Comments (1)
Hi @merlinarer,
Thanks for your message. Yes, one common evaluation is end-to-end fine-tuning with 100% labels. However, with 1% labels, iBOT achieves the best performance with logistic regression on the extracted (frozen) representations.
See Table 12 in their paper comparing fine-tuning to linear probing on 1% labels.
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Related Issues (20)
- module 'cyanure' has no attribute 'preprocess' HOT 4
- The detail setting for 1% evaluation HOT 1
- Include full checkpoint HOT 1
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- why did not take block-wise mask strategy? HOT 1
- vit-b-16 config
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- Using just the encoder
- How to change MSN loss to PMSN loss? (from paper "The Hidden Uniform Cluster Prior in Self-Supervised Learning") HOT 1
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- No such file HOT 1
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