Guidance on Tuning Data Augmentation Parameters in YOLOX

Dear YOLOX Team,

I hope this message finds you well. I am currently working with YOLOX and am interested in tuning the data augmentation parameters to optimize the performance for my specific use case. However, I am facing some challenges in understanding the best approach to adjust these parameters effectively.

Here are the default settings I am currently using:

• Degrees: 10.0
• Translate: 0.1
• Scale: (0.1, 2)
• Mosaic Scale: (0.8, 1.6)
• Shear: 2.0
• Perspective: 0.0
• Mixup: Enabled

I would greatly appreciate if you could provide some guidance or best practices on how to approach tuning these augmentation parameters. Specifically, I'm looking for advice on how to determine the optimal values for my dataset and model size, and any insights on the impact of these parameters on model training and performance.

Thank you for your time and assistance. I look forward to your expert advice.

Best regards

No file, test, or entry point is named in the issue. Start by locating YOLOX's data-augmentation configuration and documentation, then determine whether the project has guidance or experiments covering these parameters; the work would be complete when actionable tuning guidance and dataset/model considerations are documented.