Package: timm
PyTorch Image ModelsWhat's NewIntroductionModelsFeaturesResultsGetting Started (Documentation)Train, Validation, Inference ScriptsAwesome PyTorch ResourcesLicensesCitingWhat'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.layersmoved totimm.layers:from timm.models.layers import namewill still work via deprecation mapping (but please transition totimm.layers).import timm.models.layers.moduleorfrom timm.models.layers.module import nameneeds to be changed now.Builder, helper, non-model modules intimm.modelshave a_prefix added, ietimm.models.helpers->timm.models._helpers, there are temporary deprecation mapping files but those will be removed.All models now supportarchitecture.pretrained_tagnaming (exresnet50.rsb_a1).The pretrained_tag is the specific weight variant (different head) for the architecture.Using onlyarchitecturedefaults 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 supportfeatures_only=True, there arecheckpoint_filter_fnmethods in any model module that was remapped. These can be passed totimm.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 fortimmweights. Model cards include link to papers, original source, license.Previous 0.6.x can be cloned from0.6.xbranch or installed via pip with version.Feb 19, 2024Next-ViT models added. Adapted fromhttps://github.com/bytedance/Next-ViTHGNet and PP-HGNetV2 models added. Adapted fromhttps://github.com/PaddlePaddle/PaddleClasbySeeFunRemoved setup.py, moved to pyproject.toml based build supported by PDMAdd updated model EMA impl using _for_each for less overheadSupport device args in train script for non GPU devicesOther misc fixes and small additionsMin supported Python version increased to 3.8Release 0.9.16Jan 8, 2024Datasets & transform refactoringHuggingFace streaming (iterable) dataset support (--dataset hfids:org/dataset)Webdataset wrapper tweaks for improved split info fetching, can auto fetch splits from supported HF hub webdatasetTested HFdatasetsand webdataset wrapper streaming from HF hub with recenttimmImageNet uploads tohttps://huggingface.co/timmMake input & target column/field keys consistent across datasets and pass via argsFull monochrome support when using e:g:--input-size 1 224 224or--in-chans 1, sets PIL image conversion appropriately in datasetImproved several alternate crop & resize transforms (ResizeKeepRatio, RandomCropOrPad, etc) for use in PixParse document AI projectAdd SimCLR style color jitter prob along with grayscale and gaussian blur options to augmentations and argsAllow train without validation set (--val-split '') in train scriptAdd--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 codingNov 23, 2023Added EfficientViT-Large models, thanksSeeFunFix Python 3.7 compat, will be dropping support for it soonOther misc fixesRelease 0.9.12Nov 20, 2023Added significant flexibility for Hugging Face Hub based timm models viamodel_argsconfig entry.model_argswill be passed as kwargs through to models on creation.See example athttps://huggingface.co/gaunernst/vit_base_patch16_1024_128.audiomae_as2m_ft_as20k/blob/main/config.jsonUsage:https://github.com/huggingface/pytorch-image-models/discussions/2035Updated imagenet eval and test set csv files with latest modelsvision_transformer.pytyping and doc cleanup byLaureηt0.9.11 releaseNov 3, 2023DFN (Data Filtering Networks)andMetaCLIPViT weights addedDINOv2 'register' ViT model weights added (https://huggingface.co/papers/2309.16588,https://huggingface.co/papers/2304.07193)AddquickgeluViT variants for OpenAI, DFN, MetaCLIP weights that use it (less efficient)Improved typing added to ResNet, MobileNet-v3 thanks toAryanImageNet-12k fine-tuned (from LAION-2B CLIP)convnext_xxlarge0.9.9 releaseOct 20, 2023SigLIPimage tower weights supported invision_transformer.py.Great potential for fine-tune and downstream feature use.Experimental 'register' support in vit models as perVision Transformers Need RegistersUpdated RepViT with new weight release. ThankswangaoAdd patch resizing support (on pretrained weight load) to Swin models0.9.8 release pendingSep 1, 2023TinyViT added bySeeFunFix EfficientViT (MIT) to use torch.autocast so it works back to PT 1.100.9.7 releaseAug 28, 2023Add dynamic img size support to models invision_transformer.py,vision_transformer_hybrid.py,deit.py, andeva.pyw/o breaking backward compat.Adddynamic_img_size=Trueto args at model creation time to allow changing the grid size (interpolate abs and/or ROPE pos embed each forward pass).Adddynamic_img_pad=Trueto 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 differentimg_size(interpolate pretrained embed weights once) on creation still works.Existing method of changingpatch_size(resize pretrained patch_embed weights once) on creation still works.Example validation cmdpython 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=TrueAug 25, 2023Many new models since last releaseFastViT -https://arxiv.org/abs/2303.14189MobileOne -https://arxiv.org/abs/2206.04040InceptionNeXt -https://arxiv.org/abs/2303.16900RepGhostNet -https://arxiv.org/abs/2211.06088(thankshttps://github.com/ChengpengChen)GhostNetV2 -https://arxiv.org/abs/2211.12905(thankshttps://github.com/yehuitang)EfficientViT (MSRA) -https://arxiv.org/abs/2305.07027(thankshttps://github.com/seefun)EfficientViT (MIT) -https://arxiv.org/abs/2205.14756(thankshttps://github.com/seefun)Add--reparamarg tobenchmark.py,onnx_export.py, andvalidate.pyto trigger layer reparameterization / fusion for models with any one ofreparameterize(),switch_to_deploy()orfuse()Including FastViT, MobileOne, RepGhostNet, EfficientViT (MSRA), RepViT, RepVGG, and LeViTPreparing 0.9.6 'back to school' releaseAug 11, 2023Swin, MaxViT, CoAtNet, and BEiT models support resizing of image/window size on creation with adaptation of pretrained weightsExample validation cmd to test w/ non-square resizepython 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=256,320Aug 3, 2023Add GluonCV weights for HRNet w18_small and w18_small_v2. Converted bySeeFunFixselecsls*model naming regressionPatch and position embedding for ViT/EVA works for bfloat16/float16 weights on load (or activations for on-the-fly resize)v0.9.5 release prepJuly 27, 2023Added timm trainedseresnextaa201d_32x8d.sw_in12k_ft_in1k_384weights (and.sw_in12kpretrain) 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 bywangaoI-JEPA ViT feature weights (no classifier) added bySeeFunSAM-ViT (segment anything) feature weights (no classifier) added bySeeFunAdd support for alternative feat extraction methods and -ve indices to EfficientNetAdd NAdamW optimizerMisc fixesMay 11, 2023timm0.9 released, transition from 0.8.xdev releasesMay 10, 2023Hugging Face Hub downloading is now default, 1132 models onhttps://huggingface.co/timm, 1163 weights intimmDINOv2 vit feature backbone weights added thanks toLeng YueFB MAE vit feature backbone weights addedOpenCLIP DataComp-XL L/14 feat backbone weights addedMetaFormer (poolformer-v2, caformer, convformer, updated poolformer (v1)) w/ weights added byFredo GuanExperimentalget_intermediate_layersfunction 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 ifpretrained=Trueand no weights exist (instead of continuing with random initialization)Fix regression with inception / nasnet TF sourced weights with 1001 classes in original classifiersbitsandbytes (https://github.com/TimDettmers/bitsandbytes) optimizers added to factory, usebnbprefix, iebnbadam8bitMisc cleanup and fixesFinal testing before switching to a 0.9 and bringingtimmout of pre-release stateApril 27, 202397% oftimmmodels uploaded to HF Hub and almost all updated to support multi-weight pretrained configsMinor 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, 2023Gradient accumulation support added to train script and tested (--grad-accum-steps), thanksTaeksang KimMore weights on HF Hub (cspnet, cait, volo, xcit, tresnet, hardcorenas, densenet, dpn, vovnet, xception_aligned)Added--head-init-scaleand--head-init-biasto train.py to scale classiifer head and set fixed bias for fine-tuneRemove all InplaceABN (inplace_abn) use, replaced use in tresnet with standard BatchNorm (modified weights accordingly).April 12, 2023Add 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 intodrop_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/disableAdd 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, 2023ALL ResNet models pushed to Hugging Face Hub with multi-weight supportAll pasttimmtrained weights added with recipe based tags to differentiateAll ResNet strikes back A1/A2/A3 (seed 0) and R50 example B/C1/C2/D weights availableAdd torchvision v2 recipe weights to existing torchvision originalsSee comparison table inhttps://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288#model-comparisonNew ImageNet-12k + ImageNet-1k fine-tunes available for a few anti-aliased ResNet modelsresnetaa50d.sw_in12k_ft_in1k- 81.7 @ 224, 82.6 @ 288resnetaa101d.sw_in12k_ft_in1k- 83.5 @ 224, 84.1 @ 288seresnextaa101d_32x8d.sw_in12k_ft_in1k- 86.0 @ 224, 86.5 @ 288seresnextaa101d_32x8d.sw_in12k_ft_in1k_288- 86.5 @ 288, 86.7 @ 320March 31, 2023Add first ConvNext-XXLarge CLIP -> IN-1k fine-tune and IN-12k intermediate fine-tunes for convnext-base/large CLIP models.modeltop1top5img_sizeparam_countgmacsmactsconvnext_xxlarge.clip_laion2b_soup_ft_in1k88.61298.704256846.47198.09124.45convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_38488.31298.578384200.13101.11126.74convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_32087.96898.47320200.1370.2188.02convnext_base.clip_laion2b_augreg_ft_in12k_in1k_38487.13898.21238488.5945.2184.49convnext_base.clip_laion2b_augreg_ft_in12k_in1k86.34497.9725688.5920.0937.55Add 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 athttps://github.com/baaivision/EVAfor more details on their work blending MIM, CLIP w/ many model, dataset, and train recipe tweaks.modeltop1top5param_countimg_sizeeva02_large_patch14_448.mim_m38m_ft_in22k_in1k90.05499.042305.08448eva02_large_patch14_448.mim_in22k_ft_in22k_in1k89.94699.01305.08448eva_giant_patch14_560.m30m_ft_in22k_in1k89.79298.9921014.45560eva02_large_patch14_448.mim_in22k_ft_in1k89.62698.954305.08448eva02_large_patch14_448.mim_m38m_ft_in1k89.5798.918305.08448eva_giant_patch14_336.m30m_ft_in22k_in1k89.5698.9561013.01336eva_giant_patch14_336.clip_ft_in1k89.46698.821013.01336eva_large_patch14_336.in22k_ft_in22k_in1k89.21498.854304.53336eva_giant_patch14_224.clip_ft_in1k88.88298.6781012.56224eva02_base_patch14_448.mim_in22k_ft_in22k_in1k88.69298.72287.12448eva_large_patch14_336.in22k_ft_in1k88.65298.722304.53336eva_large_patch14_196.in22k_ft_in22k_in1k88.59298.656304.14196eva02_base_patch14_448.mim_in22k_ft_in1k88.2398.56487.12448eva_large_patch14_196.in22k_ft_in1k87.93498.504304.14196eva02_small_patch14_336.mim_in22k_ft_in1k85.7497.61422.13336eva02_tiny_patch14_336.mim_in22k_ft_in1k80.65895.5245.76336Multi-weight and HF hub for DeiT and MLP-Mixer based modelsMarch 22, 2023More 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.pySwin Transformer models support feature extraction (NCHW feat maps forswinv2_cr_*, and NHWC for all others) and spatial embedding outputs.FocalNet (fromhttps://github.com/microsoft/FocalNet) models and weights added with significant refactoring, feature extraction, no fixed resolution / sizing constraintRegNet 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-tunedtimmweights:rexnetr_200.sw_in12k_ft_in1k- 82.6 @ 224, 83.2 @ 288rexnetr_300.sw_in12k_ft_in1k- 84.0 @ 224, 84.5 @ 288regnety_120.sw_in12k_ft_in1k- 85.0 @ 224, 85.4 @ 288regnety_160.lion_in12k_ft_in1k- 85.6 @ 224, 86.0 @ 288regnety_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, 2023Add ConvNeXt-XXLarge CLIP pretrained image tower weights for fine-tune & features (fine-tuning TBD) -- seemodel cardUpdateconvnext_xxlargedefault LayerNorm eps to 1e-5 (for CLIP weights, improved stability)0.8.15dev0Feb 20, 2023Add 320x320convnext_large_mlp.clip_laion2b_ft_320andconvnext_lage_mlp.clip_laion2b_ft_soup_320CLIP image tower weights for features & fine-tune0.8.13dev0 pypi release for latest changes w/ move to huggingface orgFeb 16, 2023safetensorcheckpoint support addedAdd ideas from 'Scaling Vision Transformers to 22 B. Params' (https://arxiv.org/abs/2302.05442) -- qk norm, RmsNorm, parallel blockAdd F.scaled_dot_product_attention support (PyTorch 2.0 only) tovit_*,vit_relpos*,coatnet/maxxvit(to start)Lion optimizer (w/ multi-tensor option) added (https://arxiv.org/abs/2302.06675)gradient checkpointing works withfeatures_only=TrueIntroductionPyTorchImageModels (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.FeaturesModelsAll 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/2105.12723BEiT -https://arxiv.org/abs/2106.08254Big 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/2104.06399CoAtNet (Convolution and Attention) -https://arxiv.org/abs/2106.04803ConvNeXt -https://arxiv.org/abs/2201.03545ConvNeXt-V2 -http://arxiv.org/abs/2301.00808ConViT (Soft Convolutional Inductive Biases Vision Transformers)-https://arxiv.org/abs/2103.10697CspNet (Cross-Stage Partial Networks) -https://arxiv.org/abs/1911.11929DeiT -https://arxiv.org/abs/2012.12877DeiT-III -https://arxiv.org/pdf/2204.07118.pdfDenseNet -https://arxiv.org/abs/1608.06993DLA -https://arxiv.org/abs/1707.06484DPN (Dual-Path Network) -https://arxiv.org/abs/1707.01629EdgeNeXt -https://arxiv.org/abs/2206.10589EfficientFormer -https://arxiv.org/abs/2206.01191EfficientNet (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/1904.02877TinyNet -https://arxiv.org/abs/2010.14819EfficientViT (MIT) -https://arxiv.org/abs/2205.14756EfficientViT (MSRA) -https://arxiv.org/abs/2305.07027EVA -https://arxiv.org/abs/2211.07636EVA-02 -https://arxiv.org/abs/2303.11331FastViT -https://arxiv.org/abs/2303.14189FlexiViT -https://arxiv.org/abs/2212.08013FocalNet (Focal Modulation Networks) -https://arxiv.org/abs/2203.11926GCViT (Global Context Vision Transformer) -https://arxiv.org/abs/2206.09959GhostNet -https://arxiv.org/abs/1911.11907GhostNet-V2 -https://arxiv.org/abs/2211.12905gMLP -https://arxiv.org/abs/2105.08050GPU-Efficient Networks -https://arxiv.org/abs/2006.14090Halo Nets -https://arxiv.org/abs/2103.12731HGNet / HGNet-V2 - TBDHRNet -https://arxiv.org/abs/1908.07919InceptionNeXt -https://arxiv.org/abs/2303.16900Inception-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/2104.01136MaxViT (Multi-Axis Vision Transformer) -https://arxiv.org/abs/2204.01697MetaFormer (PoolFormer-v2, ConvFormer, CAFormer) -https://arxiv.org/abs/2210.13452MLP-Mixer -https://arxiv.org/abs/2105.01601MobileNet-V3 (MBConvNet w/ Efficient Head) -https://arxiv.org/abs/1905.02244FBNet-V3 -https://arxiv.org/abs/2006.02049HardCoRe-NAS -https://arxiv.org/abs/2102.11646LCNet -https://arxiv.org/abs/2109.15099MobileOne -https://arxiv.org/abs/2206.04040MobileViT -https://arxiv.org/abs/2110.02178MobileViT-V2 -https://arxiv.org/abs/2206.02680MViT-V2 (Improved Multiscale Vision Transformer) -https://arxiv.org/abs/2112.01526NASNet-A -https://arxiv.org/abs/1707.07012NesT -https://arxiv.org/abs/2105.12723Next-ViT -https://arxiv.org/abs/2207.05501NFNet-F -https://arxiv.org/abs/2102.06171NF-RegNet / NF-ResNet -https://arxiv.org/abs/2101.08692PNasNet -https://arxiv.org/abs/1712.00559PoolFormer (MetaFormer) -https://arxiv.org/abs/2111.11418Pooling-based Vision Transformer (PiT) -https://arxiv.org/abs/2103.16302PVT-V2 (Improved Pyramid Vision Transformer) -https://arxiv.org/abs/2106.13797RegNet -https://arxiv.org/abs/2003.13678RegNetZ -https://arxiv.org/abs/2103.06877RepVGG -https://arxiv.org/abs/2101.03697RepGhostNet -https://arxiv.org/abs/2211.06088RepViT -https://arxiv.org/abs/2307.09283ResMLP -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/1903.06586Sequencer2D -https://arxiv.org/abs/2205.01972Swin S3 (AutoFormerV2) -https://arxiv.org/abs/2111.14725Swin Transformer -https://arxiv.org/abs/2103.14030Swin Transformer V2 -https://arxiv.org/abs/2111.09883Transformer-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.pdfVisformer -https://arxiv.org/abs/2104.12533Vision Transformer -https://arxiv.org/abs/2010.11929VOLO (Vision Outlooker) -https://arxiv.org/abs/2106.13112VovNet 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/1802.02611XCiT (Cross-Covariance Image Transformers) -https://arxiv.org/abs/2106.09681OptimizersIncluded optimizers available viacreate_optimizer/create_optimizer_v2factory methods:adabeliefan implementation of AdaBelief adapted fromhttps://github.com/juntang-zhuang/Adabelief-Optimizer-https://arxiv.org/abs/2010.07468adafactoradapted fromFAIRSeq impl-https://arxiv.org/abs/1804.04235adahessianbyDavid Samuel-https://arxiv.org/abs/2006.00719adampandsgdpbyNaver ClovAI-https://arxiv.org/abs/2006.08217adanan implementation of Adan adapted fromhttps://github.com/sail-sg/Adan-https://arxiv.org/abs/2208.06677lamban implementation of Lamb and LambC (w/ trust-clipping) cleaned up and modified to support use with XLA -https://arxiv.org/abs/1904.00962larsan implementation of LARS and LARC (w/ trust-clipping) -https://arxiv.org/abs/1708.03888lionand implementation of Lion adapted fromhttps://github.com/google/automl/tree/master/lion-https://arxiv.org/abs/2302.06675lookaheadadapted from impl byLiam-https://arxiv.org/abs/1907.08610madgrad- and implementation of MADGRAD adapted fromhttps://github.com/facebookresearch/madgrad-https://arxiv.org/abs/2101.11075nadaman implementation of Adam w/ Nesterov momentumnadamwan impementation of AdamW (Adam w/ decoupled weight-decay) w/ Nesterov momentum. A simplified impl based onhttps://github.com/mlcommons/algorithmic-efficiencynovogradbyMasashi Kimura-https://arxiv.org/abs/1905.11286radambyLiyuan Liu-https://arxiv.org/abs/1908.03265rmsprop_tfadapted from PyTorch RMSProp by myself. Reproduces much improved Tensorflow RMSProp behavioursgdwand implementation of SGD w/ decoupled weight-decayfused<name>optimizers by name withNVIDIA Apexinstalledbits<name>optimizers by name withBitsAndBytesinstalledAugmentationsRandom Erasing fromZhun Zhong-https://arxiv.org/abs/1708.04896)Mixup -https://arxiv.org/abs/1710.09412CutMix -https://arxiv.org/abs/1905.04899AutoAugment (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/1912.02781SplitBachNorm - allows splitting batch norm layers between clean and augmented (auxiliary batch norm) dataRegularizationDropPath aka "Stochastic Depth" -https://arxiv.org/abs/1603.09382DropBlock -https://arxiv.org/abs/1810.12890Blur Pooling -https://arxiv.org/abs/1904.11486OtherSeveral (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,plateauSpace-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 theresults tablesGetting Started (Documentation)The official documentation can be found athttps://huggingface.co/docs/hub/timm. Documentation contributions are welcome.Getting Started with PyTorch Image Models (timm): A Practitioner’s GuidebyChris Hughesis an extensive blog post covering many aspects oftimmin detail.timmdocsis an alternate 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. Seedocumentation.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 (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 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