I’m planning to fine-tune a YOLO model for a custom object detection task. There seem to be two main approaches:
Clone the official YOLO GitHub repository (e.g., YOLOv5 or YOLOv8), adjust the codebase directly, and train the model locally.
Use the Hugging Face Transformers library or similar high-level frameworks that provide pre-trained YOLO models and fine-tuning pipelines.
I’m looking for insights and experiences regarding these two approaches. Specifically:
What are the advantages and disadvantages of cloning and modifying the original YOLO repo compared to using Hugging Face’s ecosystem?
How do they compare in terms of ease of use, flexibility, reproducibility, and community support?
Are there any significant performance differences or trade-offs?
Which approach is more future-proof for ongoing development and integration into production pipelines?
Any practical tips or references to tutorials/examples would also be appreciated!
Thanks in advance!
