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pytorch-image-models – huggingface
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项目简介
最大的 PyTorch 图像编码器/主干网络集合。包含训练、评估、推理、导出脚本及预训练权重——ResNet、ResNeXT、EfficientNet、NFNet、Vision Transformer (ViT)、MobileNetV4、MobileNet-V3 & V2、RegNet、DPN、CSPNet、Swin Transformer、MaxViT、CoAtNet、ConvNeXt 等。
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Stars 数
37,059
Forks 数
5,189
Watchers 数
37,059
Issue 数
60
主要语言
Python
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Apache-2.0
仓库所有者
huggingface
创建时间
2019.02.02
更新时间
2026.08.11
最近推送
2026.08.11
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README 详细介绍
README 原文为英文,如需阅读完整中文内容,建议使用 浏览器翻译插件(如 Edge 浏览器自带翻译、Google 翻译扩展)进行整页翻译。
PyTorch Image Models
- What's New
- Introduction
- Models
- Features
- Results
- Getting Started (Documentation)
- Train, Validation, Inference Scripts
- Awesome PyTorch Resources
- Licenses
- Citing
What's New
August 11, 2026
- Add model defs and pretrained weights for
- CPUBone, an efficient vision backbone for devices with low parallelization capabilities.
- PP-LCNetV2.
- LingBot-Vision.
- 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.compileand 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
- Add model defs and pretrained weights for EUPE ViT (DINOv3-style) and ConvNeXt models. See the Efficient Universal Perception Encoder paper.
- Add TIPSv2 model defs and pretrained weights for (DINOv2-style) ViTs. See the TIPSv2 paper.
May 8, 2026
- Release 1.0.27
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_causalthrough 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
timmmodels 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 - https://huggingface.co/timm/vit_dlittle_patch16_reg1_gap_256.sbb_nadamuon_in1k (83.24% top-1)
- https://huggingface.co/timm/vit_dwee_patch16_reg1_gap_256.sbb_nadamuon_in1k (81.80% top-1)
- https://huggingface.co/timm/vit_dpwee_patch16_reg1_gap_256.sbb_nadamuon_in1k (81.67% top-1)
- Add a ~21M param
timmvariant of the CSATv2 model at 512x512 & 640x640 - https://huggingface.co/timm/csatv2_21m.sw_r640_in1k (83.13% top-1)
- https://huggingface.co/timm/csatv2_21m.sw_r512_in1k (82.58% top-1)
- Factor non-persistent param init out of
__init__into a common method that can be externally called viainit_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
timmMuon 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 correspondingDiffParallelScalingBlock(for ViT), train some wee vits - https://huggingface.co/timm/vit_dwee_patch16_reg1_gap_256.sbb_in1k
- https://huggingface.co/timm/vit_dpwee_patch16_reg1_gap_256.sbb_in1k
- Add a few pooling modules,
LsePlusandSimPool - 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
- Fix LayerScale / LayerScale2d init bug (init values ignored), introduced in 1.0.21. Thanks https://github.com/Ilya-Fradlin
- Release 1.0.22
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->lvd1689mto match (same forsat_493m->sat493m) - Release 1.0.20
Sept 17, 2025
- DINOv3 (https://arxiv.org/abs/2508.10104) ConvNeXt and ViT models added. ConvNeXt models were mapped to existing
timmmodel. ViT support done via the EVA base model w/ a newRotaryEmbeddingDinoV3to match the DINOv3 specific RoPE impl - HuggingFace Hub: https://huggingface.co/collections/timm/timm-dinov3-68cb08bb0bee365973d52a4d
- MobileCLIP-2 (https://arxiv.org/abs/2508.20691) vision encoders. New MCI3/MCI4 FastViT variants added and weights mapped to existing FastViT and B, L/14 ViTs.
- MetaCLIP-2 Worldwide (https://arxiv.org/abs/2507.22062) ViT encoder weights added.
- SigLIP-2 (https://arxiv.org/abs/2502.14786) NaFlex ViT encoder weights added via timm NaFlexViT model.
- Misc fixes and contributions
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,timmSBB ViT w/ ROPE, and Naver ROPE-ViT can be now loaded in NaFlexViT whenuse_naflex=Truepassed 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 |
| 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 |
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
All model architecture families include variants with pretrained weights. There are specific model variants without any weights, it is NOT a bug. Help training new or better weights is always appreciated.
- Aggregating Nested Transformers - https://arxiv.org/abs/2105.12723
- BEiT - https://arxiv.org/abs/2106.08254
- BEiT-V2 - https://arxiv.org/abs/2208.06366
- Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370
- Bottleneck Transformers - https://arxiv.org/abs/2101.11605
- CaiT (Class-Attention in Image Transformers) - https://arxiv.org/abs/2103.17239
- CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399
- CoAtNet (Convolution and Attention) - https://arxiv.org/abs/2106.04803
- ConvNeXt - https://arxiv.org/abs/2201.03545
- ConvNeXt-V2 - http://arxiv.org/abs/2301.00808
- ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697
- ConvMixer - https://arxiv.org/abs/2201.09792
- CPUBone - https://arxiv.org/abs/2603.26425
- CrossViT - https://arxiv.org/abs/2103.14899
- CSATv2 - https://huggingface.co/Hyunil/CSATv2
- CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929
- DaViT (Dual Attention Vision Transformer) - https://arxiv.org/abs/2204.03645
- DeiT - https://arxiv.org/abs/2012.12877
- DeiT-III - https://arxiv.org/pdf/2204.07118.pdf
- DenseNet - https://arxiv.org/abs/1608.06993
- DLA - https://arxiv.org/abs/1707.06484
- DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629
- EdgeNeXt - https://arxiv.org/abs/2206.10589
- EfficientFormer - https://arxiv.org/abs/2206.01191
- EfficientFormer-V2 - https://arxiv.org/abs/2212.08059
- EfficientNet (MBConvNet Family)
- EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252
- EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665
- EfficientNet (B0-B7) - https://arxiv.org/abs/1905.11946
- EfficientNet-EdgeTPU (S, M, L) - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html
- EfficientNet V2 - https://arxiv.org/abs/2104.00298
- FBNet-C - https://arxiv.org/abs/1812.03443
- MixNet - https://arxiv.org/abs/1907.09595
- MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626
- MobileNet-V2 - https://arxiv.org/abs/1801.04381
- Single-Path NAS - https://arxiv.org/abs/1904.02877
- TinyNet - https://arxiv.org/abs/2010.14819
- EfficientViT (MIT) - https://arxiv.org/abs/2205.14756
- EfficientViT (MSRA) - https://arxiv.org/abs/2305.07027
- EVA (RoPE ViT family)
- EVA - https://arxiv.org/abs/2211.07636
- EVA-02 - https://arxiv.org/abs/2303.11331
- EVA-CLIP - https://arxiv.org/abs/2303.15389
- DINOv3 - https://arxiv.org/abs/2508.10104
- Efficient Universal Perception Encoder (EUPE) - https://arxiv.org/abs/2603.22387
- LingBot-Vision - https://arxiv.org/abs/2607.05247
- Perception Encoder (PE) - https://arxiv.org/abs/2504.13181
- RoPE-ViT - https://arxiv.org/abs/2403.13298
- FasterNet - https://arxiv.org/abs/2303.03667
- FastViT - https://arxiv.org/abs/2303.14189
- FocalNet (Focal Modulation Networks) - https://arxiv.org/abs/2203.11926
- GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959
- Gemma4 ViT encoder - https://arxiv.org/abs/2607.02770
- GhostNet - https://arxiv.org/abs/1911.11907
- GhostNet-V2 - https://arxiv.org/abs/2211.12905
- GhostNet-V3 - https://arxiv.org/abs/2404.11202
- gMLP - https://arxiv.org/abs/2105.08050
- GPU-Efficient Networks - https://arxiv.org/abs/2006.14090
- Halo Nets - https://arxiv.org/abs/2103.12731
- HGNet / HGNet-V2 - TBD
- Hiera (family)
- Hiera - https://arxiv.org/abs/2306.00989
- SAM 2 / 2.1 Hiera image encoders - https://arxiv.org/abs/2408.00714
- HRNet - https://arxiv.org/abs/1908.07919
- InceptionNeXt - https://arxiv.org/abs/2303.16900
- Inception-V3 - https://arxiv.org/abs/1512.00567
- Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
- Lambda Networks - https://arxiv.org/abs/2102.08602
- LCNet-V2 - TBD
- LeViT (Vision Transformer in ConvNet's Clothing) - https://arxiv.org/abs/2104.01136
- MambaOut - https://arxiv.org/abs/2405.07992
- MaxViT (Multi-Axis Vision Transformer) - https://arxiv.org/abs/2204.01697
- MetaFormer (PoolFormer-v2, ConvFormer, CAFormer) - https://arxiv.org/abs/2210.13452
- MLP-Mixer - https://arxiv.org/abs/2105.01601
- MobileCLIP - https://arxiv.org/abs/2311.17049
- MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244
- FBNet-V3 - https://arxiv.org/abs/2006.02049
- HardCoRe-NAS - https://arxiv.org/abs/2102.11646
- LCNet - https://arxiv.org/abs/2109.15099
- MobileNetV4 - https://arxiv.org/abs/2404.10518
- MobileNetV5 (Gemma 3n vision encoder) - https://ai.google.dev/gemma/docs/gemma-3n
- MobileOne - https://arxiv.org/abs/2206.04040
- MobileViT - https://arxiv.org/abs/2110.02178
- MobileViT-V2 - https://arxiv.org/abs/2206.02680
- MViT-V2 (Improved Multiscale Vision Transformer) - https://arxiv.org/abs/2112.01526
- NaFlexViT (NaViT / FlexiViT family)
- NaViT - https://arxiv.org/abs/2307.06304
- FlexiViT - https://arxiv.org/abs/2212.08013
- SigLIP 2 NaFlex image encoders - https://arxiv.org/abs/2502.14786
- NASNet-A - https://arxiv.org/abs/1707.07012
- NesT - https://arxiv.org/abs/2105.12723
- Next-ViT - https://arxiv.org/abs/2207.05501
- NFNet-F - https://arxiv.org/abs/2102.06171
- NF-RegNet / NF-ResNet - https://arxiv.org/abs/2101.08692
- PNasNet - https://arxiv.org/abs/1712.00559
- PoolFormer (MetaFormer) - https://arxiv.org/abs/2111.11418
- Pooling-based Vision Transformer (PiT) - https://arxiv.org/abs/2103.16302
- PVT-V2 (Improved Pyramid Vision Transformer) - https://arxiv.org/abs/2106.13797
- RDNet (DenseNets Reloaded) - https://arxiv.org/abs/2403.19588
- RegNet - https://arxiv.org/abs/2003.13678
- RegNetZ - https://arxiv.org/abs/2103.06877
- RepVGG - https://arxiv.org/abs/2101.03697
- RepGhostNet - https://arxiv.org/abs/2211.06088
- RepViT - https://arxiv.org/abs/2307.09283
- ResMLP - https://arxiv.org/abs/2105.03404
- ResNet/ResNeXt
- ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385
- ResNeXt - https://arxiv.org/abs/1611.05431
- 'Bag of Tricks' / Gluon C, D, E, S variations - https://arxiv.org/abs/1812.01187
- Weakly-supervised (WSL) Instagram pretrained / ImageNet tuned ResNeXt101 - https://arxiv.org/abs/1805.00932
- Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet/ResNeXts - https://arxiv.org/abs/1905.00546
- ECA-Net (ECAResNet) - https://arxiv.org/abs/1910.03151v4
- Squeeze-and-Excitation Networks (SEResNet) - https://arxiv.org/abs/1709.01507
- ResNet-RS - https://arxiv.org/abs/2103.07579
- Res2Net - https://arxiv.org/abs/1904.01169
- ResNeSt - https://arxiv.org/abs/2004.08955
- ReXNet - https://arxiv.org/abs/2007.00992
- SelecSLS - https://arxiv.org/abs/1907.00837
- Selective Kernel Networks - https://arxiv.org/abs/1903.06586
- Sequencer2D - https://arxiv.org/abs/2205.01972
- SHViT - https://arxiv.org/abs/2401.16456
- StarNet - https://arxiv.org/abs/2403.19967
- SwiftFormer - https://arxiv.org/pdf/2303.15446
- Swin S3 (AutoFormerV2) - https://arxiv.org/abs/2111.14725
- Swin Transformer - https://arxiv.org/abs/2103.14030
- Swin Transformer V2 - https://arxiv.org/abs/2111.09883
- TinyViT - https://arxiv.org/abs/2207.10666
- Transformer-iN-Transformer (TNT) - https://arxiv.org/abs/2103.00112
- TResNet - https://arxiv.org/abs/2003.13630
- Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/pdf/2104.13840.pdf
- VGG - https://arxiv.org/abs/1409.1556
- Visformer - https://arxiv.org/abs/2104.12533
- Vision Transformer (family)
- Vision Transformer (ViT) - https://arxiv.org/abs/2010.11929
- AIMv2 - https://arxiv.org/abs/2411.14402
- AugReg (How to train your ViT?) - https://arxiv.org/abs/2106.10270
- BEiT3 - https://arxiv.org/abs/2208.10442
- CLIP image encoders - https://arxiv.org/abs/2103.00020
- DINO - https://arxiv.org/abs/2104.14294
- DINOv2 / Vision Transformers Need Registers - https://arxiv.org/abs/2304.07193, https://arxiv.org/abs/2309.16588
- FlexiViT - https://arxiv.org/abs/2212.08013
- I-JEPA - https://arxiv.org/abs/2301.08243
- InternViT (InternVL vision encoder) - https://arxiv.org/abs/2312.14238
- MAE - https://arxiv.org/abs/2111.06377
- Sharpness-Aware Minimization (SAM) ViT weights - https://arxiv.org/abs/2106.01548
- Scaling Vision Transformers - https://arxiv.org/abs/2106.04560
- SigLIP image encoders - https://arxiv.org/abs/2303.15343
- SigLIP 2 image encoders - https://arxiv.org/abs/2502.14786
- TIPSv2 - https://arxiv.org/abs/2604.12012
- ViTamin - https://arxiv.org/abs/2404.02132
- Segment Anything (SAM) ViT image encoders - https://arxiv.org/abs/2304.02643
- VOLO (Vision Outlooker) - https://arxiv.org/abs/2106.13112
- VovNet V2 and V1 - https://arxiv.org/abs/1911.06667
- Xception - https://arxiv.org/abs/1610.02357
- Xception (Modified Aligned, Gluon) - https://arxiv.org/abs/1802.02611
- Xception (Modified Aligned, TF) - https://arxiv.org/abs/1802.02611
- XCiT (Cross-Covariance Image Transformers) - https://arxiv.org/abs/2106.09681
Optimizers
To see full list of optimizers w/ descriptions: timm.optim.list_optimizers(with_description=True)
Included optimizers available via timm.optim.create_optimizer_v2 factory method:
adabeliefan implementation of AdaBelief adapted from https://github.com/juntang-zhuang/Adabelief-Optimizer - https://arxiv.org/abs/2010.07468adafactoradapted from FAIRSeq impl - https://arxiv.org/abs/1804.04235adafactorbvadapted from Big Vision - https://arxiv.org/abs/2106.04560adahessianby David Samuel - https://arxiv.org/abs/2006.00719adampandsgdpby Naver ClovAI - https://arxiv.org/abs/2006.08217adamuonandnadamuonas per https://github.com/Chongjie-Si/AdaMuon - https://arxiv.org/abs/2507.11005adanan implementation of Adan adapted from https://github.com/sail-sg/Adan - https://arxiv.org/abs/2208.06677adoptADOPT adapted from https://github.com/iShohei220/adopt - https://arxiv.org/abs/2411.02853kronPSGD w/ Kronecker-factored preconditioner from https://github.com/evanatyourservice/kron_torch - https://sites.google.com/site/lixilinx/home/psgdlamban implementation of Lamb and LambC (w/ trust-clipping) cleaned up and modified to support use with XLA - https://arxiv.org/abs/1904.00962lapropoptimizer from https://github.com/Z-T-WANG/LaProp-Optimizer - https://arxiv.org/abs/2002.04839larsan implementation of LARS and LARC (w/ trust-clipping) - https://arxiv.org/abs/1708.03888lionand implementation of Lion adapted from https://github.com/google/automl/tree/master/lion - https://arxiv.org/abs/2302.06675lookaheadadapted from impl by Liam - https://arxiv.org/abs/1907.08610madgradan implementation of MADGRAD adapted from https://github.com/facebookresearch/madgrad - https://arxiv.org/abs/2101.11075marsMARS optimizer from https://github.com/AGI-Arena/MARS - https://arxiv.org/abs/2411.10438muonMUON optimizer from https://github.com/KellerJordan/Muon with numerous additions and improved non-transformer behaviournadaman implementation of Adam w/ Nesterov momentumnadamwan implementation of AdamW (Adam w/ decoupled weight-decay) w/ Nesterov momentum. A simplified impl based on https://github.com/mlcommons/algorithmic-efficiencynovogradby Masashi Kimura - https://arxiv.org/abs/1905.11286radamby Liyuan Liu - https://arxiv.org/abs/1908.03265rmsprop_tfadapted from PyTorch RMSProp by myself. Reproduces much improved Tensorflow RMSProp behavioursgdwand implementation of SGD w/ decoupled weight-decayfusedoptimizers by name with NVIDIA Apex installedbnboptimizers by name with BitsAndBytes installedcadamw,clion, and more 'Cautious' optimizers from https://github.com/kyleliang919/C-Optim - https://arxiv.org/abs/2411.16085adam,adamw,rmsprop,adadelta,adagrad, andsgdpass through totorch.optimimplementationscsuffix (egadamc,nadamcto implement 'corrected weight decay' in https://arxiv.org/abs/2506.02285)
Augmentations
- Random Erasing from Zhun Zhong - https://arxiv.org/abs/1708.04896)
- Mixup - https://arxiv.org/abs/1710.09412
- CutMix - https://arxiv.org/abs/1905.04899
- AutoAugment (https://arxiv.org/abs/1805.09501) and RandAugment (https://arxiv.org/abs/1909.13719) ImageNet configurations modeled after impl for EfficientNet training (https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py)
- AugMix w/ JSD loss, JSD w/ clean + augmented mixing support works with AutoAugment and RandAugment as well - https://arxiv.org/abs/1912.02781
- SplitBachNorm - allows splitting batch norm layers between clean and augmented (auxiliary batch norm) data
Regularization
- DropPath aka "Stochastic Depth" - https://arxiv.org/abs/1603.09382
- DropBlock - https://arxiv.org/abs/1810.12890
- Blur Pooling - https://arxiv.org/abs/1904.11486
Other
Several (less common) features that I often utilize in my projects are included. Many of their additions are the reason why I maintain my own set of models, instead of using others' via PIP:
- All models have a common default configuration interface and API for
- accessing/changing the classifier -
get_classifierandreset_classifier - doing a forward pass on just the features -
forward_features(see documentation) - these makes it easy to write consistent network wrappers that work with any of the models
- All models support multi-scale feature map extraction (feature pyramids) via create_model (see documentation)
create_model(name, features_only=True, out_indices=..., output_stride=...)out_indicescreation arg specifies which feature maps to return, these indices are 0 based and generally correspond to theC(i + 1)feature level.output_stridecreation arg controls output stride of the network by using dilated convolutions. Most networks are stride 32 by default. Not all networks support this.- feature map channel counts, reduction level (stride) can be queried AFTER model creation via the
.feature_infomember - All models have a consistent pretrained weight loader that adapts last linear if necessary, and from 3 to 1 channel input if desired
- High performance reference training, validation, and inference scripts that work in several process/GPU modes:
- NVIDIA DDP w/ a single GPU per process, multiple processes with APEX present (AMP mixed-precision optional)
- PyTorch DistributedDataParallel w/ multi-gpu, single process (AMP disabled as it crashes when enabled)
- PyTorch w/ single GPU single process (AMP optional)
- A dynamic global pool implementation that allows selecting from average pooling, max pooling, average + max, or concat([average, max]) at model creation. All global pooling is adaptive average by default and compatible with pretrained weights.
- A 'Test Time Pool' wrapper that can wrap any of the included models and usually provides improved performance doing inference with input images larger than the training size. Idea adapted from original DPN implementation when I ported (https://github.com/cypw/DPNs)
- Learning rate schedulers
- Ideas adopted from
- AllenNLP schedulers
- FAIRseq lr_scheduler
- SGDR: Stochastic Gradient Descent with Warm Restarts (https://arxiv.org/abs/1608.03983)
- Schedulers include
step,cosinew/ restarts,tanhw/ restarts,plateau - Space-to-Depth by mrT23 (https://arxiv.org/abs/1801.04590)
- Adaptive Gradient Clipping (https://arxiv.org/abs/2102.06171, https://github.com/deepmind/deepmind-research/tree/master/nfnets)
- An extensive selection of channel and/or spatial attention modules:
- Bottleneck Transformer - https://arxiv.org/abs/2101.11605
- CBAM - https://arxiv.org/abs/1807.06521
- Effective Squeeze-Excitation (ESE) - https://arxiv.org/abs/1911.06667
- Efficient Channel Attention (ECA) - https://arxiv.org/abs/1910.03151
- Gather-Excite (GE) - https://arxiv.org/abs/1810.12348
- Global Context (GC) - https://arxiv.org/abs/1904.11492
- Halo - https://arxiv.org/abs/2103.12731
- Involution - https://arxiv.org/abs/2103.06255
- Lambda Layer - https://arxiv.org/abs/2102.08602
- Non-Local (NL) - https://arxiv.org/abs/1711.07971
- Squeeze-and-Excitation (SE) - https://arxiv.org/abs/1709.01507
- Selective Kernel (SK) - (https://arxiv.org/abs/1903.06586
- Split (SPLAT) - https://arxiv.org/abs/2004.08955
- Shifted Window (SWIN) - https://arxiv.org/abs/2103.14030
Results
Model validation results can be found in the results tables
Getting Started (Documentation)
The official documentation can be found at https://huggingface.co/docs/hub/timm. Documentation contributions are welcome.
Getting Started with PyTorch Image Models (timm): A Practitioner’s Guide by Chris Hughes is an extensive blog post covering many aspects of timm in detail.
timmdocs is an alternate set of documentation for timm. A big thanks to Aman Arora for his efforts creating timmdocs.
paperswithcode is a good resource for browsing the models within timm.
Train, Validation, Inference Scripts
The root folder of the repository contains reference train, validation, and inference scripts that work with the included models and other features of this repository. They are adaptable for other datasets and use cases with a little hacking. See documentation.
Awesome PyTorch Resources
One of the greatest assets of PyTorch is the community and their contributions. A few of my favourite resources that pair well with the models and components here are listed below.
Object Detection, Instance and Semantic Segmentation
- Detectron2 - https://github.com/facebookresearch/detectron2
- Segmentation Models (Semantic) - https://github.com/qubvel/segmentation_models.pytorch
- EfficientDet (Obj Det, Semantic soon) - https://github.com/rwightman/efficientdet-pytorch
Computer Vision / Image Augmentation
- Albumentations - https://github.com/albumentations-team/albumentations
- Kornia - https://github.com/kornia/kornia
Knowledge Distillation
- RepDistiller - https://github.com/HobbitLong/RepDistiller
- torchdistill - https://github.com/yoshitomo-matsubara/torchdistill
Metric Learning
- PyTorch Metric Learning - https://github.com/KevinMusgrave/pytorch-metric-learning
Training / Frameworks
- fastai - https://github.com/fastai/fastai
- lightly_train - https://github.com/lightly-ai/lightly-train
Deployment
- timmx (Export timm models to ONNX, CoreML, LiteRT, TensorRT, and more) - https://github.com/Boulaouaney/timmx
Licenses
Code
The code here is licensed Apache 2.0. I've taken care to make sure any third party code included or adapted has compatible (permissive) licenses such as MIT, BSD, etc. I've made an effort to avoid any GPL / LGPL conflicts. That said, it is your responsibility to ensure you comply with licenses here and conditions of any dependent licenses. Where applicable, I've linked the sources/references for various components in docstrings. If you think I've missed anything please create an issue.
Pretrained Weights
So far all of the pretrained weights available here are pretrained on ImageNet with a select few that have some additional pretraining (see extra note below). ImageNet was released for non-commercial research purposes only (https://image-net.org/download). It's not clear what the implications of that are for the use of pretrained weights from that dataset. Any models I have trained with ImageNet are done for research purposes and one should assume that the original dataset license applies to the weights. It's best to seek legal advice if you intend to use the pretrained weights in a commercial product.
Pretrained on more than ImageNet
Several weights included or references here were pretrained with proprietary datasets that I do not have access to. These include the Facebook WSL, SSL, SWSL ResNe(Xt) and the Google Noisy Student EfficientNet models. The Facebook models have an explicit non-commercial license (CC-BY-NC 4.0, https://github.com/facebookresearch/semi-supervised-ImageNet1K-models, https://github.com/facebookresearch/WSL-Images). The Google models do not appear to have any restriction beyond the Apache 2.0 license (and ImageNet concerns). In either case, you should contact Facebook or Google with any questions.
Citing
BibTeX
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {url{https://github.com/rwightman/pytorch-image-models}}
}Latest DOI
<img src="https://zenodo.org/badge/168799526.svg" alt="DOI" class="ra0-md-img" loading="lazy" />
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