PyTorch Image Models
What's New
August 27, 2026
- Add LowFormer model and weights
- Fix or merge a host of real but small or not commonly encountered bugs surfaced by agents
- Release 1.0.29
August 11, 2026
- Add model defs and pretrained weights for
- Add per-batch image and batch size scheduling for non-NaFlex training, including progressive small-to-large resolution schedules.
- Improve NaFlexViT with key-only attention masks, compile-friendly and memory-efficient position sampling, and cached variable-patch interpolation.
- Improve model factory path and checkpoint handling; fix equal-token positional embedding resampling.
July 10, 2026
- Improve optimizer
torch.compile and tensor learning-rate support.
- Extend NaFlexViT patch-layout (for NaFlex-CLAP), and
forward_intermediates (NaFlex dict input) support.
- Harden pickle loading and improve custom-label inference.
- Release 1.0.28
May 27, 2026
May 8, 2026
April 23, 2026
- Add Gemma4 ViT encoders w/ NaFlex pipeline support (variable aspect/size per image). Thanks Yonghye Kwon
- Support DINOv3 weights in NaFlexVit. Thanks Yonghye Kwon
- Some improvements to Muon fallback (AdamW/NadamW) lr behavior
March 23, 2026
- Improve pickle checkpoint handling security. Default all loading to
weights_only=True, add safe_global for ArgParse.
- Improve attention mask handling for core ViT/EVA models & layers. Resolve bool masks, pass
is_causal through for SSL tasks.
- Fix class & register token uses with ViT and no pos embed enabled.
- Add Patch Representation Refinement (PRR) as a pooling option in ViT. Thanks Sina (https://github.com/sinahmr).
- Improve consistency of output projection / MLP dimensions for attention pooling layers.
- Hiera model F.SDPA optimization to allow Flash Attention kernel use.
- Caution added to SGDP optimizer.
- Release 1.0.26. First maintenance release since my departure from Hugging Face.
Feb 23, 2026
- Add token distillation training support to distillation task wrappers
- Remove some torch.jit usage in prep for official deprecation
- Caution added to AdamP optimizer
- Call reset_parameters() even if meta-device init so that buffers get init w/ hacks like init_empty_weights
- Tweak Muon optimizer to work with DTensor/FSDP2 (clamp_ instead of clamp_min_, alternate NS branch for DTensor)
- Release 1.0.25
Jan 21, 2026
- Compat Break: Fix oversight w/ QKV vs MLP bias in
ParallelScalingBlock (& DiffParallelScalingBlock)
- Does not impact any trained
timm models but could impact downstream use.
Jan 5 & 6, 2026
- Release 1.0.24
- Add new benchmark result csv files for inference timing on all models w/ RTX Pro 6000, 5090, and 4090 cards w/ PyTorch 2.9.1
- Fix moved module error in deprecated timm.models.layers import path that impacts legacy imports
- Release 1.0.23
Dec 30, 2025
- Add better NAdaMuon trained
dpwee, dwee, dlittle (differential) ViTs with a small boost over previous runs
- Add a ~21M param
timm variant of the CSATv2 model at 512x512 & 640x640
- Factor non-persistent param init out of
__init__ into a common method that can be externally called via init_non_persistent_buffers() after meta-device init.
Dec 12, 2025
- Add CSATV2 model (thanks https://github.com/gusdlf93) -- a lightweight but high res model with DCT stem & spatial attention. https://huggingface.co/Hyunil/CSATv2
- Add AdaMuon and NAdaMuon optimizer support to existing
timm Muon impl. Appears more competitive vs AdamW with familiar hparams for image tasks.
- End of year PR cleanup, merge aspects of several long open PR
- Merge differential attention (
DiffAttention), add corresponding DiffParallelScalingBlock (for ViT), train some wee vits
- Add a few pooling modules,
LsePlus and SimPool
- Cleanup, optimize
DropBlock2d (also add support to ByobNet based models)
- Bump unit tests to PyTorch 2.9.1 + Python 3.13 on upper end, lower still PyTorch 1.13 + Python 3.10
Dec 1, 2025
- Add lightweight task abstraction, add logits and feature distillation support to train script via new tasks.
- Remove old APEX AMP support
Nov 4, 2025
Oct 31, 2025 🎃
- Update imagenet & OOD variant result csv files to include a few new models and verify correctness over several torch & timm versions
- EfficientNet-X and EfficientNet-H B5 model weights added as part of a hparam search for AdamW vs Muon (still iterating on Muon runs)
Oct 16-20, 2025
- Add an impl of the Muon optimizer (based on https://github.com/KellerJordan/Muon) with customizations
- extra flexibility and improved handling for conv weights and fallbacks for weight shapes not suited for orthogonalization
- small speedup for NS iterations by reducing allocs and using fused (b)add(b)mm ops
- by default uses AdamW (or NAdamW if
nesterov=True) updates if muon not suitable for parameter shape (or excluded via param group flag)
- like torch impl, select from several LR scale adjustment fns via
adjust_lr_fn
- select from several NS coefficient presets or specify your own via
ns_coefficients
- First 2 steps of 'meta' device model initialization supported
- Fix several ops that were breaking creation under 'meta' device context
- Add device & dtype factory kwarg support to all models and modules (anything inherting from nn.Module) in
timm
- License fields added to pretrained cfgs in code
- Release 1.0.21
Sept 21, 2025
- Remap DINOv3 ViT weight tags from
lvd_1689m -> lvd1689m to match (same for sat_493m -> sat493m)
- Release 1.0.20
Sept 17, 2025
July 23, 2025
- Add
set_input_size() method to EVA models, used by OpenCLIP 3.0.0 to allow resizing for timm based encoder models.
- Release 1.0.18, needed for PE-Core S & T models in OpenCLIP 3.0.0
- Fix small typing issue that broke Python 3.9 compat. 1.0.19 patch release.
July 21, 2025
- ROPE support added to NaFlexViT. All models covered by the EVA base (
eva.py) including EVA, EVA02, Meta PE ViT, timm SBB ViT w/ ROPE, and Naver ROPE-ViT can be now loaded in NaFlexViT when use_naflex=True passed at model creation time
- More Meta PE ViT encoders added, including small/tiny variants, lang variants w/ tiling, and more spatial variants.
- PatchDropout fixed with NaFlexViT and also w/ EVA models (regression after adding Naver ROPE-ViT)
- Fix XY order with grid_indexing='xy', impacted non-square image use in 'xy' mode (only ROPE-ViT and PE impacted).
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
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.
- 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:
- Encapsulated embedding and position encoding in a single module
- Support for nn.Linear patch embedding on pre-patchified (dictionary) inputs
- Support for NaFlex variable aspect, variable resolution (SigLip-2: https://arxiv.org/abs/2502.14786)
- Support for FlexiViT variable patch size (https://arxiv.org/abs/2212.08013)
- 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
- 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
Feb 21, 2025
- SigLIP 2 ViT image encoders added (https://huggingface.co/collections/timm/siglip-2-67b8e72ba08b09dd97aecaf9)
- Variable resolution / aspect NaFlex versions are a WIP
- Add 'SO150M2' ViT weights trained with SBB recipes, great results, better for ImageNet than previous attempt w/ less training.
vit_so150m2_patch16_reg1_gap_448.sbb_e200_in12k_ft_in1k - 88.1% top-1vit_so150m2_patch16_reg1_gap_384.sbb_e200_in12k_ft_in1k - 87.9% top-1vit_so150m2_patch16_reg1_gap_256.sbb_e200_in12k_ft_in1k - 87.3% top-1vit_so150m2_patch16_reg4_gap_256.sbb_e200_in12k
- Updated InternViT-300M '2.5' weights
- Release 1.0.15
Feb 1, 2025
- FYI PyTorch 2.6 & Python 3.13 are tested and working w/ current main and released version of
timm
Jan 27, 2025
- Add Kron Optimizer (PSGD w/ Kronecker-factored preconditioner)
Jan 19, 2025
- Fix loading of LeViT safetensor weights, remove conversion code which should have been deactivated
- Add 'SO150M' ViT weights trained with SBB recipes, decent results, but not optimal shape for ImageNet-12k/1k pretrain/ft
vit_so150m_patch16_reg4_gap_256.sbb_e250_in12k_ft_in1k - 86.7% top-1vit_so150m_patch16_reg4_gap_384.sbb_e250_in12k_ft_in1k - 87.4% top-1vit_so150m_patch16_reg4_gap_256.sbb_e250_in12k
- Misc typing, typo, etc. cleanup
- 1.0.14 release to get above LeViT fix out
Jan 9, 2025
- Add support to train and validate in pure
bfloat16 or float16
wandb project name arg added by https://github.com/caojiaolong, use arg.experiment for name- Fix old issue w/ checkpoint saving not working on filesystem w/o hard-link support (e.g. FUSE fs mounts)
- 1.0.13 release
Jan 6, 2025
- Add
torch.utils.checkpoint.checkpoint() wrapper in timm.models that defaults use_reentrant=False, unless TIMM_REENTRANT_CKPT=1 is set in env.
Dec 31, 2024
convnext_nano 384x384 ImageNet-12k pretrain & fine-tune. https://huggingface.co/models?search=convnext_nano%20r384- Add AIM-v2 encoders from https://github.com/apple/ml-aim, see on Hub: https://huggingface.co/models?search=timm%20aimv2
- Add PaliGemma2 encoders from https://github.com/google-research/big_vision to existing PaliGemma, see on Hub: https://huggingface.co/models?search=timm%20pali2
- Add missing L/14 DFN2B 39B CLIP ViT,
vit_large_patch14_clip_224.dfn2b_s39b
- Fix existing
RmsNorm layer & fn to match standard formulation, use PT 2.5 impl when possible. Move old impl to SimpleNorm layer, it's LN w/o centering or bias. There were only two timm models using it, and they have been updated.
- Allow override of
cache_dir arg for model creation
- Pass through
trust_remote_code for HF datasets wrapper
inception_next_atto model added by creator- Adan optimizer caution, and Lamb decoupled weight decay options
- Some feature_info metadata fixed by https://github.com/brianhou0208
- All OpenCLIP and JAX (CLIP, SigLIP, Pali, etc) model weights that used load time remapping were given their own HF Hub instances so that they work with
hf-hub: based loading, and thus will work with new Transformers TimmWrapperModel
Introduction
PyTorch Image Models (timm) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results.
The work of many others is present here. I've tried to make sure all source material is acknowledged via links to github, arxiv papers, etc in the README, documentation, and code docstrings. Please let me know if I missed anything.
Features
Models
PyTorch Image Models (timm) — the largest collection of PyTorch image encoders and backbones, shipped with pretrained weights plus train, eval, inference and export scripts.