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

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

• 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.

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.

• 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

• Added significant flexibility for Hugging Face Hub based timm models via
  model_args
  config entry.
  model_args
  will be passed as kwargs through to models on creation.
  • See example at https://huggingface.co/gaunernst/vit_base_patch16_1024_128.audiomae_as2m_ft_as20k/blob/main/config.json
  • Usage: https://github.com/huggingface/pytorch-image-models/discussions/2035
• Updated imagenet eval and test set csv files with latest models
• vision_transformer.py
  typing and doc cleanup by Laureηt
• 0.9.11 release

Nov 3, 2023

• DFN (Data Filtering Networks) and MetaCLIP ViT weights added
• DINOv2 'register' ViT model weights added (https://huggingface.co/papers/2309.16588, https://huggingface.co/papers/2304.07193)
• Add
  quickgelu
  ViT variants for OpenAI, DFN, MetaCLIP weights that use it (less efficient)
• Improved typing added to ResNet, MobileNet-v3 thanks to Aryan
• ImageNet-12k fine-tuned (from LAION-2B CLIP)
  convnext_xxlarge
• 0.9.9 release

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

Aug 28, 2023

• Add dynamic img size support to models in
  vision_transformer.py
  ,
  vision_transformer_hybrid.py
  ,
  deit.py
  , and
  eva.py
  w/o breaking backward compat.
  • Add
    dynamic_img_size=True
    to args at model creation time to allow changing the grid size (interpolate abs and/or ROPE pos embed each forward pass).
  • Add
    dynamic_img_pad=True
    to allow image sizes that aren't divisible by patch size (pad bottom right to patch size each forward pass).
  • Enabling either dynamic mode will break FX tracing unless PatchEmbed module added as leaf.
  • Existing method of resizing position embedding by passing different
    img_size
    (interpolate pretrained embed weights once) on creation still works.
  • Existing method of changing
    patch_size
    (resize pretrained patch_embed weights once) on creation still works.
  • Example validation cmd
    python validate.py /imagenet --model vit_base_patch16_224 --amp --amp-dtype bfloat16 --img-size 255 --crop-pct 1.0 --model-kwargs dynamic_img_size=True dyamic_img_pad=True

Aug 25, 2023

• Many new models since last release
  • FastViT - https://arxiv.org/abs/2303.14189
  • MobileOne - https://arxiv.org/abs/2206.04040
  • InceptionNeXt - https://arxiv.org/abs/2303.16900
  • RepGhostNet - https://arxiv.org/abs/2211.06088 (thanks https://github.com/ChengpengChen)
  • GhostNetV2 - https://arxiv.org/abs/2211.12905 (thanks https://github.com/yehuitang)
  • EfficientViT (MSRA) - https://arxiv.org/abs/2305.07027 (thanks https://github.com/seefun)
  • EfficientViT (MIT) - https://arxiv.org/abs/2205.14756 (thanks https://github.com/seefun)
• Add
  --reparam
  arg to
  benchmark.py
  ,
  onnx_export.py
  , and
  validate.py
  to trigger layer reparameterization / fusion for models with any one of
  reparameterize()
  ,
  switch_to_deploy()
  or
  fuse()
  • Including FastViT, MobileOne, RepGhostNet, EfficientViT (MSRA), RepViT, RepVGG, and LeViT
• Preparing 0.9.6 'back to school' release

Aug 11, 2023

• Swin, MaxViT, CoAtNet, and BEiT models support resizing of image/window size on creation with adaptation of pretrained weights
• Example validation cmd to test w/ non-square resize
  python validate.py /imagenet --model swin_base_patch4_window7_224.ms_in22k_ft_in1k --amp --amp-dtype bfloat16 --input-size 3 256 320 --model-kwargs window_size=8,10 img_size=256,320

Aug 3, 2023

• Add GluonCV weights for HRNet w18_small and w18_small_v2. Converted by SeeFun
• Fix
  selecsls*
  model naming regression
• Patch and position embedding for ViT/EVA works for bfloat16/float16 weights on load (or activations for on-the-fly resize)
• v0.9.5 release prep

July 27, 2023

• Added timm trained
  seresnextaa201d_32x8d.sw_in12k_ft_in1k_384
  weights (and
  .sw_in12k
  pretrain) with 87.3% top-1 on ImageNet-1k, best ImageNet ResNet family model I'm aware of.
• RepViT model and weights (https://arxiv.org/abs/2307.09283) added by wangao
• I-JEPA ViT feature weights (no classifier) added by SeeFun
• SAM-ViT (segment anything) feature weights (no classifier) added by SeeFun
• Add support for alternative feat extraction methods and -ve indices to EfficientNet
• Add NAdamW optimizer
• Misc fixes

May 11, 2023

• timm
  0.9 released, transition from 0.8.xdev releases

May 10, 2023

• Hugging Face Hub downloading is now default, 1132 models on https://huggingface.co/timm, 1163 weights in
  timm
• DINOv2 vit feature backbone weights added thanks to Leng Yue
• FB MAE vit feature backbone weights added
• OpenCLIP DataComp-XL L/14 feat backbone weights added
• MetaFormer (poolformer-v2, caformer, convformer, updated poolformer (v1)) w/ weights added by Fredo Guan
• Experimental
  get_intermediate_layers
  function on vit/deit models for grabbing hidden states (inspired by DINO impl). This is WIP and may change significantly... feedback welcome.
• Model creation throws error if
  pretrained=True
  and no weights exist (instead of continuing with random initialization)
• Fix regression with inception / nasnet TF sourced weights with 1001 classes in original classifiers
• bitsandbytes (https://github.com/TimDettmers/bitsandbytes) optimizers added to factory, use
  bnb
  prefix, ie
  bnbadam8bit
• Misc cleanup and fixes
• Final testing before switching to a 0.9 and bringing
  timm
  out of pre-release state

April 27, 2023

• 97% of
  timm
  models uploaded to HF Hub and almost all updated to support multi-weight pretrained configs
• Minor cleanup and refactoring of another batch of models as multi-weight added. More fused_attn (F.sdpa) and features_only support, and torchscript fixes.

April 21, 2023

• Gradient accumulation support added to train script and tested (
  --grad-accum-steps
  ), thanks Taeksang Kim
• More weights on HF Hub (cspnet, cait, volo, xcit, tresnet, hardcorenas, densenet, dpn, vovnet, xception_aligned)
• Added
  --head-init-scale
  and
  --head-init-bias
  to train.py to scale classiifer head and set fixed bias for fine-tune
• Remove all InplaceABN (
  inplace_abn
  ) use, replaced use in tresnet with standard BatchNorm (modified weights accordingly).

April 12, 2023

• Add ONNX export script, validate script, helpers that I've had kicking around for along time. Tweak 'same' padding for better export w/ recent ONNX + pytorch.
• Refactor dropout args for vit and vit-like models, separate drop_rate into
  drop_rate
  (classifier dropout),
  proj_drop_rate
  (block mlp / out projections),
  pos_drop_rate
  (position embedding drop),
  attn_drop_rate
  (attention dropout). Also add patch dropout (FLIP) to vit and eva models.
• fused F.scaled_dot_product_attention support to more vit models, add env var (TIMM_FUSED_ATTN) to control, and config interface to enable/disable
• Add EVA-CLIP backbones w/ image tower weights, all the way up to 4B param 'enormous' model, and 336x336 OpenAI ViT mode that was missed.

April 5, 2023

• ALL ResNet models pushed to Hugging Face Hub with multi-weight support
  • All past
    timm
    trained weights added with recipe based tags to differentiate
  • All ResNet strikes back A1/A2/A3 (seed 0) and R50 example B/C1/C2/D weights available
  • Add torchvision v2 recipe weights to existing torchvision originals
  • See comparison table in https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288#model-comparison
• New ImageNet-12k + ImageNet-1k fine-tunes available for a few anti-aliased ResNet models
  • resnetaa50d.sw_in12k_ft_in1k
    - 81.7 @ 224, 82.6 @ 288
  • resnetaa101d.sw_in12k_ft_in1k
    - 83.5 @ 224, 84.1 @ 288
  • seresnextaa101d_32x8d.sw_in12k_ft_in1k
    - 86.0 @ 224, 86.5 @ 288
  • seresnextaa101d_32x8d.sw_in12k_ft_in1k_288
    - 86.5 @ 288, 86.7 @ 320

March 31, 2023

• Add first ConvNext-XXLarge CLIP -> IN-1k fine-tune and IN-12k intermediate fine-tunes for convnext-base/large CLIP models.

• Add EVA-02 MIM pretrained and fine-tuned weights, push to HF hub and update model cards for all EVA models. First model over 90% top-1 (99% top-5)! Check out the original code & weights at https://github.com/baaivision/EVA for more details on their work blending MIM, CLIP w/ many model, dataset, and train recipe tweaks.

• Multi-weight and HF hub for DeiT and MLP-Mixer based models

March 22, 2023

• More weights pushed to HF hub along with multi-weight support, including:
  regnet.py
  ,
  rexnet.py
  ,
  byobnet.py
  ,
  resnetv2.py
  ,
  swin_transformer.py
  ,
  swin_transformer_v2.py
  ,
  swin_transformer_v2_cr.py
• Swin Transformer models support feature extraction (NCHW feat maps for
  swinv2_cr_*
  , and NHWC for all others) and spatial embedding outputs.
• FocalNet (from https://github.com/microsoft/FocalNet) models and weights added with significant refactoring, feature extraction, no fixed resolution / sizing constraint
• RegNet weights increased with HF hub push, SWAG, SEER, and torchvision v2 weights. SEER is pretty poor wrt to performance for model size, but possibly useful.
• More ImageNet-12k pretrained and 1k fine-tuned
  timm
  weights:
  • rexnetr_200.sw_in12k_ft_in1k
    - 82.6 @ 224, 83.2 @ 288
  • rexnetr_300.sw_in12k_ft_in1k
    - 84.0 @ 224, 84.5 @ 288
  • regnety_120.sw_in12k_ft_in1k
    - 85.0 @ 224, 85.4 @ 288
  • regnety_160.lion_in12k_ft_in1k
    - 85.6 @ 224, 86.0 @ 288
  • regnety_160.sw_in12k_ft_in1k
    - 85.6 @ 224, 86.0 @ 288 (compare to SWAG PT + 1k FT this is same BUT much lower res, blows SEER FT away)
• Model name deprecation + remapping functionality added (a milestone for bringing 0.8.x out of pre-release). Mappings being added...
• Minor bug fixes and improvements.

Feb 26, 2023

• Add ConvNeXt-XXLarge CLIP pretrained image tower weights for fine-tune & features (fine-tuning TBD) -- see model card
• Update
  convnext_xxlarge
  default LayerNorm eps to 1e-5 (for CLIP weights, improved stability)
• 0.8.15dev0

Feb 20, 2023

• Add 320x320
  convnext_large_mlp.clip_laion2b_ft_320
  and
  convnext_lage_mlp.clip_laion2b_ft_soup_320
  CLIP image tower weights for features & fine-tune
• 0.8.13dev0 pypi release for latest changes w/ move to huggingface org

Feb 16, 2023

• safetensor
  checkpoint support added
• Add ideas from 'Scaling Vision Transformers to 22 B. Params' (https://arxiv.org/abs/2302.05442) -- qk norm, RmsNorm, parallel block
• Add F.scaled_dot_product_attention support (PyTorch 2.0 only) to
  vit_*
  ,
  vit_relpos*
  ,
  coatnet
  /
  maxxvit
  (to start)
• Lion optimizer (w/ multi-tensor option) added (https://arxiv.org/abs/2302.06675)
• gradient checkpointing works with
  features_only=True

Introduction

PyTorch Image Models (

) 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
• 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
• CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929
• 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
• 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 - https://arxiv.org/abs/2211.07636
• EVA-02 - https://arxiv.org/abs/2303.11331
• FastViT - https://arxiv.org/abs/2303.14189
• FlexiViT - https://arxiv.org/abs/2212.08013
• FocalNet (Focal Modulation Networks) - https://arxiv.org/abs/2203.11926
• GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959
• GhostNet - https://arxiv.org/abs/1911.11907
• GhostNet-V2 - https://arxiv.org/abs/2211.12905
• 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
• 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
• LeViT (Vision Transformer in ConvNet's Clothing) - https://arxiv.org/abs/2104.01136
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Visformer - https://arxiv.org/abs/2104.12533
• Vision Transformer - https://arxiv.org/abs/2010.11929
• 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

Included optimizers available via

/

create_optimizer_v2

factory methods:

• adabelief
  an implementation of AdaBelief adapted from https://github.com/juntang-zhuang/Adabelief-Optimizer - https://arxiv.org/abs/2010.07468
• adafactor
  adapted from FAIRSeq impl - https://arxiv.org/abs/1804.04235
• adahessian
  by David Samuel - https://arxiv.org/abs/2006.00719
• adamp
  and
  sgdp
  by Naver ClovAI - https://arxiv.org/abs/2006.08217
• adan
  an implementation of Adan adapted from https://github.com/sail-sg/Adan - https://arxiv.org/abs/2208.06677
• lamb
  an implementation of Lamb and LambC (w/ trust-clipping) cleaned up and modified to support use with XLA - https://arxiv.org/abs/1904.00962
• lars
  an implementation of LARS and LARC (w/ trust-clipping) - https://arxiv.org/abs/1708.03888
• lion
  and implementation of Lion adapted from https://github.com/google/automl/tree/master/lion - https://arxiv.org/abs/2302.06675
• lookahead
  adapted from impl by Liam - https://arxiv.org/abs/1907.08610
• madgrad
  - and implementation of MADGRAD adapted from https://github.com/facebookresearch/madgrad - https://arxiv.org/abs/2101.11075
• nadam
  an implementation of Adam w/ Nesterov momentum
• nadamw
  an impementation of AdamW (Adam w/ decoupled weight-decay) w/ Nesterov momentum. A simplified impl based on https://github.com/mlcommons/algorithmic-efficiency
• novograd
  by Masashi Kimura - https://arxiv.org/abs/1905.11286
• radam
  by Liyuan Liu - https://arxiv.org/abs/1908.03265
• rmsprop_tf
  adapted from PyTorch RMSProp by myself. Reproduces much improved Tensorflow RMSProp behaviour
• sgdw
  and implementation of SGD w/ decoupled weight-decay
• fused<name>
  optimizers by name with NVIDIA Apex installed
• bits<name>
  optimizers by name with BitsAndBytes installed

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_classifier
    and
    reset_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_indices
    creation arg specifies which feature maps to return, these indices are 0 based and generally correspond to the
    C(i + 1)
    feature level.
  • output_stride
    creation 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_info
    member
• 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
    ,
    cosine
    w/ restarts,
    tanh
    w/ restarts,
    plateau
• Space-to-Depth by mrT23 (https://arxiv.org/abs/1801.04590) -- original paper?
• 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

in detail.

timmdocs is an alternate set of documentation for

. A big thanks to Aman Arora for his efforts creating timmdocs.

paperswithcode is a good resource for browsing the models within

.

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

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

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