Training CLI Fork for YOLOv9

[mapo80]

Hi everyone,

I've been working on a fork that adds a complete training infrastructure to the YOLOv9 codebase. The original repository focuses on model architecture and research—this fork adds everything needed to actually train and deploy models.

What's Included

Training Infrastructure

• Complete CLI with YAML configuration and command-line overrides
• PyTorch Lightning integration for clean, scalable training
• Multi-GPU support with automatic DDP
• Mixed precision training (FP16 and BF16)
• Gradient accumulation and clipping

Data Pipeline

• COCO JSON and YOLO TXT format support
• Dataset caching for faster loading
• Augmentation suite: Mosaic 4/9, MixUp, CutMix, RandomPerspective, RandomHSV, RandomFlip

Evaluation & Monitoring

• Full COCO evaluation metrics (mAP, AP50, AP75, AR)
• Confusion matrix and PR curve generation
• Rich progress bars and eval dashboard
• TensorBoard integration

Export

• ONNX export
• TFLite export (FP32, FP16, INT8 quantization)
• SavedModel format

Testing

• 301 unit tests covering data loading, augmentations, metrics, and training pipeline

Repository

https://github.com/mapo80/YOLO

Feedback and contributions are welcome!

[henrytsui000]

Hi,

Thanks so much for your feedback and for sharing your repo—it looks like a great effort and contribution to the ecosystem!

There are two points I’d like to raise:

Feature Overlap: Some of the features you mentioned (like mixed precision training, COCO evaluation, multi-format export, etc.) are already implemented or in progress in this repo. We’re working to keep the core lean while supporting flexible extensions, so any ideas around modularization are appreciated!

License Concerns: I noticed some use of AI agents in code generation. While that’s a valid approach, it raises a concern regarding license compliance. Specifically, it's hard to verify whether AI-generated code has any indirect dependency on non-MIT-licensed implementations. Since this repo is MIT-licensed, I want to ensure all contributions align with that to avoid legal or attribution issues.

Thanks again for sharing, and feel free to open discussions or PRs if you'd like to upstream any modular improvements!

Best,
Henry Tsui