• 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
• TNT model updated w/ new weights forward_intermediates() thanks to https://github.com/brianhou0208
• Add local-dir: pretrained schema, can use local-dir:/path/to/model/folder for model name to source model / pretrained cfg & weights Hugging Face Hub models (config.json + weights file) from a local folder.
• Fixes, improvements for onnx export

What's Changed

• Fix arg merging of sknet, old seresnet. Fix #2470 by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2471
• Fix onnx export by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2475
• Add local-dir: schema support for model loading (config + weights) from folder by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2476
• Fix: Allow img_size to be int or tuple in PatchEmbed by @sddongxh in https://github.com/huggingface/pytorch-image-models/pull/2477
• Add LightlyTrain Integration for Pretraining Support by @yutong-xiang-97 in https://github.com/huggingface/pytorch-image-models/pull/2474
• Check forward_intermediates features against forward_features output by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2483
• More models support forward_intermediates by @brianhou0208 in https://github.com/huggingface/pytorch-image-models/pull/2482
• Update README.md by @atharva-pathak in https://github.com/huggingface/pytorch-image-models/pull/2484
• remove download argument from torch_kwargs for torchvision ImageNet class by @ryan-caesar-ramos in https://github.com/huggingface/pytorch-image-models/pull/2486
• Update TNT-(S/B) model weights and add feature extraction support by @brianhou0208 in https://github.com/huggingface/pytorch-image-models/pull/2480
• Add EVA ViT based PE (Perceptual Encoder) impl by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2487
• Add SwiftFormer, SHViT, StarNet, FasterNet and GhostNetV3 by @brianhou0208 in https://github.com/huggingface/pytorch-image-models/pull/2499
• A cleaned up beit3 remap onto vision_transformer.py vit by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2503
• Initial NaFlex ViT model and training support by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2466
• Forgot to compact attention pool branches after verifying by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2507
• Throw exception on non-directory path for pretrained weights by @emmanuel-ferdman in https://github.com/huggingface/pytorch-image-models/pull/2510
• Add corrected_weight decay to several optimizers by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2511
• Doing some Claude enabled docstring, type annotation and other cleanup by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2504
• Fix #2513, be explicit about stream devices by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2515
• Update legacy AdamW impl so it has a multi-tensor impl like NAdamW (n… by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2517
• Fix head_dim reference in AttentionRope class of attention.py by @amorehead in https://github.com/huggingface/pytorch-image-models/pull/2519
• Refactor patch and pos embed resampling based on feedback from https://github.com/stas-sl by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2518
• Add initial weights for my first 3 naflexvit_base models by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2523
• Support gradient checkpointing in forward_intermediates() by @brianhou0208 in https://github.com/huggingface/pytorch-image-models/pull/2501
• Update README: add references for additional supported models by @brianhou0208 in https://github.com/huggingface/pytorch-image-models/pull/2526
• MobileNetV5 by @rwightman in https://github.com/huggingface/pytorch-image-models/pull/2527

New Contributors

• @sddongxh made their first contribution in https://github.com/huggingface/pytorch-image-models/pull/2477
• @yutong-xiang-97 made their first contribution in https://github.com/huggingface/pytorch-image-models/pull/2474
• @atharva-pathak made their first contribution in https://github.com/huggingface/pytorch-image-models/pull/2484
• @ryan-caesar-ramos made their first contribution in https://github.com/huggingface/pytorch-image-models/pull/2486
• @emmanuel-ferdman made their first contribution in https://github.com/huggingface/pytorch-image-models/pull/2510
• @amorehead made their first contribution in https://github.com/huggingface/pytorch-image-models/pull/2519

Full Changelog: https://github.com/huggingface/pytorch-image-models/compare/v1.0.15...v1.0.16