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Recent Changes

Aug 28, 2023

  • Add dynamic img size support to models in vision_transformer.py, vision_transformer_hybrid.py, deit.py, and eva.py w/o breaking backward compat.
    • Add dynamic_img_size=True to args at model creation time to allow changing the grid size (interpolate abs and/or ROPE pos embed each forward pass).
    • Add dynamic_img_pad=True to 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 different img_size (interpolate pretrained embed weights once) on creation still works.
    • Existing method of changing patch_size (resize pretrained patch_embed weights once) on creation still works.
    • Example validation cmd python 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=True

Aug 25, 2023

Aug 11, 2023

  • Swin, MaxViT, CoAtNet, and BEiT models support resizing of image/window size on creation with adaptation of pretrained weights
  • Example validation cmd to test w/ non-square resize python 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=256,320

Aug 3, 2023

  • Add GluonCV weights for HRNet w18_small and w18_small_v2. Converted by SeeFun
  • Fix selecsls* model naming regression
  • Patch and position embedding for ViT/EVA works for bfloat16/float16 weights on load (or activations for on-the-fly resize)
  • v0.9.5 release prep

July 27, 2023

  • Added timm trained seresnextaa201d_32x8d.sw_in12k_ft_in1k_384 weights (and .sw_in12k pretrain) 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 by wangao
  • I-JEPA ViT feature weights (no classifier) added by SeeFun
  • SAM-ViT (segment anything) feature weights (no classifier) added by SeeFun
  • Add support for alternative feat extraction methods and -ve indices to EfficientNet
  • Add NAdamW optimizer
  • Misc fixes

May 11, 2023

  • timm 0.9 released, transition from 0.8.xdev releases

May 10, 2023

  • Hugging Face Hub downloading is now default, 1132 models on https://huggingface.co/timm, 1163 weights in timm
  • DINOv2 vit feature backbone weights added thanks to Leng Yue
  • FB MAE vit feature backbone weights added
  • OpenCLIP DataComp-XL L/14 feat backbone weights added
  • MetaFormer (poolformer-v2, caformer, convformer, updated poolformer (v1)) w/ weights added by Fredo Guan
  • Experimental get_intermediate_layers function 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 if pretrained=True and no weights exist (instead of continuing with random initialization)
  • Fix regression with inception / nasnet TF sourced weights with 1001 classes in original classifiers
  • bitsandbytes (https://github.com/TimDettmers/bitsandbytes) optimizers added to factory, use bnb prefix, ie bnbadam8bit
  • Misc cleanup and fixes
  • Final testing before switching to a 0.9 and bringing timm out of pre-release state

April 27, 2023

  • 97% of timm models uploaded to HF Hub and almost all updated to support multi-weight pretrained configs
  • Minor 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, 2023

  • Gradient accumulation support added to train script and tested (--grad-accum-steps), thanks Taeksang Kim
  • More weights on HF Hub (cspnet, cait, volo, xcit, tresnet, hardcorenas, densenet, dpn, vovnet, xception_aligned)
  • Added --head-init-scale and --head-init-bias to train.py to scale classiifer head and set fixed bias for fine-tune
  • Remove all InplaceABN (inplace_abn) use, replaced use in tresnet with standard BatchNorm (modified weights accordingly).

April 12, 2023

  • Add 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 into drop_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/disable
  • Add 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, 2023

  • ALL ResNet models pushed to Hugging Face Hub with multi-weight support
  • New ImageNet-12k + ImageNet-1k fine-tunes available for a few anti-aliased ResNet models
    • resnetaa50d.sw_in12k_ft_in1k - 81.7 @ 224, 82.6 @ 288
    • resnetaa101d.sw_in12k_ft_in1k - 83.5 @ 224, 84.1 @ 288
    • seresnextaa101d_32x8d.sw_in12k_ft_in1k - 86.0 @ 224, 86.5 @ 288
    • seresnextaa101d_32x8d.sw_in12k_ft_in1k_288 - 86.5 @ 288, 86.7 @ 320

March 31, 2023

  • Add first ConvNext-XXLarge CLIP -> IN-1k fine-tune and IN-12k intermediate fine-tunes for convnext-base/large CLIP models.
model top1 top5 img_size param_count gmacs macts
convnext_xxlarge.clip_laion2b_soup_ft_in1k 88.612 98.704 256 846.47 198.09 124.45
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384 88.312 98.578 384 200.13 101.11 126.74
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320 87.968 98.47 320 200.13 70.21 88.02
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384 87.138 98.212 384 88.59 45.21 84.49
convnext_base.clip_laion2b_augreg_ft_in12k_in1k 86.344 97.97 256 88.59 20.09 37.55
  • Add 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 at https://github.com/baaivision/EVA for more details on their work blending MIM, CLIP w/ many model, dataset, and train recipe tweaks.
model top1 top5 param_count img_size
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k 90.054 99.042 305.08 448
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k 89.946 99.01 305.08 448
eva_giant_patch14_560.m30m_ft_in22k_in1k 89.792 98.992 1014.45 560
eva02_large_patch14_448.mim_in22k_ft_in1k 89.626 98.954 305.08 448
eva02_large_patch14_448.mim_m38m_ft_in1k 89.57 98.918 305.08 448
eva_giant_patch14_336.m30m_ft_in22k_in1k 89.56 98.956 1013.01 336
eva_giant_patch14_336.clip_ft_in1k 89.466 98.82 1013.01 336
eva_large_patch14_336.in22k_ft_in22k_in1k 89.214 98.854 304.53 336
eva_giant_patch14_224.clip_ft_in1k 88.882 98.678 1012.56 224
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k 88.692 98.722 87.12 448
eva_large_patch14_336.in22k_ft_in1k 88.652 98.722 304.53 336
eva_large_patch14_196.in22k_ft_in22k_in1k 88.592 98.656 304.14 196
eva02_base_patch14_448.mim_in22k_ft_in1k 88.23 98.564 87.12 448
eva_large_patch14_196.in22k_ft_in1k 87.934 98.504 304.14 196
eva02_small_patch14_336.mim_in22k_ft_in1k 85.74 97.614 22.13 336
eva02_tiny_patch14_336.mim_in22k_ft_in1k 80.658 95.524 5.76 336
  • Multi-weight and HF hub for DeiT and MLP-Mixer based models

March 22, 2023

  • More 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.py
  • Swin Transformer models support feature extraction (NCHW feat maps for swinv2_cr_*, and NHWC for all others) and spatial embedding outputs.
  • FocalNet (from https://github.com/microsoft/FocalNet) models and weights added with significant refactoring, feature extraction, no fixed resolution / sizing constraint
  • RegNet 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-tuned timm weights:
    • rexnetr_200.sw_in12k_ft_in1k - 82.6 @ 224, 83.2 @ 288
    • rexnetr_300.sw_in12k_ft_in1k - 84.0 @ 224, 84.5 @ 288
    • regnety_120.sw_in12k_ft_in1k - 85.0 @ 224, 85.4 @ 288
    • regnety_160.lion_in12k_ft_in1k - 85.6 @ 224, 86.0 @ 288
    • regnety_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, 2023

  • Add ConvNeXt-XXLarge CLIP pretrained image tower weights for fine-tune & features (fine-tuning TBD) — see model card
  • Update convnext_xxlarge default LayerNorm eps to 1e-5 (for CLIP weights, improved stability)
  • 0.8.15dev0

Feb 20, 2023

  • Add 320x320 convnext_large_mlp.clip_laion2b_ft_320 and convnext_lage_mlp.clip_laion2b_ft_soup_320 CLIP image tower weights for features & fine-tune
  • 0.8.13dev0 pypi release for latest changes w/ move to huggingface org

Feb 16, 2023

  • safetensor checkpoint support added
  • Add ideas from ‘Scaling Vision Transformers to 22 B. Params’ (https://arxiv.org/abs/2302.05442) — qk norm, RmsNorm, parallel block
  • Add F.scaleddot_product_attention support (PyTorch 2.0 only) to `vit, vit_relpos, coatnet/maxxvit` (to start)
  • Lion optimizer (w/ multi-tensor option) added (https://arxiv.org/abs/2302.06675)
  • gradient checkpointing works with features_only=True

Feb 7, 2023

  • New inference benchmark numbers added in results folder.
  • Add convnext LAION CLIP trained weights and initial set of in1k fine-tunes
    • convnext_base.clip_laion2b_augreg_ft_in1k - 86.2% @ 256x256
    • convnext_base.clip_laiona_augreg_ft_in1k_384 - 86.5% @ 384x384
    • convnext_large_mlp.clip_laion2b_augreg_ft_in1k - 87.3% @ 256x256
    • convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384 - 87.9% @ 384x384
  • Add DaViT models. Supports features_only=True. Adapted from https://github.com/dingmyu/davit by Fredo.
  • Use a common NormMlpClassifierHead across MaxViT, ConvNeXt, DaViT
  • Add EfficientFormer-V2 model, update EfficientFormer, and refactor LeViT (closely related architectures). Weights on HF hub.
    • New EfficientFormer-V2 arch, significant refactor from original at (https://github.com/snap-research/EfficientFormer). Supports features_only=True.
    • Minor updates to EfficientFormer.
    • Refactor LeViT models to stages, add features_only=True support to new conv variants, weight remap required.
  • Move ImageNet meta-data (synsets, indices) from /results to timm/data/_info.
  • Add ImageNetInfo / DatasetInfo classes to provide labelling for various ImageNet classifier layouts in timm
    • Update inference.py to use, try: python inference.py /folder/to/images --model convnext_small.in12k --label-type detail --topk 5
  • Ready for 0.8.10 pypi pre-release (final testing).

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 (https://github.com/rwightman/pytorch-image-models/pull/1274#issuecomment-1178303103)
  • 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

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 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/parsers. See (https://github.com/rwightman/pytorch-image-models/pull/1274#issuecomment-1178303103)
  • 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
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