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Release v1.0.7

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@rwightman rwightman released this 19 Jun 06:52
· 404 commits to main since this release

June 12, 2024

  • MobileNetV4 models and initial set of timm trained weights added:
model top1 top1_err top5 top5_err param_count img_size
mobilenetv4_hybrid_large.e600_r384_in1k 84.266 15.734 96.936 3.064 37.76 448
mobilenetv4_hybrid_large.e600_r384_in1k 83.800 16.200 96.770 3.230 37.76 384
mobilenetv4_conv_large.e600_r384_in1k 83.392 16.608 96.622 3.378 32.59 448
mobilenetv4_conv_large.e600_r384_in1k 82.952 17.048 96.266 3.734 32.59 384
mobilenetv4_conv_large.e500_r256_in1k 82.674 17.326 96.31 3.69 32.59 320
mobilenetv4_conv_large.e500_r256_in1k 81.862 18.138 95.69 4.31 32.59 256
mobilenetv4_hybrid_medium.e500_r224_in1k 81.276 18.724 95.742 4.258 11.07 256
mobilenetv4_conv_medium.e500_r256_in1k 80.858 19.142 95.768 4.232 9.72 320
mobilenetv4_hybrid_medium.e500_r224_in1k 80.442 19.558 95.38 4.62 11.07 224
mobilenetv4_conv_blur_medium.e500_r224_in1k 80.142 19.858 95.298 4.702 9.72 256
mobilenetv4_conv_medium.e500_r256_in1k 79.928 20.072 95.184 4.816 9.72 256
mobilenetv4_conv_medium.e500_r224_in1k 79.808 20.192 95.186 4.814 9.72 256
mobilenetv4_conv_blur_medium.e500_r224_in1k 79.438 20.562 94.932 5.068 9.72 224
mobilenetv4_conv_medium.e500_r224_in1k 79.094 20.906 94.77 5.23 9.72 224
mobilenetv4_conv_small.e2400_r224_in1k 74.616 25.384 92.072 7.928 3.77 256
mobilenetv4_conv_small.e1200_r224_in1k 74.292 25.708 92.116 7.884 3.77 256
mobilenetv4_conv_small.e2400_r224_in1k 73.756 26.244 91.422 8.578 3.77 224
mobilenetv4_conv_small.e1200_r224_in1k 73.454 26.546 91.34 8.66 3.77 224
  • Apple MobileCLIP (https://arxiv.org/pdf/2311.17049, FastViT and ViT-B) image tower model support & weights added (part of OpenCLIP support).
  • ViTamin (https://arxiv.org/abs/2404.02132) CLIP image tower model & weights added (part of OpenCLIP support).
  • OpenAI CLIP Modified ResNet image tower modelling & weight support (via ByobNet). Refactor AttentionPool2d.
  • Refactoring & improvements, especially related to classifier_reset and num_features vs head_hidden_size for forward_features() vs pre_logits
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