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PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

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PyTorch Image Models

Sponsors

Thanks to the following for hardware support:

And a big thanks to all GitHub sponsors who helped with some of my costs before I joined Hugging Face.

What's New

  • ❗Updates after Oct 10, 2022 are available in 0.8.x pre-releases (pip install --pre timm) or cloning main❗
  • Stable releases are 0.6.x and available by normal pip install or clone from 0.6.x branch.

Jan 20, 2023

  • Add two convnext 12k -> 1k fine-tunes at 384x384

    • convnext_tiny.in12k_ft_in1k_384 - 85.1 @ 384
    • convnext_small.in12k_ft_in1k_384 - 86.2 @ 384

  • Push all MaxxViT weights to HF hub, and add new ImageNet-12k -> 1k fine-tunes for rw base MaxViT and CoAtNet 1/2 models

model top1 top5 samples / sec Params (M) GMAC Act (M)
maxvit_xlarge_tf_512.in21k_ft_in1k 88.53 98.64 21.76 475.77 534.14 1413.22
maxvit_xlarge_tf_384.in21k_ft_in1k 88.32 98.54 42.53 475.32 292.78 668.76
maxvit_base_tf_512.in21k_ft_in1k 88.20 98.53 50.87 119.88 138.02 703.99
maxvit_large_tf_512.in21k_ft_in1k 88.04 98.40 36.42 212.33 244.75 942.15
maxvit_large_tf_384.in21k_ft_in1k 87.98 98.56 71.75 212.03 132.55 445.84
maxvit_base_tf_384.in21k_ft_in1k 87.92 98.54 104.71 119.65 73.80 332.90
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k 87.81 98.37 106.55 116.14 70.97 318.95
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k 87.47 98.37 149.49 116.09 72.98 213.74
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k 87.39 98.31 160.80 73.88 47.69 209.43
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k 86.89 98.02 375.86 116.14 23.15 92.64
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k 86.64 98.02 501.03 116.09 24.20 62.77
maxvit_base_tf_512.in1k 86.60 97.92 50.75 119.88 138.02 703.99
coatnet_2_rw_224.sw_in12k_ft_in1k 86.57 97.89 631.88 73.87 15.09 49.22
maxvit_large_tf_512.in1k 86.52 97.88 36.04 212.33 244.75 942.15
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k 86.49 97.90 620.58 73.88 15.18 54.78
maxvit_base_tf_384.in1k 86.29 97.80 101.09 119.65 73.80 332.90
maxvit_large_tf_384.in1k 86.23 97.69 70.56 212.03 132.55 445.84
maxvit_small_tf_512.in1k 86.10 97.76 88.63 69.13 67.26 383.77
maxvit_tiny_tf_512.in1k 85.67 97.58 144.25 31.05 33.49 257.59
maxvit_small_tf_384.in1k 85.54 97.46 188.35 69.02 35.87 183.65
maxvit_tiny_tf_384.in1k 85.11 97.38 293.46 30.98 17.53 123.42
maxvit_large_tf_224.in1k 84.93 96.97 247.71 211.79 43.68 127.35
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k 84.90 96.96 1025.45 41.72 8.11 40.13
maxvit_base_tf_224.in1k 84.85 96.99 358.25 119.47 24.04 95.01
maxxvit_rmlp_small_rw_256.sw_in1k 84.63 97.06 575.53 66.01 14.67 58.38
coatnet_rmlp_2_rw_224.sw_in1k 84.61 96.74 625.81 73.88 15.18 54.78
maxvit_rmlp_small_rw_224.sw_in1k 84.49 96.76 693.82 64.90 10.75 49.30
maxvit_small_tf_224.in1k 84.43 96.83 647.96 68.93 11.66 53.17
maxvit_rmlp_tiny_rw_256.sw_in1k 84.23 96.78 807.21 29.15 6.77 46.92
coatnet_1_rw_224.sw_in1k 83.62 96.38 989.59 41.72 8.04 34.60
maxvit_tiny_rw_224.sw_in1k 83.50 96.50 1100.53 29.06 5.11 33.11
maxvit_tiny_tf_224.in1k 83.41 96.59 1004.94 30.92 5.60 35.78
coatnet_rmlp_1_rw_224.sw_in1k 83.36 96.45 1093.03 41.69 7.85 35.47
maxxvitv2_nano_rw_256.sw_in1k 83.11 96.33 1276.88 23.70 6.26 23.05
maxxvit_rmlp_nano_rw_256.sw_in1k 83.03 96.34 1341.24 16.78 4.37 26.05
maxvit_rmlp_nano_rw_256.sw_in1k 82.96 96.26 1283.24 15.50 4.47 31.92
maxvit_nano_rw_256.sw_in1k 82.93 96.23 1218.17 15.45 4.46 30.28
coatnet_bn_0_rw_224.sw_in1k 82.39 96.19 1600.14 27.44 4.67 22.04
coatnet_0_rw_224.sw_in1k 82.39 95.84 1831.21 27.44 4.43 18.73
coatnet_rmlp_nano_rw_224.sw_in1k 82.05 95.87 2109.09 15.15 2.62 20.34
coatnext_nano_rw_224.sw_in1k 81.95 95.92 2525.52 14.70 2.47 12.80
coatnet_nano_rw_224.sw_in1k 81.70 95.64 2344.52 15.14 2.41 15.41
maxvit_rmlp_pico_rw_256.sw_in1k 80.53 95.21 1594.71 7.52 1.85 24.86

Jan 11, 2023

  • Update ConvNeXt ImageNet-12k pretrain series w/ two new fine-tuned weights (and pre FT .in12k tags)
    • convnext_nano.in12k_ft_in1k - 82.3 @ 224, 82.9 @ 288 (previously released)
    • convnext_tiny.in12k_ft_in1k - 84.2 @ 224, 84.5 @ 288
    • convnext_small.in12k_ft_in1k - 85.2 @ 224, 85.3 @ 288

Jan 6, 2023

  • Finally got around to adding --model-kwargs and --opt-kwargs to scripts to pass through rare args directly to model classes from cmd line
    • train.py /imagenet --model resnet50 --amp --model-kwargs output_stride=16 act_layer=silu
    • train.py /imagenet --model vit_base_patch16_clip_224 --img-size 240 --amp --model-kwargs img_size=240 patch_size=12
  • Cleanup some popular models to better support arg passthrough / merge with model configs, more to go.

Jan 5, 2023

Dec 23, 2022 🎄☃

  • Add FlexiViT models and weights from https://github.com/google-research/big_vision (check out paper at https://arxiv.org/abs/2212.08013)
    • NOTE currently resizing is static on model creation, on-the-fly dynamic / train patch size sampling is a WIP
  • Many more models updated to multi-weight and downloadable via HF hub now (convnext, efficientnet, mobilenet, vision_transformer*, beit)
  • More model pretrained tag and adjustments, some model names changed (working on deprecation translations, consider main branch DEV branch right now, use 0.6.x for stable use)
  • More ImageNet-12k (subset of 22k) pretrain models popping up:
    • efficientnet_b5.in12k_ft_in1k - 85.9 @ 448x448
    • vit_medium_patch16_gap_384.in12k_ft_in1k - 85.5 @ 384x384
    • vit_medium_patch16_gap_256.in12k_ft_in1k - 84.5 @ 256x256
    • convnext_nano.in12k_ft_in1k - 82.9 @ 288x288

Dec 8, 2022

  • Add 'EVA l' to vision_transformer.py, MAE style ViT-L/14 MIM pretrain w/ EVA-CLIP targets, FT on ImageNet-1k (w/ ImageNet-22k intermediate for some)
model top1 param_count gmac macts hub
eva_large_patch14_336.in22k_ft_in22k_in1k 89.2 304.5 191.1 270.2 link
eva_large_patch14_336.in22k_ft_in1k 88.7 304.5 191.1 270.2 link
eva_large_patch14_196.in22k_ft_in22k_in1k 88.6 304.1 61.6 63.5 link
eva_large_patch14_196.in22k_ft_in1k 87.9 304.1 61.6 63.5 link

Dec 6, 2022

model top1 param_count gmac macts hub
eva_giant_patch14_560.m30m_ft_in22k_in1k 89.8 1014.4 1906.8 2577.2 link
eva_giant_patch14_336.m30m_ft_in22k_in1k 89.6 1013 620.6 550.7 link
eva_giant_patch14_336.clip_ft_in1k 89.4 1013 620.6 550.7 link
eva_giant_patch14_224.clip_ft_in1k 89.1 1012.6 267.2 192.6 link

Dec 5, 2022

  • Pre-release (0.8.0dev0) of multi-weight support (model_arch.pretrained_tag). Install with pip install --pre timm
    • vision_transformer, maxvit, convnext are the first three model impl w/ support
    • model names are changing with this (previous _21k, etc. fn will merge), still sorting out deprecation handling
    • bugs are likely, but I need feedback so please try it out
    • if stability is needed, please use 0.6.x pypi releases or clone from 0.6.x branch
  • Support for PyTorch 2.0 compile is added in train/validate/inference/benchmark, use --torchcompile argument
  • Inference script allows more control over output, select k for top-class index + prob json, csv or parquet output
  • Add a full set of fine-tuned CLIP image tower weights from both LAION-2B and original OpenAI CLIP models
model top1 param_count gmac macts hub
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k 88.6 632.5 391 407.5 link
vit_large_patch14_clip_336.openai_ft_in12k_in1k 88.3 304.5 191.1 270.2 link
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k 88.2 632 167.4 139.4 link
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k 88.2 304.5 191.1 270.2 link
vit_large_patch14_clip_224.openai_ft_in12k_in1k 88.2 304.2 81.1 88.8 link
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k 87.9 304.2 81.1 88.8 link
vit_large_patch14_clip_224.openai_ft_in1k 87.9 304.2 81.1 88.8 link
vit_large_patch14_clip_336.laion2b_ft_in1k 87.9 304.5 191.1 270.2 link
vit_huge_patch14_clip_224.laion2b_ft_in1k 87.6 632 167.4 139.4 link
vit_large_patch14_clip_224.laion2b_ft_in1k 87.3 304.2 81.1 88.8 link
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k 87.2 86.9 55.5 101.6 link
vit_base_patch16_clip_384.openai_ft_in12k_in1k 87 86.9 55.5 101.6 link
vit_base_patch16_clip_384.laion2b_ft_in1k 86.6 86.9 55.5 101.6 link
vit_base_patch16_clip_384.openai_ft_in1k 86.2 86.9 55.5 101.6 link
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k 86.2 86.6 17.6 23.9 link
vit_base_patch16_clip_224.openai_ft_in12k_in1k 85.9 86.6 17.6 23.9 link
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k 85.8 88.3 17.9 23.9 link
vit_base_patch16_clip_224.laion2b_ft_in1k 85.5 86.6 17.6 23.9 link
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k 85.4 88.3 13.1 16.5 link
vit_base_patch16_clip_224.openai_ft_in1k 85.3 86.6 17.6 23.9 link
vit_base_patch32_clip_384.openai_ft_in12k_in1k 85.2 88.3 13.1 16.5 link
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k 83.3 88.2 4.4 5 link
vit_base_patch32_clip_224.laion2b_ft_in1k 82.6 88.2 4.4 5 link
vit_base_patch32_clip_224.openai_ft_in1k 81.9 88.2 4.4 5 link
  • Port of MaxViT Tensorflow Weights from official impl at https://github.com/google-research/maxvit
    • There was larger than expected drops for the upscaled 384/512 in21k fine-tune weights, possible detail missing, but the 21k FT did seem sensitive to small preprocessing
model top1 param_count gmac macts hub
maxvit_xlarge_tf_512.in21k_ft_in1k 88.5 475.8 534.1 1413.2 link
maxvit_xlarge_tf_384.in21k_ft_in1k 88.3 475.3 292.8 668.8 link
maxvit_base_tf_512.in21k_ft_in1k 88.2 119.9 138 704 link
maxvit_large_tf_512.in21k_ft_in1k 88 212.3 244.8 942.2 link
maxvit_large_tf_384.in21k_ft_in1k 88 212 132.6 445.8 link
maxvit_base_tf_384.in21k_ft_in1k 87.9 119.6 73.8 332.9 link
maxvit_base_tf_512.in1k 86.6 119.9 138 704 link
maxvit_large_tf_512.in1k 86.5 212.3 244.8 942.2 link
maxvit_base_tf_384.in1k 86.3 119.6 73.8 332.9 link
maxvit_large_tf_384.in1k 86.2 212 132.6 445.8 link
maxvit_small_tf_512.in1k 86.1 69.1 67.3 383.8 link
maxvit_tiny_tf_512.in1k 85.7 31 33.5 257.6 link
maxvit_small_tf_384.in1k 85.5 69 35.9 183.6 link
maxvit_tiny_tf_384.in1k 85.1 31 17.5 123.4 link
maxvit_large_tf_224.in1k 84.9 211.8 43.7 127.4 link
maxvit_base_tf_224.in1k 84.9 119.5 24 95 link
maxvit_small_tf_224.in1k 84.4 68.9 11.7 53.2 link
maxvit_tiny_tf_224.in1k 83.4 30.9 5.6 35.8 link

Oct 15, 2022

  • Train and validation script enhancements
  • Non-GPU (ie CPU) device support
  • SLURM compatibility for train script
  • HF datasets support (via ReaderHfds)
  • TFDS/WDS dataloading improvements (sample padding/wrap for distributed use fixed wrt sample count estimate)
  • in_chans !=3 support for scripts / loader
  • Adan optimizer
  • Can enable per-step LR scheduling via args
  • Dataset 'parsers' renamed to 'readers', more descriptive of purpose
  • AMP args changed, APEX via --amp-impl apex, bfloat16 supportedf via --amp-dtype bfloat16
  • main branch switched to 0.7.x version, 0.6x forked for stable release of weight only adds
  • master -> main branch rename

Oct 10, 2022

  • More weights in maxxvit series, incl first ConvNeXt block based coatnext and maxxvit experiments:
    • coatnext_nano_rw_224 - 82.0 @ 224 (G) -- (uses ConvNeXt conv block, no BatchNorm)
    • maxxvit_rmlp_nano_rw_256 - 83.0 @ 256, 83.7 @ 320 (G) (uses ConvNeXt conv block, no BN)
    • maxvit_rmlp_small_rw_224 - 84.5 @ 224, 85.1 @ 320 (G)
    • maxxvit_rmlp_small_rw_256 - 84.6 @ 256, 84.9 @ 288 (G) -- could be trained better, hparams need tuning (uses ConvNeXt block, no BN)
    • coatnet_rmlp_2_rw_224 - 84.6 @ 224, 85 @ 320 (T)
    • NOTE: official MaxVit weights (in1k) have been released at https://github.com/google-research/maxvit -- some extra work is needed to port and adapt since my impl was created independently of theirs and has a few small differences + the whole TF same padding fun.

Sept 23, 2022

  • LAION-2B CLIP image towers supported as pretrained backbones for fine-tune or features (no classifier)
    • vit_base_patch32_224_clip_laion2b
    • vit_large_patch14_224_clip_laion2b
    • vit_huge_patch14_224_clip_laion2b
    • vit_giant_patch14_224_clip_laion2b

Sept 7, 2022

  • Hugging Face timm docs home now exists, look for more here in the future
  • Add BEiT-v2 weights for base and large 224x224 models from https://github.com/microsoft/unilm/tree/master/beit2
  • Add more weights in maxxvit series incl a pico (7.5M params, 1.9 GMACs), two tiny variants:
    • maxvit_rmlp_pico_rw_256 - 80.5 @ 256, 81.3 @ 320 (T)
    • maxvit_tiny_rw_224 - 83.5 @ 224 (G)
    • maxvit_rmlp_tiny_rw_256 - 84.2 @ 256, 84.8 @ 320 (T)

Aug 29, 2022

  • MaxVit window size scales with img_size by default. Add new RelPosMlp MaxViT weight that leverages this:
    • maxvit_rmlp_nano_rw_256 - 83.0 @ 256, 83.6 @ 320 (T)

Aug 26, 2022

Aug 15, 2022

  • ConvNeXt atto weights added
    • convnext_atto - 75.7 @ 224, 77.0 @ 288
    • convnext_atto_ols - 75.9 @ 224, 77.2 @ 288

Aug 5, 2022

  • More custom ConvNeXt smaller model defs with weights
    • convnext_femto - 77.5 @ 224, 78.7 @ 288
    • convnext_femto_ols - 77.9 @ 224, 78.9 @ 288
    • convnext_pico - 79.5 @ 224, 80.4 @ 288
    • convnext_pico_ols - 79.5 @ 224, 80.5 @ 288
    • convnext_nano_ols - 80.9 @ 224, 81.6 @ 288
  • Updated EdgeNeXt to improve ONNX export, add new base variant and weights from original (https://github.com/mmaaz60/EdgeNeXt)

July 28, 2022

  • Add freshly minted DeiT-III Medium (width=512, depth=12, num_heads=8) model weights. Thanks Hugo Touvron!

July 27, 2022

  • All runtime benchmark and validation result csv files are finally up-to-date!
  • A few more weights & model defs added:
    • darknetaa53 - 79.8 @ 256, 80.5 @ 288
    • convnext_nano - 80.8 @ 224, 81.5 @ 288
    • cs3sedarknet_l - 81.2 @ 256, 81.8 @ 288
    • cs3darknet_x - 81.8 @ 256, 82.2 @ 288
    • cs3sedarknet_x - 82.2 @ 256, 82.7 @ 288
    • cs3edgenet_x - 82.2 @ 256, 82.7 @ 288
    • cs3se_edgenet_x - 82.8 @ 256, 83.5 @ 320
  • cs3* weights above all trained on TPU w/ bits_and_tpu branch. Thanks to TRC program!
  • Add output_stride=8 and 16 support to ConvNeXt (dilation)
  • deit3 models not being able to resize pos_emb fixed
  • Version 0.6.7 PyPi release (/w above bug fixes and new weighs since 0.6.5)

July 8, 2022

More models, more fixes

  • Official research models (w/ weights) added:
  • My own models:
    • Small ResNet defs added by request with 1 block repeats for both basic and bottleneck (resnet10 and resnet14)
    • CspNet refactored with dataclass config, simplified CrossStage3 (cs3) option. These are closer to YOLO-v5+ backbone defs.
    • More relative position vit fiddling. Two srelpos (shared relative position) models trained, and a medium w/ class token.
    • Add an alternate downsample mode to EdgeNeXt and train a small model. Better than original small, but not their new USI trained weights.
  • My own model weight results (all ImageNet-1k training)
    • resnet10t - 66.5 @ 176, 68.3 @ 224
    • resnet14t - 71.3 @ 176, 72.3 @ 224
    • resnetaa50 - 80.6 @ 224 , 81.6 @ 288
    • darknet53 - 80.0 @ 256, 80.5 @ 288
    • cs3darknet_m - 77.0 @ 256, 77.6 @ 288
    • cs3darknet_focus_m - 76.7 @ 256, 77.3 @ 288
    • cs3darknet_l - 80.4 @ 256, 80.9 @ 288
    • cs3darknet_focus_l - 80.3 @ 256, 80.9 @ 288
    • vit_srelpos_small_patch16_224 - 81.1 @ 224, 82.1 @ 320
    • vit_srelpos_medium_patch16_224 - 82.3 @ 224, 83.1 @ 320
    • vit_relpos_small_patch16_cls_224 - 82.6 @ 224, 83.6 @ 320
    • edgnext_small_rw - 79.6 @ 224, 80.4 @ 320
  • cs3, darknet, and vit_*relpos weights above all trained on TPU thanks to TRC program! Rest trained on overheating GPUs.
  • Hugging Face Hub support fixes verified, demo notebook TBA
  • Pretrained weights / configs can be loaded externally (ie from local disk) w/ support for head adaptation.
  • Add support to change image extensions scanned by timm datasets/readers. See (huggingface#1274 (comment))
  • Default ConvNeXt LayerNorm impl to use F.layer_norm(x.permute(0, 2, 3, 1), ...).permute(0, 3, 1, 2) via LayerNorm2d in all cases.
    • a bit slower than previous custom impl on some hardware (ie Ampere w/ CL), but overall fewer regressions across wider HW / PyTorch version ranges.
    • previous impl exists as LayerNormExp2d in models/layers/norm.py
  • Numerous bug fixes
  • Currently testing for imminent PyPi 0.6.x release
  • LeViT pretraining of larger models still a WIP, they don't train well / easily without distillation. Time to add distill support (finally)?
  • ImageNet-22k weight training + finetune ongoing, work on multi-weight support (slowly) chugging along (there are a LOT of weights, sigh) ...

May 13, 2022

  • Official Swin-V2 models and weights added from (https://github.com/microsoft/Swin-Transformer). Cleaned up to support torchscript.
  • Some refactoring for existing timm Swin-V2-CR impl, will likely do a bit more to bring parts closer to official and decide whether to merge some aspects.
  • More Vision Transformer relative position / residual post-norm experiments (all trained on TPU thanks to TRC program)
    • vit_relpos_small_patch16_224 - 81.5 @ 224, 82.5 @ 320 -- rel pos, layer scale, no class token, avg pool
    • vit_relpos_medium_patch16_rpn_224 - 82.3 @ 224, 83.1 @ 320 -- rel pos + res-post-norm, no class token, avg pool
    • vit_relpos_medium_patch16_224 - 82.5 @ 224, 83.3 @ 320 -- rel pos, layer scale, no class token, avg pool
    • vit_relpos_base_patch16_gapcls_224 - 82.8 @ 224, 83.9 @ 320 -- rel pos, layer scale, class token, avg pool (by mistake)
  • Bring 512 dim, 8-head 'medium' ViT model variant back to life (after using in a pre DeiT 'small' model for first ViT impl back in 2020)
  • Add ViT relative position support for switching btw existing impl and some additions in official Swin-V2 impl for future trials
  • Sequencer2D impl (https://arxiv.org/abs/2205.01972), added via PR from author (https://github.com/okojoalg)

May 2, 2022

  • Vision Transformer experiments adding Relative Position (Swin-V2 log-coord) (vision_transformer_relpos.py) and Residual Post-Norm branches (from Swin-V2) (vision_transformer*.py)
    • vit_relpos_base_patch32_plus_rpn_256 - 79.5 @ 256, 80.6 @ 320 -- rel pos + extended width + res-post-norm, no class token, avg pool
    • vit_relpos_base_patch16_224 - 82.5 @ 224, 83.6 @ 320 -- rel pos, layer scale, no class token, avg pool
    • vit_base_patch16_rpn_224 - 82.3 @ 224 -- rel pos + res-post-norm, no class token, avg pool
  • Vision Transformer refactor to remove representation layer that was only used in initial vit and rarely used since with newer pretrain (ie How to Train Your ViT)
  • vit_* models support removal of class token, use of global average pool, use of fc_norm (ala beit, mae).

April 22, 2022

  • timm models are now officially supported in fast.ai! Just in time for the new Practical Deep Learning course. timmdocs documentation link updated to timm.fast.ai.
  • Two more model weights added in the TPU trained series. Some In22k pretrain still in progress.
    • seresnext101d_32x8d - 83.69 @ 224, 84.35 @ 288
    • seresnextaa101d_32x8d (anti-aliased w/ AvgPool2d) - 83.85 @ 224, 84.57 @ 288

March 23, 2022

  • Add ParallelBlock and LayerScale option to base vit models to support model configs in Three things everyone should know about ViT
  • convnext_tiny_hnf (head norm first) weights trained with (close to) A2 recipe, 82.2% top-1, could do better with more epochs.

March 21, 2022

  • Merge norm_norm_norm. IMPORTANT this update for a coming 0.6.x release will likely de-stabilize the master branch for a while. Branch 0.5.x or a previous 0.5.x release can be used if stability is required.
  • Significant weights update (all TPU trained) as described in this release
    • regnety_040 - 82.3 @ 224, 82.96 @ 288
    • regnety_064 - 83.0 @ 224, 83.65 @ 288
    • regnety_080 - 83.17 @ 224, 83.86 @ 288
    • regnetv_040 - 82.44 @ 224, 83.18 @ 288 (timm pre-act)
    • regnetv_064 - 83.1 @ 224, 83.71 @ 288 (timm pre-act)
    • regnetz_040 - 83.67 @ 256, 84.25 @ 320
    • regnetz_040h - 83.77 @ 256, 84.5 @ 320 (w/ extra fc in head)
    • resnetv2_50d_gn - 80.8 @ 224, 81.96 @ 288 (pre-act GroupNorm)
    • resnetv2_50d_evos 80.77 @ 224, 82.04 @ 288 (pre-act EvoNormS)
    • regnetz_c16_evos - 81.9 @ 256, 82.64 @ 320 (EvoNormS)
    • regnetz_d8_evos - 83.42 @ 256, 84.04 @ 320 (EvoNormS)
    • xception41p - 82 @ 299 (timm pre-act)
    • xception65 - 83.17 @ 299
    • xception65p - 83.14 @ 299 (timm pre-act)
    • resnext101_64x4d - 82.46 @ 224, 83.16 @ 288
    • seresnext101_32x8d - 83.57 @ 224, 84.270 @ 288
    • resnetrs200 - 83.85 @ 256, 84.44 @ 320
  • HuggingFace hub support fixed w/ initial groundwork for allowing alternative 'config sources' for pretrained model definitions and weights (generic local file / remote url support soon)
  • SwinTransformer-V2 implementation added. Submitted by Christoph Reich. Training experiments and model changes by myself are ongoing so expect compat breaks.
  • Swin-S3 (AutoFormerV2) models / weights added from https://github.com/microsoft/Cream/tree/main/AutoFormerV2
  • MobileViT models w/ weights adapted from https://github.com/apple/ml-cvnets
  • PoolFormer models w/ weights adapted from https://github.com/sail-sg/poolformer
  • VOLO models w/ weights adapted from https://github.com/sail-sg/volo
  • Significant work experimenting with non-BatchNorm norm layers such as EvoNorm, FilterResponseNorm, GroupNorm, etc
  • Enhance support for alternate norm + act ('NormAct') layers added to a number of models, esp EfficientNet/MobileNetV3, RegNet, and aligned Xception
  • Grouped conv support added to EfficientNet family
  • Add 'group matching' API to all models to allow grouping model parameters for application of 'layer-wise' LR decay, lr scale added to LR scheduler
  • Gradient checkpointing support added to many models
  • forward_head(x, pre_logits=False) fn added to all models to allow separate calls of forward_features + forward_head
  • All vision transformer and vision MLP models update to return non-pooled / non-token selected features from foward_features, for consistency with CNN models, token selection or pooling now applied in forward_head

Feb 2, 2022

  • Chris Hughes posted an exhaustive run through of timm on his blog yesterday. Well worth a read. Getting Started with PyTorch Image Models (timm): A Practitioner’s Guide
  • I'm currently prepping to merge the norm_norm_norm branch back to master (ver 0.6.x) in next week or so.
    • The changes are more extensive than usual and may destabilize and break some model API use (aiming for full backwards compat). So, beware pip install git+https://github.com/rwightman/pytorch-image-models installs!
    • 0.5.x releases and a 0.5.x branch will remain stable with a cherry pick or two until dust clears. Recommend sticking to pypi install for a bit if you want stable.

Jan 14, 2022

  • Version 0.5.4 w/ release to be pushed to pypi. It's been a while since last pypi update and riskier changes will be merged to main branch soon....
  • Add ConvNeXT models /w weights from official impl (https://github.com/facebookresearch/ConvNeXt), a few perf tweaks, compatible with timm features
  • Tried training a few small (~1.8-3M param) / mobile optimized models, a few are good so far, more on the way...
    • mnasnet_small - 65.6 top-1
    • mobilenetv2_050 - 65.9
    • lcnet_100/075/050 - 72.1 / 68.8 / 63.1
    • semnasnet_075 - 73
    • fbnetv3_b/d/g - 79.1 / 79.7 / 82.0
  • TinyNet models added by rsomani95
  • LCNet added via MobileNetV3 architecture

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.

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. Here are some example training hparams to get you started.

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

Features

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:

Results

Model validation results can be found in the documentation and in the results tables

Getting Started (Documentation)

My current documentation for timm covers the basics.

Hugging Face timm docs will be the documentation focus going forward and will eventually replace the github.io docs above.

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 quickly becoming a much more comprehensive 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 for some basics and training hparams for some train examples that produce SOTA ImageNet results.

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

Computer Vision / Image Augmentation

Knowledge Distillation

Metric Learning

Training / Frameworks

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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PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

huggingface.co/docs/timm

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