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chore(deps): update dependency timm to v0.9.16 #1083

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@renovate renovate bot commented Nov 30, 2023 •
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This PR contains the following updates:

Package Change Age Adoption Passing Confidence
timm ==0.4.12 -> ==0.9.16 age adoption passing confidence

Release Notes

huggingface/pytorch-image-models (timm)

v0.9.16

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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

v0.9.12

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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

v0.9.11

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Nov 20, 2023

v0.9.10

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Nov 4
  • Patch fix for 0.9.9 to fix FrozenBatchnorm2d import path for old torchvision (~2 years )
Nov 3, 2023
  • DFN (Data Filtering Networks) and MetaCLIP ViT weights added
  • DINOv2 'register' ViT model weights added
  • 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

v0.9.9

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Nov 3, 2023
  • DFN (Data Filtering Networks) and MetaCLIP ViT weights added
  • DINOv2 'register' ViT model weights added
  • 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

v0.9.8

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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

v0.9.7

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Small bug fix & extra model from v0.9.6

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

v0.9.6

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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

v0.9.5

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Minor updates and bug fixes. New ResNeXT w/ highest ImageNet eval I'm aware of in the ResNe(X)t family (seresnextaa201d_32x8d.sw_in12k_ft_in1k_384)

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

v0.9.2

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  • Fix _hub deprecation pass through import

v0.9.1

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The first non pre-release since Oct 2022 with a long list of changes from 0.6.x releases...

May 12, 2023
  • Fix Python 3.7 import error re Final[] typing annotation
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.scaled_dot_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

v0.9.0

Compare Source

First non pre-release in a loooong while, changelog from 0.6.x below...

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.scaled_dot_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](https://huggingface.co/timm/maxvit_r

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Kudos, SonarCloud Quality Gate passed!    Quality Gate passed

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@renovate renovate bot force-pushed the renovate/timm-0.x branch 2 times, most recently from da280be to 55669ce Compare January 24, 2024 09:55
@renovate renovate bot changed the title Update dependency timm to v0.9.12 Update dependency timm to v0.9.16 Feb 19, 2024
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@renovate renovate bot changed the title Update dependency timm to v0.9.16 chore(deps): update dependency timm to v0.9.16 May 12, 2024
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