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A big thank you to myGitHub Sponsors for their support!

In addition to the sponsors at the link above, I've received hardware and/or cloud resources from

• Nvidia (https://www.nvidia.com/en-us/)
• TFRC (https://www.tensorflow.org/tfrc)

I'm fortunate to be able to dedicate significant time and money of my own supporting this and other open source projects. However, as the projects increase in scope, outside support is needed to continue with the current trajectory of hardware, infrastructure, and electricty costs.

• Add LeViT, Visformer, ConViT (PR by Aman Arora), Twins (PR by paper authors) transformer models
• Add ResMLP and gMLP MLP vision models to the existing MLP Mixer impl
• Fix a number of torchscript issues with various vision transformer models
• Cleanup input_size/img_size override handling and improve testing / test coverage for all vision transformer and MLP models
• More flexible pos embedding resize (non-square) for ViT and TnT. ThanksAlexander Soare
• Addefficientnetv2_rw_m model and weights (started training before official code). 84.8 top-1, 53M params.

• Add EfficientNet-V2 official model defs w/ ported weights from officialTensorflow/Keras impl.
  • 1k trained variants:tf_efficientnetv2_s/m/l
  • 21k trained variants:tf_efficientnetv2_s/m/l_in21k
  • 21k pretrained -> 1k fine-tuned:tf_efficientnetv2_s/m/l_in21ft1k
  • v2 models w/ v1 scaling:tf_efficientnetv2_b0 throughb3
  • Rename my prev V2 guessefficientnet_v2s ->efficientnetv2_rw_s
  • Some blankefficientnetv2_* models in-place for future native PyTorch training

• Add MLP-Mixer models and port pretrained weights fromGoogle JAX impl
• Add CaiT models and pretrained weights fromFB
• Add ResNet-RS models and weights fromTF. ThanksAman Arora
• Add CoaT models and weights. ThanksMohammed Rizin
• Add new ImageNet-21k weights & finetuned weights for TResNet, MobileNet-V3, ViT models. ThanksmrT
• Add GhostNet models and weights. ThanksKai Han
• Update ByoaNet attention modules
  • Improve SA module inits
  • Hack together experimental stand-alone Swin based attn module andswinnet
  • Consistent '26t' model defs for experiments.
• Add improved Efficientnet-V2S (prelim model def) weights. 83.8 top-1.
• WandB logging support

• Add Swin Transformer models and weights fromhttps://github.com/microsoft/Swin-Transformer

• Add ECA-NFNet-L1 (slimmed down F1 w/ SiLU, 41M params) trained with this code. 84% top-1 @ 320x320. Trained at 256x256.
• Add EfficientNet-V2S model (unverified model definition) weights. 83.3 top-1 @ 288x288. Only trained single res 224. Working on progressive training.
• Add ByoaNet model definition (Bring-your-own-attention) w/ SelfAttention block and corresponding SA/SA-like modules and model defs
  • Lambda Networks -https://arxiv.org/abs/2102.08602
  • Bottleneck Transformers -https://arxiv.org/abs/2101.11605
  • Halo Nets -https://arxiv.org/abs/2103.12731
• Adabelief optimizer contributed by Juntang Zhuang

• Add snazzybenchmark.py script for bulktimm model benchmarking of train and/or inference
• Add Pooling-based Vision Transformer (PiT) models (fromhttps://github.com/naver-ai/pit)
  • Merged distilled variant into main for torchscript compatibility
  • Sometimm cleanup/style tweaks and weights have hub download support
• Cleanup Vision Transformer (ViT) models
  • Merge distilled (DeiT) model into main so that torchscript can work
  • Support updated weight init (defaults to old still) that closer matches original JAX impl (possibly better training from scratch)
  • Separate hybrid model defs into different file and add several new model defs to fiddle with, support patch_size != 1 for hybrids
  • Fix fine-tuning num_class changes (PiT and ViT) and pos_embed resizing (Vit) with distilled variants
  • nn.Sequential for block stack (does not break downstream compat)
• TnT (Transformer-in-Transformer) models contributed by author (fromhttps://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT)
• Add RegNetY-160 weights from DeiT teacher model
• Add new NFNet-L0 w/ SE attn (renamenfnet_l0b->nfnet_l0) weights 82.75 top-1 @ 288x288
• Some fixes/improvements for TFDS dataset wrapper

• Add new ECA-NFNet-L0 (renamenfnet_l0c->eca_nfnet_l0) weights trained by myself.
  • 82.6 top-1 @ 288x288, 82.8 @ 320x320, trained at 224x224
  • Uses SiLU activation, approx 2x faster thandm_nfnet_f0 and 50% faster thannfnet_f0s w/ 1/3 param count
• IntegrateHugging Face model hub into timm create_model and default_cfg handling for pretrained weight and config sharing (more on this soon!)
• Merge HardCoRe NAS models contributed byhttps://github.com/yoniaflalo
• Merge PyTorch trained EfficientNet-EL and pruned ES/EL variants contributed byDeGirum

• First 0.4.x PyPi release w/ NFNets (& related), ByoB (GPU-Efficient, RepVGG, etc).
• Change feature extraction for pre-activation nets (NFNets, ResNetV2) to return features before activation.
• Tested with PyTorch 1.8 release. Updated CI to use 1.8.
• Benchmarked several arch on RTX 3090, Titan RTX, and V100 across 1.7.1, 1.8, NGC 20.12, and 21.02. Some interesting performance variations to take note ofhttps://gist.github.com/rwightman/bb59f9e245162cee0e38bd66bd8cd77f

• Add pretrained weights and model variants for NFNet-F* models fromDeepMind Haiku impl.
  • Models are prefixed withdm_. They require SAME padding conv, skipinit enabled, and activation gains applied in act fn.
  • These models are big, expect to run out of GPU memory. With the GELU activiation + other options, they are roughly 1/2 the inference speed of my SiLU PyTorch optimizeds variants.
  • Original model results are based on pre-processing that is not the same as all other models so you'll see different results in the results csv (once updated).
  • Matching the original pre-processing as closely as possible I get these results:
    • dm_nfnet_f6 - 86.352
    • dm_nfnet_f5 - 86.100
    • dm_nfnet_f4 - 85.834
    • dm_nfnet_f3 - 85.676
    • dm_nfnet_f2 - 85.178
    • dm_nfnet_f1 - 84.696
    • dm_nfnet_f0 - 83.464

• Add Adaptive Gradient Clipping (AGC) as perhttps://arxiv.org/abs/2102.06171. Integrated w/ PyTorch gradient clipping via mode arg that defaults to prev 'norm' mode. For backward arg compat, clip-grad arg must be specified to enable when using train.py.
  • AGC w/ default clipping factor--clip-grad .01 --clip-mode agc
  • PyTorch global norm of 1.0 (old behaviour, always norm),--clip-grad 1.0
  • PyTorch value clipping of 10,--clip-grad 10. --clip-mode value
  • AGC performance is definitely sensitive to the clipping factor. More experimentation needed to determine good values for smaller batch sizes and optimizers besides those in paper. So far I've found .001-.005 is necessary for stable RMSProp training w/ NFNet/NF-ResNet.

• Update Normalization-Free nets to include new NFNet-F (https://arxiv.org/abs/2102.06171) model defs

• First Normalization-Free model training experiments done,
  • nf_resnet50 - 80.68 top-1 @ 288x288, 80.31 @ 256x256
  • nf_regnet_b1 - 79.30 @ 288x288, 78.75 @ 256x256
• More model archs, incl a flexible ByobNet backbone ('Bring-your-own-blocks')
  • GPU-Efficient-Networks (https://github.com/idstcv/GPU-Efficient-Networks), impl inbyobnet.py
  • RepVGG (https://github.com/DingXiaoH/RepVGG), impl inbyobnet.py
  • classic VGG (from torchvision, impl invgg.py)
• Refinements to normalizer layer arg handling and normalizer+act layer handling in some models
• Default AMP mode changed to native PyTorch AMP instead of APEX. Issues not being fixed with APEX. Native works with--channels-last and--torchscript model training, APEX does not.
• Fix a few bugs introduced since last pypi release

• Add several ResNet weights with ECA attention. 26t & 50t trained @ 256, test @ 320. 269d train @ 256, fine-tune @320, test @ 352.
  • ecaresnet26t - 79.88 top-1 @ 320x320, 79.08 @ 256x256
  • ecaresnet50t - 82.35 top-1 @ 320x320, 81.52 @ 256x256
  • ecaresnet269d - 84.93 top-1 @ 352x352, 84.87 @ 320x320
• Remove separate tiered (t) vs tiered_narrow (tn) ResNet model defs, alltn changed tot andt models removed (seresnext26t_32x4d only model w/ weights that was removed).
• Support model default_cfgs with separate train vs test resolutiontest_input_size and remove extra_320 suffix ResNet model defs that were just for test.

• Add initial "Normalization Free" NF-RegNet-B* and NF-ResNet model definitions based onpaper

• Add ResNetV2 Big Transfer (BiT) models w/ ImageNet-1k and 21k weights fromhttps://github.com/google-research/big_transfer
• Add official R50+ViT-B/16 hybrid models + weights fromhttps://github.com/google-research/vision_transformer
• ImageNet-21k ViT weights are added w/ model defs and representation layer (pre logits) support
  • NOTE: ImageNet-21k classifier heads were zero'd in original weights, they are only useful for transfer learning
• Add model defs and weights for DeiT Vision Transformer models fromhttps://github.com/facebookresearch/deit
• Refactor dataset classes into ImageDataset/IterableImageDataset + dataset specific parser classes
• Add Tensorflow-Datasets (TFDS) wrapper to allow use of TFDS image classification sets with train script
  • Ex:train.py /data/tfds --dataset tfds/oxford_iiit_pet --val-split test --model resnet50 -b 256 --amp --num-classes 37 --opt adamw --lr 3e-4 --weight-decay .001 --pretrained -j 2
• Add improved .tar dataset parser that reads images from .tar, folder of .tar files, or .tar within .tar
  • Run validation on full ImageNet-21k directly from tar w/ BiT model:validate.py /data/fall11_whole.tar --model resnetv2_50x1_bitm_in21k --amp
• Models in this update should be stable w/ possible exception of ViT/BiT, possibility of some regressions with train/val scripts and dataset handling

• Add SE-ResNet-152D weights
  • 256x256 val, 0.94 crop top-1 - 83.75
  • 320x320 val, 1.0 crop - 84.36
• Updateresults files

• Add ResNet-101D, ResNet-152D, and ResNet-200D weights trained @ 256x256
  • 256x256 val, 0.94 crop (top-1) - 101D (82.33), 152D (83.08), 200D (83.25)
  • 288x288 val, 1.0 crop - 101D (82.64), 152D (83.48), 200D (83.76)
  • 320x320 val, 1.0 crop - 101D (83.00), 152D (83.66), 200D (84.01)

• Simplify EMA module (ModelEmaV2), compatible with fully torchscripted models
• Misc fixes for SiLU ONNX export, default_cfg missing from Feature extraction models, Linear layer w/ AMP + torchscript
• PyPi release @ 0.3.2 (needed by EfficientDet)

PyTorchImageModels (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.

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. Here are some exampletraining hparams to get you started.

A full version of the list below with source links can be found in thedocumentation.

• 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
• 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 (Vision Transformer) -https://arxiv.org/abs/2012.12877
• DenseNet -https://arxiv.org/abs/1608.06993
• DLA -https://arxiv.org/abs/1707.06484
• DPN (Dual-Path Network) -https://arxiv.org/abs/1707.01629
• 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
• GhostNet -https://arxiv.org/abs/1911.11907
• gMLP -https://arxiv.org/abs/2105.08050
• GPU-Efficient Networks -https://arxiv.org/abs/2006.14090
• Halo Nets -https://arxiv.org/abs/2103.12731
• HardCoRe-NAS -https://arxiv.org/abs/2102.11646
• HRNet -https://arxiv.org/abs/1908.07919
• 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
• MLP-Mixer -https://arxiv.org/abs/2105.01601
• MobileNet-V3 (MBConvNet w/ Efficient Head) -https://arxiv.org/abs/1905.02244
• NASNet-A -https://arxiv.org/abs/1707.07012
• 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
• Pooling-based Vision Transformer (PiT) -https://arxiv.org/abs/2103.16302
• RegNet -https://arxiv.org/abs/2003.13678
• RepVGG -https://arxiv.org/abs/2101.03697
• 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
• Swin Transformer -https://arxiv.org/abs/2103.14030
• 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
• Vision Transformer -https://arxiv.org/abs/2010.11929
• 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

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 andreset_classifier
  • doing a forward pass on just the features -forward_features (seedocumentation)
  • 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 (seedocumentation)
  • 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 theC(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 performancereference 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 includestep,cosine w/ restarts,tanh w/ restarts,plateau
• Optimizers:
  • rmsprop_tf adapted from PyTorch RMSProp by myself. Reproduces much improved Tensorflow RMSProp behaviour.
  • radam byLiyuan Liu (https://arxiv.org/abs/1908.03265)
  • novograd byMasashi Kimura (https://arxiv.org/abs/1905.11286)
  • lookahead adapted from impl byLiam (https://arxiv.org/abs/1907.08610)
  • fused<name> optimizers by name withNVIDIA Apex installed
  • adamp andsgdp byNaver ClovAI (https://arxiv.org/abs/2006.08217)
  • adafactor adapted fromFAIRSeq impl (https://arxiv.org/abs/1804.04235)
  • adahessian byDavid Samuel (https://arxiv.org/abs/2006.00719)
• Random Erasing fromZhun 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 (https://arxiv.org/abs/1912.02781), JSD w/ clean + augmented mixing support works with AutoAugment and RandAugment as well
• SplitBachNorm - allows splitting batch norm layers between clean and augmented (auxiliary batch norm) data
• DropPath aka "Stochastic Depth" (https://arxiv.org/abs/1603.09382)
• DropBlock (https://arxiv.org/abs/1810.12890)
• Efficient Channel Attention - ECA (https://arxiv.org/abs/1910.03151)
• Blur Pooling (https://arxiv.org/abs/1904.11486)
• Space-to-Depth bymrT23 (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)

Model validation results can be found in thedocumentation and in theresults tables

My currentdocumentation fortimm covers the basics.

timmdocs is quickly becoming a much more comprehensive set of documentation fortimm. A big thanks toAman Arora for his efforts creating timmdocs.

paperswithcode is a good resource for browsing the models withintimm.

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. Seedocumentation for some basics andtraining hparams for some train examples that produce SOTA ImageNet results.

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.

• 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

• Albumentations -https://github.com/albumentations-team/albumentations
• Kornia -https://github.com/kornia/kornia

• RepDistiller -https://github.com/HobbitLong/RepDistiller
• torchdistill -https://github.com/yoshitomo-matsubara/torchdistill

• PyTorch Metric Learning -https://github.com/KevinMusgrave/pytorch-metric-learning

• fastai -https://github.com/fastai/fastai

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.

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 (http://www.image-net.org/download-faq). 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.

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.

@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}}}