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The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

huggingface.co/docs/timm

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PyTorch Image Models

What's New

âť—Updates after Oct 10, 2022 are available in version >= 0.9âť—

  • Many changes since the last 0.6.x stable releases. They were previewed in 0.8.x dev releases but not everyone transitioned.
  • timm.models.layers moved to timm.layers:
    • from timm.models.layers import name will still work via deprecation mapping (but please transition to timm.layers).
    • import timm.models.layers.module or from timm.models.layers.module import name needs to be changed now.
  • Builder, helper, non-model modules in timm.models have a _ prefix added, ie timm.models.helpers -> timm.models._helpers, there are temporary deprecation mapping files but those will be removed.
  • All models now support architecture.pretrained_tag naming (ex resnet50.rsb_a1).
    • The pretrained_tag is the specific weight variant (different head) for the architecture.
    • Using only architecture defaults to the first weights in the default_cfgs for that model architecture.
    • In adding pretrained tags, many model names that existed to differentiate were renamed to use the tag (ex: vit_base_patch16_224_in21k -> vit_base_patch16_224.augreg_in21k). There are deprecation mappings for these.
  • A number of models had their checkpoints remaped to match architecture changes needed to better support features_only=True, there are checkpoint_filter_fn methods in any model module that was remapped. These can be passed to timm.models.load_checkpoint(..., filter_fn=timm.models.swin_transformer_v2.checkpoint_filter_fn) to remap your existing checkpoint.
  • The Hugging Face Hub (https://huggingface.co/timm) is now the primary source for timm weights. Model cards include link to papers, original source, license.
  • Previous 0.6.x can be cloned from 0.6.x branch or installed via pip with version.

Aug 8, 2024

July 28, 2024

  • Add mobilenet_edgetpu_v2_m weights w/ ra4 mnv4-small based recipe. 80.1% top-1 @ 224 and 80.7 @ 256.
  • Release 1.0.8

July 26, 2024

  • More MobileNet-v4 weights, ImageNet-12k pretrain w/ fine-tunes, and anti-aliased ConvLarge models
model top1 top1_err top5 top5_err param_count img_size
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k 84.99 15.01 97.294 2.706 32.59 544
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k 84.772 15.228 97.344 2.656 32.59 480
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k 84.64 15.36 97.114 2.886 32.59 448
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k 84.314 15.686 97.102 2.898 32.59 384
mobilenetv4_conv_aa_large.e600_r384_in1k 83.824 16.176 96.734 3.266 32.59 480
mobilenetv4_conv_aa_large.e600_r384_in1k 83.244 16.756 96.392 3.608 32.59 384
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k 82.99 17.01 96.67 3.33 11.07 320
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k 82.364 17.636 96.256 3.744 11.07 256
model top1 top1_err top5 top5_err param_count img_size
efficientnet_b0.ra4_e3600_r224_in1k 79.364 20.636 94.754 5.246 5.29 256
efficientnet_b0.ra4_e3600_r224_in1k 78.584 21.416 94.338 5.662 5.29 224
mobilenetv1_100h.ra4_e3600_r224_in1k 76.596 23.404 93.272 6.728 5.28 256
mobilenetv1_100.ra4_e3600_r224_in1k 76.094 23.906 93.004 6.996 4.23 256
mobilenetv1_100h.ra4_e3600_r224_in1k 75.662 24.338 92.504 7.496 5.28 224
mobilenetv1_100.ra4_e3600_r224_in1k 75.382 24.618 92.312 7.688 4.23 224
  • Prototype of set_input_size() added to vit and swin v1/v2 models to allow changing image size, patch size, window size after model creation.
  • Improved support in swin for different size handling, in addition to set_input_size, always_partition and strict_img_size args have been added to __init__ to allow more flexible input size constraints
  • Fix out of order indices info for intermediate 'Getter' feature wrapper, check out or range indices for same.
  • Add several tiny < .5M param models for testing that are actually trained on ImageNet-1k
model top1 top1_err top5 top5_err param_count img_size crop_pct
test_efficientnet.r160_in1k 47.156 52.844 71.726 28.274 0.36 192 1.0
test_byobnet.r160_in1k 46.698 53.302 71.674 28.326 0.46 192 1.0
test_efficientnet.r160_in1k 46.426 53.574 70.928 29.072 0.36 160 0.875
test_byobnet.r160_in1k 45.378 54.622 70.572 29.428 0.46 160 0.875
test_vit.r160_in1k 42.0 58.0 68.664 31.336 0.37 192 1.0
test_vit.r160_in1k 40.822 59.178 67.212 32.788 0.37 160 0.875
  • Fix vit reg token init, thanks Promisery
  • Other misc fixes

June 24, 2024

  • 3 more MobileNetV4 hyrid weights with different MQA weight init scheme
model top1 top1_err top5 top5_err param_count img_size
mobilenetv4_hybrid_large.ix_e600_r384_in1k 84.356 15.644 96.892 3.108 37.76 448
mobilenetv4_hybrid_large.ix_e600_r384_in1k 83.990 16.010 96.702 3.298 37.76 384
mobilenetv4_hybrid_medium.ix_e550_r384_in1k 83.394 16.606 96.760 3.240 11.07 448
mobilenetv4_hybrid_medium.ix_e550_r384_in1k 82.968 17.032 96.474 3.526 11.07 384
mobilenetv4_hybrid_medium.ix_e550_r256_in1k 82.492 17.508 96.278 3.722 11.07 320
mobilenetv4_hybrid_medium.ix_e550_r256_in1k 81.446 18.554 95.704 4.296 11.07 256
  • florence2 weight loading in DaViT model

June 12, 2024

  • MobileNetV4 models and initial set of timm trained weights added:
model top1 top1_err top5 top5_err param_count img_size
mobilenetv4_hybrid_large.e600_r384_in1k 84.266 15.734 96.936 3.064 37.76 448
mobilenetv4_hybrid_large.e600_r384_in1k 83.800 16.200 96.770 3.230 37.76 384
mobilenetv4_conv_large.e600_r384_in1k 83.392 16.608 96.622 3.378 32.59 448
mobilenetv4_conv_large.e600_r384_in1k 82.952 17.048 96.266 3.734 32.59 384
mobilenetv4_conv_large.e500_r256_in1k 82.674 17.326 96.31 3.69 32.59 320
mobilenetv4_conv_large.e500_r256_in1k 81.862 18.138 95.69 4.31 32.59 256
mobilenetv4_hybrid_medium.e500_r224_in1k 81.276 18.724 95.742 4.258 11.07 256
mobilenetv4_conv_medium.e500_r256_in1k 80.858 19.142 95.768 4.232 9.72 320
mobilenetv4_hybrid_medium.e500_r224_in1k 80.442 19.558 95.38 4.62 11.07 224
mobilenetv4_conv_blur_medium.e500_r224_in1k 80.142 19.858 95.298 4.702 9.72 256
mobilenetv4_conv_medium.e500_r256_in1k 79.928 20.072 95.184 4.816 9.72 256
mobilenetv4_conv_medium.e500_r224_in1k 79.808 20.192 95.186 4.814 9.72 256
mobilenetv4_conv_blur_medium.e500_r224_in1k 79.438 20.562 94.932 5.068 9.72 224
mobilenetv4_conv_medium.e500_r224_in1k 79.094 20.906 94.77 5.23 9.72 224
mobilenetv4_conv_small.e2400_r224_in1k 74.616 25.384 92.072 7.928 3.77 256
mobilenetv4_conv_small.e1200_r224_in1k 74.292 25.708 92.116 7.884 3.77 256
mobilenetv4_conv_small.e2400_r224_in1k 73.756 26.244 91.422 8.578 3.77 224
mobilenetv4_conv_small.e1200_r224_in1k 73.454 26.546 91.34 8.66 3.77 224
  • Apple MobileCLIP (https://arxiv.org/pdf/2311.17049, FastViT and ViT-B) image tower model support & weights added (part of OpenCLIP support).
  • ViTamin (https://arxiv.org/abs/2404.02132) CLIP image tower model & weights added (part of OpenCLIP support).
  • OpenAI CLIP Modified ResNet image tower modelling & weight support (via ByobNet). Refactor AttentionPool2d.

May 14, 2024

  • Support loading PaliGemma jax weights into SigLIP ViT models with average pooling.
  • Add Hiera models from Meta (https://github.com/facebookresearch/hiera).
  • Add normalize= flag for transorms, return non-normalized torch.Tensor with original dytpe (for chug)
  • Version 1.0.3 release

May 11, 2024

  • Searching for Better ViT Baselines (For the GPU Poor) weights and vit variants released. Exploring model shapes between Tiny and Base.
model top1 top5 param_count img_size
vit_mediumd_patch16_reg4_gap_256.sbb_in12k_ft_in1k 86.202 97.874 64.11 256
vit_betwixt_patch16_reg4_gap_256.sbb_in12k_ft_in1k 85.418 97.48 60.4 256
vit_mediumd_patch16_rope_reg1_gap_256.sbb_in1k 84.322 96.812 63.95 256
vit_betwixt_patch16_rope_reg4_gap_256.sbb_in1k 83.906 96.684 60.23 256
vit_base_patch16_rope_reg1_gap_256.sbb_in1k 83.866 96.67 86.43 256
vit_medium_patch16_rope_reg1_gap_256.sbb_in1k 83.81 96.824 38.74 256
vit_betwixt_patch16_reg4_gap_256.sbb_in1k 83.706 96.616 60.4 256
vit_betwixt_patch16_reg1_gap_256.sbb_in1k 83.628 96.544 60.4 256
vit_medium_patch16_reg4_gap_256.sbb_in1k 83.47 96.622 38.88 256
vit_medium_patch16_reg1_gap_256.sbb_in1k 83.462 96.548 38.88 256
vit_little_patch16_reg4_gap_256.sbb_in1k 82.514 96.262 22.52 256
vit_wee_patch16_reg1_gap_256.sbb_in1k 80.256 95.360 13.42 256
vit_pwee_patch16_reg1_gap_256.sbb_in1k 80.072 95.136 15.25 256
vit_mediumd_patch16_reg4_gap_256.sbb_in12k N/A N/A 64.11 256
vit_betwixt_patch16_reg4_gap_256.sbb_in12k N/A N/A 60.4 256
  • AttentionExtract helper added to extract attention maps from timm models. See example in huggingface#1232 (comment)
  • forward_intermediates() API refined and added to more models including some ConvNets that have other extraction methods.
  • 1017 of 1047 model architectures support features_only=True feature extraction. Remaining 34 architectures can be supported but based on priority requests.
  • Remove torch.jit.script annotated functions including old JIT activations. Conflict with dynamo and dynamo does a much better job when used.

April 11, 2024

  • Prepping for a long overdue 1.0 release, things have been stable for a while now.
  • Significant feature that's been missing for a while, features_only=True support for ViT models with flat hidden states or non-std module layouts (so far covering 'vit_*', 'twins_*', 'deit*', 'beit*', 'mvitv2*', 'eva*', 'samvit_*', 'flexivit*')
  • Above feature support achieved through a new forward_intermediates() API that can be used with a feature wrapping module or direclty.
model = timm.create_model('vit_base_patch16_224')
final_feat, intermediates = model.forward_intermediates(input) 
output = model.forward_head(final_feat)  # pooling + classifier head

print(final_feat.shape)
torch.Size([2, 197, 768])

for f in intermediates:
    print(f.shape)
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])

print(output.shape)
torch.Size([2, 1000])
model = timm.create_model('eva02_base_patch16_clip_224', pretrained=True, img_size=512, features_only=True, out_indices=(-3, -2,))
output = model(torch.randn(2, 3, 512, 512))

for o in output:    
    print(o.shape)   
torch.Size([2, 768, 32, 32])
torch.Size([2, 768, 32, 32])
  • TinyCLIP vision tower weights added, thx Thien Tran

Feb 19, 2024

  • Next-ViT models added. Adapted from https://github.com/bytedance/Next-ViT
  • HGNet and PP-HGNetV2 models added. Adapted from https://github.com/PaddlePaddle/PaddleClas by SeeFun
  • Removed setup.py, moved to pyproject.toml based build supported by PDM
  • Add updated model EMA impl using _for_each for less overhead
  • Support device args in train script for non GPU devices
  • Other misc fixes and small additions
  • Min supported Python version increased to 3.8
  • Release 0.9.16

Jan 8, 2024

Datasets & transform refactoring

  • HuggingFace streaming (iterable) dataset support (--dataset hfids:org/dataset)
  • Webdataset wrapper tweaks for improved split info fetching, can auto fetch splits from supported HF hub webdataset
  • Tested HF datasets and webdataset wrapper streaming from HF hub with recent timm ImageNet uploads to https://huggingface.co/timm
  • Make input & target column/field keys consistent across datasets and pass via args
  • Full monochrome support when using e:g: --input-size 1 224 224 or --in-chans 1, sets PIL image conversion appropriately in dataset
  • Improved several alternate crop & resize transforms (ResizeKeepRatio, RandomCropOrPad, etc) for use in PixParse document AI project
  • Add SimCLR style color jitter prob along with grayscale and gaussian blur options to augmentations and args
  • Allow train without validation set (--val-split '') in train script
  • Add --bce-sum (sum over class dim) and --bce-pos-weight (positive weighting) args for training as they're common BCE loss tweaks I was often hard coding

Nov 23, 2023

  • Added EfficientViT-Large models, thanks SeeFun
  • Fix Python 3.7 compat, will be dropping support for it soon
  • Other misc fixes
  • Release 0.9.12

Nov 20, 2023

Nov 3, 2023

Oct 20, 2023

  • SigLIP image tower weights supported in vision_transformer.py.
    • Great potential for fine-tune and downstream feature use.
  • Experimental 'register' support in vit models as per Vision Transformers Need Registers
  • Updated RepViT with new weight release. Thanks wangao
  • Add patch resizing support (on pretrained weight load) to Swin models
  • 0.9.8 release pending

Sep 1, 2023

  • TinyViT added by SeeFun
  • Fix EfficientViT (MIT) to use torch.autocast so it works back to PT 1.10
  • 0.9.7 release

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

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.

Optimizers

Included optimizers available via create_optimizer / create_optimizer_v2 factory methods:

Augmentations

Regularization

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:

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

Computer Vision / Image Augmentation

Knowledge Distillation

Metric Learning

Training / Frameworks

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

DOI

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The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

huggingface.co/docs/timm

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