Release v1.0.17
July 7, 2025
- MobileNet-v5 backbone tweaks for improved Google Gemma 3n behaviour (to pair with updated official weights)
- Add stem bias (zero'd in updated weights, compat break with old weights)
- GELU -> GELU (tanh approx). A minor change to be closer to JAX
- Add two arguments to layer-decay support, a min scale clamp and 'no optimization' scale threshold
- Add 'Fp32' LayerNorm, RMSNorm, SimpleNorm variants that can be enabled to force computation of norm in float32
- Some typing, argument cleanup for norm, norm+act layers done with above
- Support Naver ROPE-ViT (https://github.com/naver-ai/rope-vit) in
eva.py, add RotaryEmbeddingMixed module for mixed mode, weights on HuggingFace Hub
|model |img_size|top1 |top5 |param_count| |--------------------------------------------------|--------|------|------|-----------| |vit_large_patch16_rope_mixed_ape_224.naver_in1k |224 |84.84 |97.122|304.4 | |vit_large_patch16_rope_mixed_224.naver_in1k |224 |84.828|97.116|304.2 | |vit_large_patch16_rope_ape_224.naver_in1k |224 |84.65 |97.154|304.37 | |vit_large_patch16_rope_224.naver_in1k |224 |84.648|97.122|304.17 | |vit_base_patch16_rope_mixed_ape_224.naver_in1k |224 |83.894|96.754|86.59 | |vit_base_patch16_rope_mixed_224.naver_in1k |224 |83.804|96.712|86.44 | |vit_base_patch16_rope_ape_224.naver_in1k |224 |83.782|96.61 |86.59 | |vit_base_patch16_rope_224.naver_in1k |224 |83.718|96.672|86.43 | |vit_small_patch16_rope_224.naver_in1k |224 |81.23 |95.022|21.98 | |vit_small_patch16_rope_mixed_224.naver_in1k |224 |81.216|95.022|21.99 | |vit_small_patch16_rope_ape_224.naver_in1k |224 |81.004|95.016|22.06 | |vit_small_patch16_rope_mixed_ape_224.naver_in1k |224 |80.986|94.976|22.06 |
- Some cleanup of ROPE modules, helpers, and FX tracing leaf registration
- Preparing version 1.0.17 release
What's Changed
- Adding Naver rope-vit compatibility to EVA ViT by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2529
- Update no_grad usage to inference_mode if possible by @GuillaumeErhard in https://github.com/huggingface/pytorch-image-models/pull/2534
- Add a min layer-decay scale clamp, and no optimization threshold to exclude groups from optimization by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2537
- Add stem_bias option to MNV5. Resolve the norm layer so can pass string. by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2538
- Add flag to enable float32 computation for normalization (norm + affine) by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2536
- fix: mnv5 conv_stem bias and GELU with approximate=tanh by @RyanMullins in https://github.com/huggingface/pytorch-image-models/pull/2533
- Fixup casting issues for weights/bias in fp32 norm layers by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2539
- Fix H, W ordering for xy indexing in ROPE by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2541
- Fix 3 typos in README.md by @robin-ede in https://github.com/huggingface/pytorch-image-models/pull/2544
New Contributors
- @GuillaumeErhard made their first contribution in https://github.com/huggingface/pytorch-image-models/pull/2534
- @RyanMullins made their first contribution in https://github.com/huggingface/pytorch-image-models/pull/2533
- @robin-ede made their first contribution in https://github.com/huggingface/pytorch-image-models/pull/2544
Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.16...v1.0.17