README.md

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

Aug 21, 2024

• Updated SBB ViT models trained on ImageNet-12k and fine-tuned on ImageNet-1k, challenging quite a number of much larger, slower models

model	top1	top5	param_count	img_size
vit_mediumd_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k	87.438	98.256	64.11	384
vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k	86.608	97.934	64.11	256
vit_betwixt_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k	86.594	98.02	60.4	384
vit_betwixt_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k	85.734	97.61	60.4	256

• MobileNet-V1 1.25, EfficientNet-B1, & ResNet50-D weights w/ MNV4 baseline challenge recipe

model	top1	top5	param_count	img_size
resnet50d.ra4_e3600_r224_in1k	81.838	95.922	25.58	288
efficientnet_b1.ra4_e3600_r240_in1k	81.440	95.700	7.79	288
resnet50d.ra4_e3600_r224_in1k	80.952	95.384	25.58	224
efficientnet_b1.ra4_e3600_r240_in1k	80.406	95.152	7.79	240
mobilenetv1_125.ra4_e3600_r224_in1k	77.600	93.804	6.27	256
mobilenetv1_125.ra4_e3600_r224_in1k	76.924	93.234	6.27	224

• Add SAM2 (HieraDet) backbone arch & weight loading support
• Add Hiera Small weights trained w/ abswin pos embed on in12k & fine-tuned on 1k

model	top1	top5	param_count
hiera_small_abswin_256.sbb2_e200_in12k_ft_in1k	84.912	97.260	35.01
hiera_small_abswin_256.sbb2_pd_e200_in12k_ft_in1k	84.560	97.106	35.01

Aug 8, 2024

• Add RDNet ('DenseNets Reloaded', https://arxiv.org/abs/2403.19588), thanks Donghyun Kim

July 28, 2024

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

July 26, 2024

• More MobileNet-v4 weights, ImageNet-12k pretrain w/ fine-tunes, and anti-aliased ConvLarge models

model	top1	top1_err	top5	top5_err	param_count	img_size
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k	84.99	15.01	97.294	2.706	32.59	544
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k	84.772	15.228	97.344	2.656	32.59	480
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k	84.64	15.36	97.114	2.886	32.59	448
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k	84.314	15.686	97.102	2.898	32.59	384
mobilenetv4_conv_aa_large.e600_r384_in1k	83.824	16.176	96.734	3.266	32.59	480
mobilenetv4_conv_aa_large.e600_r384_in1k	83.244	16.756	96.392	3.608	32.59	384
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k	82.99	17.01	96.67	3.33	11.07	320
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k	82.364	17.636	96.256	3.744	11.07	256

• Impressive MobileNet-V1 and EfficientNet-B0 baseline challenges (https://huggingface.co/blog/rwightman/mobilenet-baselines)

model	top1	top1_err	top5	top5_err	param_count	img_size
efficientnet_b0.ra4_e3600_r224_in1k	79.364	20.636	94.754	5.246	5.29	256
efficientnet_b0.ra4_e3600_r224_in1k	78.584	21.416	94.338	5.662	5.29	224
mobilenetv1_100h.ra4_e3600_r224_in1k	76.596	23.404	93.272	6.728	5.28	256
mobilenetv1_100.ra4_e3600_r224_in1k	76.094	23.906	93.004	6.996	4.23	256
mobilenetv1_100h.ra4_e3600_r224_in1k	75.662	24.338	92.504	7.496	5.28	224
mobilenetv1_100.ra4_e3600_r224_in1k	75.382	24.618	92.312	7.688	4.23	224

• Prototype of set_input_size() added to vit and swin v1/v2 models to allow changing image size, patch size, window size after model creation.
• Improved support in swin for different size handling, in addition to set_input_size, always_partition and strict_img_size args have been added to __init__ to allow more flexible input size constraints
• Fix out of order indices info for intermediate 'Getter' feature wrapper, check out or range indices for same.
• Add several tiny < .5M param models for testing that are actually trained on ImageNet-1k

model	top1	top1_err	top5	top5_err	param_count	img_size	crop_pct
test_efficientnet.r160_in1k	47.156	52.844	71.726	28.274	0.36	192	1.0
test_byobnet.r160_in1k	46.698	53.302	71.674	28.326	0.46	192	1.0
test_efficientnet.r160_in1k	46.426	53.574	70.928	29.072	0.36	160	0.875
test_byobnet.r160_in1k	45.378	54.622	70.572	29.428	0.46	160	0.875
test_vit.r160_in1k	42.0	58.0	68.664	31.336	0.37	192	1.0
test_vit.r160_in1k	40.822	59.178	67.212	32.788	0.37	160	0.875

• Fix vit reg token init, thanks Promisery
• Other misc fixes

June 24, 2024

• 3 more MobileNetV4 hyrid weights with different MQA weight init scheme

model	top1	top1_err	top5	top5_err	param_count	img_size
mobilenetv4_hybrid_large.ix_e600_r384_in1k	84.356	15.644	96.892	3.108	37.76	448
mobilenetv4_hybrid_large.ix_e600_r384_in1k	83.990	16.010	96.702	3.298	37.76	384
mobilenetv4_hybrid_medium.ix_e550_r384_in1k	83.394	16.606	96.760	3.240	11.07	448
mobilenetv4_hybrid_medium.ix_e550_r384_in1k	82.968	17.032	96.474	3.526	11.07	384
mobilenetv4_hybrid_medium.ix_e550_r256_in1k	82.492	17.508	96.278	3.722	11.07	320
mobilenetv4_hybrid_medium.ix_e550_r256_in1k	81.446	18.554	95.704	4.296	11.07	256

• florence2 weight loading in DaViT model

June 12, 2024

• MobileNetV4 models and initial set of timm trained weights added:

model	top1	top1_err	top5	top5_err	param_count	img_size
mobilenetv4_hybrid_large.e600_r384_in1k	84.266	15.734	96.936	3.064	37.76	448
mobilenetv4_hybrid_large.e600_r384_in1k	83.800	16.200	96.770	3.230	37.76	384
mobilenetv4_conv_large.e600_r384_in1k	83.392	16.608	96.622	3.378	32.59	448
mobilenetv4_conv_large.e600_r384_in1k	82.952	17.048	96.266	3.734	32.59	384
mobilenetv4_conv_large.e500_r256_in1k	82.674	17.326	96.31	3.69	32.59	320
mobilenetv4_conv_large.e500_r256_in1k	81.862	18.138	95.69	4.31	32.59	256
mobilenetv4_hybrid_medium.e500_r224_in1k	81.276	18.724	95.742	4.258	11.07	256
mobilenetv4_conv_medium.e500_r256_in1k	80.858	19.142	95.768	4.232	9.72	320
mobilenetv4_hybrid_medium.e500_r224_in1k	80.442	19.558	95.38	4.62	11.07	224
mobilenetv4_conv_blur_medium.e500_r224_in1k	80.142	19.858	95.298	4.702	9.72	256
mobilenetv4_conv_medium.e500_r256_in1k	79.928	20.072	95.184	4.816	9.72	256
mobilenetv4_conv_medium.e500_r224_in1k	79.808	20.192	95.186	4.814	9.72	256
mobilenetv4_conv_blur_medium.e500_r224_in1k	79.438	20.562	94.932	5.068	9.72	224
mobilenetv4_conv_medium.e500_r224_in1k	79.094	20.906	94.77	5.23	9.72	224
mobilenetv4_conv_small.e2400_r224_in1k	74.616	25.384	92.072	7.928	3.77	256
mobilenetv4_conv_small.e1200_r224_in1k	74.292	25.708	92.116	7.884	3.77	256
mobilenetv4_conv_small.e2400_r224_in1k	73.756	26.244	91.422	8.578	3.77	224
mobilenetv4_conv_small.e1200_r224_in1k	73.454	26.546	91.34	8.66	3.77	224

• Apple MobileCLIP (https://arxiv.org/pdf/2311.17049, FastViT and ViT-B) image tower model support & weights added (part of OpenCLIP support).
• ViTamin (https://arxiv.org/abs/2404.02132) CLIP image tower model & weights added (part of OpenCLIP support).
• OpenAI CLIP Modified ResNet image tower modelling & weight support (via ByobNet). Refactor AttentionPool2d.

May 14, 2024

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

May 11, 2024

• Searching for Better ViT Baselines (For the GPU Poor) weights and vit variants released. Exploring model shapes between Tiny and Base.

model	top1	top5	param_count	img_size
vit_mediumd_patch16_reg4_gap_256.sbb_in12k_ft_in1k	86.202	97.874	64.11	256
vit_betwixt_patch16_reg4_gap_256.sbb_in12k_ft_in1k	85.418	97.48	60.4	256
vit_mediumd_patch16_rope_reg1_gap_256.sbb_in1k	84.322	96.812	63.95	256
vit_betwixt_patch16_rope_reg4_gap_256.sbb_in1k	83.906	96.684	60.23	256
vit_base_patch16_rope_reg1_gap_256.sbb_in1k	83.866	96.67	86.43	256
vit_medium_patch16_rope_reg1_gap_256.sbb_in1k	83.81	96.824	38.74	256
vit_betwixt_patch16_reg4_gap_256.sbb_in1k	83.706	96.616	60.4	256
vit_betwixt_patch16_reg1_gap_256.sbb_in1k	83.628	96.544	60.4	256
vit_medium_patch16_reg4_gap_256.sbb_in1k	83.47	96.622	38.88	256
vit_medium_patch16_reg1_gap_256.sbb_in1k	83.462	96.548	38.88	256
vit_little_patch16_reg4_gap_256.sbb_in1k	82.514	96.262	22.52	256
vit_wee_patch16_reg1_gap_256.sbb_in1k	80.256	95.360	13.42	256
vit_pwee_patch16_reg1_gap_256.sbb_in1k	80.072	95.136	15.25	256
vit_mediumd_patch16_reg4_gap_256.sbb_in12k	N/A	N/A	64.11	256
vit_betwixt_patch16_reg4_gap_256.sbb_in12k	N/A	N/A	60.4	256

• AttentionExtract helper added to extract attention maps from timm models. See example in huggingface#1232 (comment)
• forward_intermediates() API refined and added to more models including some ConvNets that have other extraction methods.
• 1017 of 1047 model architectures support features_only=True feature extraction. Remaining 34 architectures can be supported but based on priority requests.
• Remove torch.jit.script annotated functions including old JIT activations. Conflict with dynamo and dynamo does a much better job when used.

April 11, 2024

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

model = timm.create_model('vit_base_patch16_224')
final_feat, intermediates = model.forward_intermediates(input) 
output = model.forward_head(final_feat)  # pooling + classifier head

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

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

print(output.shape)
torch.Size([2, 1000])

model = timm.create_model('eva02_base_patch16_clip_224', pretrained=True, img_size=512, features_only=True, out_indices=(-3, -2,))
output = model(torch.randn(2, 3, 512, 512))

for o in output:    
    print(o.shape)   
torch.Size([2, 768, 32, 32])
torch.Size([2, 768, 32, 32])

• TinyCLIP vision tower weights added, thx Thien Tran

Feb 19, 2024

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

Jan 8, 2024

Datasets & transform refactoring

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

Nov 23, 2023

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

Nov 20, 2023

• 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: huggingface#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

Introduction

PyTorch Image Models (timm) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results.

The work of many others is present here. I've tried to make sure all source material is acknowledged via links to github, arxiv papers, etc in the README, documentation, and code docstrings. Please let me know if I missed anything.

Features

Models

All model architecture families include variants with pretrained weights. There are specific model variants without any weights, it is NOT a bug. Help training new or better weights is always appreciated.

• 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
• MobileCLIP - https://arxiv.org/abs/2311.17049
• 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
• MobileNetV4 - https://arxiv.org/abs/2404.10518
• 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
• RDNet (DenseNets Reloaded) - https://arxiv.org/abs/2403.19588
• 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
• ViTamin - https://arxiv.org/abs/2404.02132
• 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 / 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 timm in detail.

timmdocs is an alternate set of documentation for timm. A big thanks to Aman Arora for his efforts creating timmdocs.

paperswithcode is a good resource for browsing the models within timm.

Train, Validation, Inference Scripts

The root folder of the repository contains reference train, validation, and inference scripts that work with the included models and other features of this repository. They are adaptable for other datasets and use cases with a little hacking. See documentation.

Awesome PyTorch Resources

One of the greatest assets of PyTorch is the community and their contributions. A few of my favourite resources that pair well with the models and components here are listed below.

Object Detection, Instance and Semantic Segmentation

• 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

@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}}
}

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