Published February 7, 2023 | Version v0.8.10dev0
Software Open

rwightman/pytorch-image-models: v0.8.10dev0 Release

Creators

  • 1. @huggingface
  • 2. Weights & Biases
  • 3. independent
  • 4. Technical University of Darmstadt
  • 5. Stony Brook Medicine
  • 6. MIT
  • 7. Kaggle Competition Master
  • 8. @toss
  • 9. Neuro Event Labs Oy
  • 10. University of Georgia
  • 11. ajou university
  • 12. Indie developer
  • 13. University of Bern
  • 14. @NVIDIA
  • 15. MarkAny
  • 16. Mobility Technologies Co., Ltd.

Description

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

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https://github.com/rwightman/pytorch-image-models/tree/v0.8.10dev0 (URL)