Comments (2)
First answer by @andsteing
- We used our default transfer config: big_vision/configs/transfer.py, which uses inception crop (config -> preprocessing) and random horizontal flip (config).
- As for pre-training, refer to these configs: big_vision/configs/vit_i21k.py and big_vision/configs/vit_i1k.py (see module pydoc for more information).
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And addition by me, checking the old training logs and providing free-form text summary:
We select these on minival (held out from train):
- We sweep lr in 0.03, 0.01, 0.003, 0.001
- We sweep fine-tuning steps in 500, 2500, 10k, 20k
Couple probably important and fixed (not swept) settings, should be visible in the config Andreas linked:
- inception-crop and flip-lr almost everywhere except where it's a completely silly thing to do (see BiT paper appendix)
- no dropout or stochastic depth
- no mixup, no randaugment
- no weight decay
- batch-size 512
- softmax cross-entropy loss
- SGDMomentum optimizer, momentum 0.9 in bfloat16.
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Related Issues (20)
- questions about t-SNE visualization in FlexiViT HOT 1
- requirements issue HOT 2
- Announcement: big_vision is transitioning from jax.pmap to jax.jit. HOT 1
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- Question about SigLIP HOT 7
- Memory Efficient Attention integration HOT 2
- Contrastive Input Pipeline HOT 2
- [BUG] in big_vision.models.proj.flexi.vit HOT 1
- Reproduced result for flexivit HOT 2
- SigLIP and canonicalize
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- FlexiVit is also flexible with image resolution?
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