Bump timm from 1.0.15 to 1.0.16

[dependabot]

Bumps timm from 1.0.15 to 1.0.16.

Release notes

Sourced from timm's releases.

Release v1.0.16

June 26, 2025

• MobileNetV5 backbone (w/ encoder only variant) for Gemma 3n image encoder
• Version 1.0.16 released

June 23, 2025

• Add F.grid_sample based 2D and factorized pos embed resize to NaFlexViT. Faster when lots of different sizes (based on example by https://github.com/stas-sl).
• Further speed up patch embed resample by replacing vmap with matmul (based on snippet by https://github.com/stas-sl).
• Add 3 initial native aspect NaFlexViT checkpoints created while testing, ImageNet-1k and 3 different pos embed configs w/ same hparams.

Model	Top-1 Acc	Top-5 Acc	Params (M)	Eval Seq Len
naflexvit_base_patch16_par_gap.e300_s576_in1k	83.67	96.45	86.63	576
naflexvit_base_patch16_parfac_gap.e300_s576_in1k	83.63	96.41	86.46	576
naflexvit_base_patch16_gap.e300_s576_in1k	83.50	96.46	86.63	576

• Support gradient checkpointing for forward_intermediates and fix some checkpointing bugs. Thanks https://github.com/brianhou0208
• Add 'corrected weight decay' (https://arxiv.org/abs/2506.02285) as option to AdamW (legacy), Adopt, Kron, Adafactor (BV), Lamb, LaProp, Lion, NadamW, RmsPropTF, SGDW optimizers
• Switch PE (perception encoder) ViT models to use native timm weights instead of remapping on the fly
• Fix cuda stream bug in prefetch loader

June 5, 2025

• Initial NaFlexVit model code. NaFlexVit is a Vision Transformer with:
  1. Encapsulated embedding and position encoding in a single module
  2. Support for nn.Linear patch embedding on pre-patchified (dictionary) inputs
  3. Support for NaFlex variable aspect, variable resolution (SigLip-2: https://arxiv.org/abs/2502.14786)
  4. Support for FlexiViT variable patch size (https://arxiv.org/abs/2212.08013)
  5. Support for NaViT fractional/factorized position embedding (https://arxiv.org/abs/2307.06304)
• Existing vit models in vision_transformer.py can be loaded into the NaFlexVit model by adding the use_naflex=True flag to create_model
  • Some native weights coming soon
• A full NaFlex data pipeline is available that allows training / fine-tuning / evaluating with variable aspect / size images
  • To enable in train.py and validate.py add the --naflex-loader arg, must be used with a NaFlexVit
• To evaluate an existing (classic) ViT loaded in NaFlexVit model w/ NaFlex data pipe:
  • python validate.py /imagenet --amp -j 8 --model vit_base_patch16_224 --model-kwargs use_naflex=True --naflex-loader --naflex-max-seq-len 256
• The training has some extra args features worth noting
  • The --naflex-train-seq-lens' argument specifies which sequence lengths to randomly pick from per batch during training
  • The --naflex-max-seq-len argument sets the target sequence length for validation
  • Adding --model-kwargs enable_patch_interpolator=True --naflex-patch-sizes 12 16 24 will enable random patch size selection per-batch w/ interpolation
  • The --naflex-loss-scale arg changes loss scaling mode per batch relative to the batch size, timm NaFlex loading changes the batch size for each seq len

May 28, 2025

• Add a number of small/fast models thanks to https://github.com/brianhou0208
  • SwiftFormer - (ICCV2023) SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications
  • FasterNet - (CVPR2023) Run, Don’t Walk: Chasing Higher FLOPS for Faster Neural Networks
  • SHViT - (CVPR2024) SHViT: Single-Head Vision Transformer with Memory Efficient
  • StarNet - (CVPR2024) Rewrite the Stars
  • GhostNet-V3 GhostNetV3: Exploring the Training Strategies for Compact Models
• Update EVA ViT (closest match) to support Perception Encoder models (https://arxiv.org/abs/2504.13181) from Meta, loading Hub weights but I still need to push dedicated timm weights
  • Add some flexibility to ROPE impl
• Big increase in number of models supporting forward_intermediates() and some additional fixes thanks to https://github.com/brianhou0208
  • DaViT, EdgeNeXt, EfficientFormerV2, EfficientViT(MIT), EfficientViT(MSRA), FocalNet, GCViT, HGNet /V2, InceptionNeXt, Inception-V4, MambaOut, MetaFormer, NesT, Next-ViT, PiT, PVT V2, RepGhostNet, RepViT, ResNetV2, ReXNet, TinyViT, TResNet, VoV

... (truncated)

Commits

• 7101adb Update README.md
• 85b65f0 Update version for 1.0.16 release
• 1f69a52 Merge pull request #2527 from huggingface/mobilenetv5
• 3828676 Make RmsNormAct sync with RmsNorm re default eps of 1e-6
• 136440d Switch to 'same' padding emulation for the enc model as it should be closer f...
• ddd3f99 Update test, encoder_only mode for backward test
• 4cc7fdb Cleanup imports, mark MSFA as notrace
• 857727d Simplify resolution check for improved script/trace compat
• e0cb669 Make features_only=True work with mnv5 & enc, uses forward_intermediates()
• 739b46c Fixed pool size (16,16) because of of MSFA.
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