Package: timm-clean
PyTorch Image ModelsSponsorsWhat's NewIntroductionModelsFeaturesResultsGetting Started (Documentation)Train, Validation, Inference ScriptsAwesome PyTorch ResourcesLicensesCitingSponsorsA big thank you to myGitHub Sponsorsfor their support!In addition to the sponsors at the link above, I've received hardware and/or cloud resources fromNvidia (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.What's NewJune 23, 2021Reproduce gMLP model training,gmlp_s16_224trained to 79.6 top-1, matchingpaper. Hparams for this and other recent MLP traininghereJune 20, 2021Release Vision Transformer 'AugReg' weights fromHow to train your ViT? Data, Augmentation, and Regularization in Vision Transformers.npz weight loading support added, can load any of the 50K+ weights from theAugReg seriesSeeexample notebookfromofficial implfor navigating the augreg weightsReplaced all default weights w/ best AugReg variant (if possible). All AugReg 21k classifiers work.Highlights:vit_large_patch16_384(87.1 top-1),vit_large_r50_s32_384(86.2 top-1),vit_base_patch16_384(86.0 top-1)vit_deit_*renamed to justdeit_*Remove my old small model, replace with DeiT compatible small w/ AugReg weightsAdd 1st training of mygmixer_24_224MLP /w GLU, 78.1 top-1 w/ 25M params.Add weights from official ResMLP release (https://github.com/facebookresearch/deit)Addeca_nfnet_l2weights from my 'lightweight' series. 84.7 top-1 at 384x384.Add distilled BiT 50x1 student and 152x2 Teacher weights fromKnowledge distillation: A good teacher is patient and consistentNFNets and ResNetV2-BiT models work w/ Pytorch XLA nowweight standardization uses F.batch_norm instead of std_mean (std_mean wasn't lowered)eps values adjusted, will be slight differences but should be quite closeImprove test coverage and classifier interface of non-conv (vision transformer and mlp) modelsCleanup a few classifier / flatten details for models w/ conv classifiers or early global poolPlease report any regressions, this PR touched quite a few models.June 8, 2021Add first ResMLP weights, trained in PyTorch XLA on TPU-VM w/ my XLA branch. 24 block variant, 79.2 top-1.Add ResNet51-Q model w/ pretrained weights at 82.36 top-1.NFNet inspired block layout with quad layer stem and no maxpoolSame param count (35.7M) and throughput as ResNetRS-50 but +1.5 top-1 @ 224x224 and +2.5 top-1 at 288x288May 25, 2021Add LeViT, Visformer, ConViT (PR by Aman Arora), Twins (PR by paper authors) transformer modelsAdd ResMLP and gMLP MLP vision models to the existing MLP Mixer implFix a number of torchscript issues with various vision transformer modelsCleanup input_size/img_size override handling and improve testing / test coverage for all vision transformer and MLP modelsMore flexible pos embedding resize (non-square) for ViT and TnT. ThanksAlexander SoareAddefficientnetv2_rw_mmodel and weights (started training before official code). 84.8 top-1, 53M params.May 14, 2021Add EfficientNet-V2 official model defs w/ ported weights from officialTensorflow/Kerasimpl.1k trained variants:tf_efficientnetv2_s/m/l21k trained variants:tf_efficientnetv2_s/m/l_in21k21k pretrained -> 1k fine-tuned:tf_efficientnetv2_s/m/l_in21ft1kv2 models w/ v1 scaling:tf_efficientnetv2_b0throughb3Rename my prev V2 guessefficientnet_v2s->efficientnetv2_rw_sSome blankefficientnetv2_*models in-place for future native PyTorch trainingMay 5, 2021Add MLP-Mixer models and port pretrained weights fromGoogle JAX implAdd CaiT models and pretrained weights fromFBAdd ResNet-RS models and weights fromTF. ThanksAman AroraAdd CoaT models and weights. ThanksMohammed RizinAdd new ImageNet-21k weights & finetuned weights for TResNet, MobileNet-V3, ViT models. ThanksmrTAdd GhostNet models and weights. ThanksKai HanUpdate ByoaNet attention modulesImprove SA module initsHack together experimental stand-alone Swin based attn module andswinnetConsistent '26t' model defs for experiments.Add improved Efficientnet-V2S (prelim model def) weights. 83.8 top-1.WandB logging supportApril 13, 2021Add Swin Transformer models and weights fromhttps://github.com/microsoft/Swin-TransformerApril 12, 2021Add 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 defsLambda Networks -https://arxiv.org/abs/2102.08602Bottleneck Transformers -https://arxiv.org/abs/2101.11605Halo Nets -https://arxiv.org/abs/2103.12731Adabelief optimizer contributed by Juntang ZhuangApril 1, 2021Add snazzybenchmark.pyscript for bulktimmmodel benchmarking of train and/or inferenceAdd Pooling-based Vision Transformer (PiT) models (fromhttps://github.com/naver-ai/pit)Merged distilled variant into main for torchscript compatibilitySometimmcleanup/style tweaks and weights have hub download supportCleanup Vision Transformer (ViT) modelsMerge distilled (DeiT) model into main so that torchscript can workSupport 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 hybridsFix fine-tuning num_class changes (PiT and ViT) and pos_embed resizing (Vit) with distilled variantsnn.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 modelAdd new NFNet-L0 w/ SE attn (renamenfnet_l0b->nfnet_l0) weights 82.75 top-1 @ 288x288Some fixes/improvements for TFDS dataset wrapperMarch 17, 2021Add new ECA-NFNet-L0 (renamenfnet_l0c->eca_nfnet_l0) weights trained by myself.82.6 top-1 @ 288x288, 82.8 @ 320x320, trained at 224x224Uses SiLU activation, approx 2x faster thandm_nfnet_f0and 50% faster thannfnet_f0sw/ 1/3 param countIntegrateHugging Face model hubinto 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/yoniaflaloMerge PyTorch trained EfficientNet-EL and pruned ES/EL variants contributed byDeGirumMarch 7, 2021First 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/bb59f9e245162cee0e38bd66bd8cd77fFeb 18, 2021Add 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 optimizedsvariants.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- 86.352dm_nfnet_f5- 86.100dm_nfnet_f4- 85.834dm_nfnet_f3- 85.676dm_nfnet_f2- 85.178dm_nfnet_f1- 84.696dm_nfnet_f0- 83.464Feb 16, 2021Add 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 agcPyTorch global norm of 1.0 (old behaviour, always norm),--clip-grad 1.0PyTorch value clipping of 10,--clip-grad 10. --clip-mode valueAGC 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.Feb 12, 2021Update Normalization-Free nets to include new NFNet-F (https://arxiv.org/abs/2102.06171) model defsFeb 10, 2021First Normalization-Free model training experiments done,nf_resnet50 - 80.68 top-1 @ 288x288, 80.31 @ 256x256nf_regnet_b1 - 79.30 @ 288x288, 78.75 @ 256x256More model archs, incl a flexible ByobNet backbone ('Bring-your-own-blocks')GPU-Efficient-Networks (https://github.com/idstcv/GPU-Efficient-Networks), impl inbyobnet.pyRepVGG (https://github.com/DingXiaoH/RepVGG), impl inbyobnet.pyclassic VGG (from torchvision, impl invgg.py)Refinements to normalizer layer arg handling and normalizer+act layer handling in some modelsDefault AMP mode changed to native PyTorch AMP instead of APEX. Issues not being fixed with APEX. Native works with--channels-lastand--torchscriptmodel training, APEX does not.Fix a few bugs introduced since last pypi releaseFeb 8, 2021Add 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 @ 256x256ecaresnet50t- 82.35 top-1 @ 320x320, 81.52 @ 256x256ecaresnet269d- 84.93 top-1 @ 352x352, 84.87 @ 320x320Remove separate tiered (t) vs tiered_narrow (tn) ResNet model defs, alltnchanged totandtmodels removed (seresnext26t_32x4donly model w/ weights that was removed).Support model default_cfgs with separate train vs test resolutiontest_input_sizeand remove extra_320suffix ResNet model defs that were just for test.Jan 30, 2021Add initial "Normalization Free" NF-RegNet-B* and NF-ResNet model definitions based onpaperJan 25, 2021Add ResNetV2 Big Transfer (BiT) models w/ ImageNet-1k and 21k weights fromhttps://github.com/google-research/big_transferAdd official R50+ViT-B/16 hybrid models + weights fromhttps://github.com/google-research/vision_transformerImageNet-21k ViT weights are added w/ model defs and representation layer (pre logits) supportNOTE: ImageNet-21k classifier heads were zero'd in original weights, they are only useful for transfer learningAdd model defs and weights for DeiT Vision Transformer models fromhttps://github.com/facebookresearch/deitRefactor dataset classes into ImageDataset/IterableImageDataset + dataset specific parser classesAdd Tensorflow-Datasets (TFDS) wrapper to allow use of TFDS image classification sets with train scriptEx: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 2Add improved .tar dataset parser that reads images from .tar, folder of .tar files, or .tar within .tarRun validation on full ImageNet-21k directly from tar w/ BiT model:validate.py /data/fall11_whole.tar --model resnetv2_50x1_bitm_in21k --ampModels in this update should be stable w/ possible exception of ViT/BiT, possibility of some regressions with train/val scripts and dataset handlingJan 3, 2021Add SE-ResNet-152D weights256x256 val, 0.94 crop top-1 - 83.75320x320 val, 1.0 crop - 84.36Updateresults filesIntroductionPyTorchImageModels (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.ModelsAll 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 hparamsto 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/1912.11370Bottleneck Transformers -https://arxiv.org/abs/2101.11605CaiT (Class-Attention in Image Transformers) -https://arxiv.org/abs/2103.17239CoaT (Co-Scale Conv-Attentional Image Transformers) -https://arxiv.org/abs/2104.06399ConViT (Soft Convolutional Inductive Biases Vision Transformers)-https://arxiv.org/abs/2103.10697CspNet (Cross-Stage Partial Networks) -https://arxiv.org/abs/1911.11929DeiT (Vision Transformer) -https://arxiv.org/abs/2012.12877DenseNet -https://arxiv.org/abs/1608.06993DLA -https://arxiv.org/abs/1707.06484DPN (Dual-Path Network) -https://arxiv.org/abs/1707.01629EfficientNet (MBConvNet Family)EfficientNet NoisyStudent (B0-B7, L2) -https://arxiv.org/abs/1911.04252EfficientNet AdvProp (B0-B8) -https://arxiv.org/abs/1911.09665EfficientNet (B0-B7) -https://arxiv.org/abs/1905.11946EfficientNet-EdgeTPU (S, M, L) -https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.htmlEfficientNet V2 -https://arxiv.org/abs/2104.00298FBNet-C -https://arxiv.org/abs/1812.03443MixNet -https://arxiv.org/abs/1907.09595MNASNet B1, A1 (Squeeze-Excite), and Small -https://arxiv.org/abs/1807.11626MobileNet-V2 -https://arxiv.org/abs/1801.04381Single-Path NAS -https://arxiv.org/abs/1904.02877GhostNet -https://arxiv.org/abs/1911.11907gMLP -https://arxiv.org/abs/2105.08050GPU-Efficient Networks -https://arxiv.org/abs/2006.14090Halo Nets -https://arxiv.org/abs/2103.12731HardCoRe-NAS -https://arxiv.org/abs/2102.11646HRNet -https://arxiv.org/abs/1908.07919Inception-V3 -https://arxiv.org/abs/1512.00567Inception-ResNet-V2 and Inception-V4 -https://arxiv.org/abs/1602.07261Lambda Networks -https://arxiv.org/abs/2102.08602LeViT (Vision Transformer in ConvNet's Clothing) -https://arxiv.org/abs/2104.01136MLP-Mixer -https://arxiv.org/abs/2105.01601MobileNet-V3 (MBConvNet w/ Efficient Head) -https://arxiv.org/abs/1905.02244NASNet-A -https://arxiv.org/abs/1707.07012NFNet-F -https://arxiv.org/abs/2102.06171NF-RegNet / NF-ResNet -https://arxiv.org/abs/2101.08692PNasNet -https://arxiv.org/abs/1712.00559Pooling-based Vision Transformer (PiT) -https://arxiv.org/abs/2103.16302RegNet -https://arxiv.org/abs/2003.13678RepVGG -https://arxiv.org/abs/2101.03697ResMLP -https://arxiv.org/abs/2105.03404ResNet/ResNeXtResNet (v1b/v1.5) -https://arxiv.org/abs/1512.03385ResNeXt -https://arxiv.org/abs/1611.05431'Bag of Tricks' / Gluon C, D, E, S variations -https://arxiv.org/abs/1812.01187Weakly-supervised (WSL) Instagram pretrained / ImageNet tuned ResNeXt101 -https://arxiv.org/abs/1805.00932Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet/ResNeXts -https://arxiv.org/abs/1905.00546ECA-Net (ECAResNet) -https://arxiv.org/abs/1910.03151v4Squeeze-and-Excitation Networks (SEResNet) -https://arxiv.org/abs/1709.01507ResNet-RS -https://arxiv.org/abs/2103.07579Res2Net -https://arxiv.org/abs/1904.01169ResNeSt -https://arxiv.org/abs/2004.08955ReXNet -https://arxiv.org/abs/2007.00992SelecSLS -https://arxiv.org/abs/1907.00837Selective Kernel Networks -https://arxiv.org/abs/1903.06586Swin Transformer -https://arxiv.org/abs/2103.14030Transformer-iN-Transformer (TNT) -https://arxiv.org/abs/2103.00112TResNet -https://arxiv.org/abs/2003.13630Twins (Spatial Attention in Vision Transformers) -https://arxiv.org/pdf/2104.13840.pdfVision Transformer -https://arxiv.org/abs/2010.11929VovNet V2 and V1 -https://arxiv.org/abs/1911.06667Xception -https://arxiv.org/abs/1610.02357Xception (Modified Aligned, Gluon) -https://arxiv.org/abs/1802.02611Xception (Modified Aligned, TF) -https://arxiv.org/abs/1802.02611FeaturesSeveral (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 foraccessing/changing the classifier -get_classifierandreset_classifierdoing 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 modelsAll models support multi-scale feature map extraction (feature pyramids) via create_model (seedocumentation)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_infomemberAll models have a consistent pretrained weight loader that adapts last linear if necessary, and from 3 to 1 channel input if desiredHigh performancereference training, validation, and inference scriptsthat 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 schedulersIdeas adopted fromAllenNLP schedulersFAIRseq lr_schedulerSGDR: Stochastic Gradient Descent with Warm Restarts (https://arxiv.org/abs/1608.03983)Schedulers includestep,cosinew/ restarts,tanhw/ restarts,plateauOptimizers:rmsprop_tfadapted from PyTorch RMSProp by myself. Reproduces much improved Tensorflow RMSProp behaviour.radambyLiyuan Liu(https://arxiv.org/abs/1908.03265)novogradbyMasashi Kimura(https://arxiv.org/abs/1905.11286)lookaheadadapted from impl byLiam(https://arxiv.org/abs/1907.08610)fused<name>optimizers by name withNVIDIA ApexinstalledadampandsgdpbyNaver ClovAI(https://arxiv.org/abs/2006.08217)adafactoradapted fromFAIRSeq impl(https://arxiv.org/abs/1804.04235)adahessianbyDavid 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 wellSplitBachNorm - allows splitting batch norm layers between clean and augmented (auxiliary batch norm) dataDropPath aka "Stochastic Depth" (https://arxiv.org/abs/1603.09382)DropBlock (https://arxiv.org/abs/1810.12890)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)An extensive selection of channel and/or spatial attention modules:Bottleneck Transformer -https://arxiv.org/abs/2101.11605CBAM -https://arxiv.org/abs/1807.06521Effective Squeeze-Excitation (ESE) -https://arxiv.org/abs/1911.06667Efficient Channel Attention (ECA) -https://arxiv.org/abs/1910.03151Gather-Excite (GE) -https://arxiv.org/abs/1810.12348Global Context (GC) -https://arxiv.org/abs/1904.11492Halo -https://arxiv.org/abs/2103.12731Involution -https://arxiv.org/abs/2103.06255Lambda Layer -https://arxiv.org/abs/2102.08602Non-Local (NL) -https://arxiv.org/abs/1711.07971Squeeze-and-Excitation (SE) -https://arxiv.org/abs/1709.01507Selective Kernel (SK) - (https://arxiv.org/abs/1903.06586Split (SPLAT) -https://arxiv.org/abs/2004.08955Shifted Window (SWIN) -https://arxiv.org/abs/2103.14030ResultsModel validation results can be found in thedocumentationand in theresults tablesGetting Started (Documentation)My currentdocumentationfortimmcovers the basics.timmdocsis quickly becoming a much more comprehensive set of documentation fortimm. A big thanks toAman Arorafor his efforts creating timmdocs.paperswithcodeis a good resource for browsing the models withintimm.Train, Validation, Inference ScriptsThe 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. Seedocumentationfor some basics andtraining hparamsfor some train examples that produce SOTA ImageNet results.Awesome PyTorch ResourcesOne 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 SegmentationDetectron2 -https://github.com/facebookresearch/detectron2Segmentation Models (Semantic) -https://github.com/qubvel/segmentation_models.pytorchEfficientDet (Obj Det, Semantic soon) -https://github.com/rwightman/efficientdet-pytorchComputer Vision / Image AugmentationAlbumentations -https://github.com/albumentations-team/albumentationsKornia -https://github.com/kornia/korniaKnowledge DistillationRepDistiller -https://github.com/HobbitLong/RepDistillertorchdistill -https://github.com/yoshitomo-matsubara/torchdistillMetric LearningPyTorch Metric Learning -https://github.com/KevinMusgrave/pytorch-metric-learningTraining / Frameworksfastai -https://github.com/fastai/fastaiLicensesCodeThe 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 WeightsSo 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.Pretrained on more than ImageNetSeveral 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.CitingBibTeX@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