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Did you use the default fine-tuning recipe for supervised Swin-T? If yes, you could probably try to tweak the fine-tuning learning rate, layer decay rate, etc. Setting them smaller usually helps when fine-tuning self-supervised models.
Hi.
I compared iBOT Swin-T and supervised Swin-T as pre-trained models for COCO, getting the following results:
Supervised Swin-T: mAP 0.432
iBOT Swin-T: mAP 0.428
The detection framework is Mask R-CNN 1x with multi-scale training. Do you have any ideas on that?
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