Autodistill or Reparameterize?

[adrielkuek]

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Question

So Robovision provides a framework with Autodistill to transfer knowledge from larger foundational models into smaller models on custom data that runs faster. https://roboflow.com/train/yolo-world-and-yolov8. I'm just curious on the differences between this framework, and that of Reparameterization of Yolo-World with the same custom dataset to improve efficiency on custom datasets (https://github.com/AILab-CVC/YOLO-World/blob/master/docs/reparameterize.md). From the Yolo-World paper, it does seem that reparameterization, at least for coco dataset's vocabulary, does seem to perform slightly better with Yolov8-fine-tuned.

Just wondering are there any merits to both of the methods? Have anybody evaluated either of the approach and which would be the recommended approach? Thanks!

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[SkalskiP]

Hi, @adrielkuek. Let me first convert this issue into a discussion.

[SkalskiP]

Hi @adrielkuek 👋🏻 This question should probably be directed to the autodistill team, but I'll try to answer it as best as I can.

I think if you're using YOLO-World as a teacher model in autodistill to ultimately transfer that knowledge to YOLOv8, you're right. It's better to reparameterize YOLO-World. But autodistill also allows you to use other models as teacher models.