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@rwightman rwightman released this 29 Jul 05:18
· 1 commit to main since this release
v1.0.8
a6fe31b

July 28, 2024

  • Add mobilenet_edgetpu_v2_m weights w/ ra4 mnv4-small based recipe. 80.1% top-1 @ 224 and 80.7 @ 256.
  • Release 1.0.8

July 26, 2024

  • More MobileNet-v4 weights, ImageNet-12k pretrain w/ fine-tunes, and anti-aliased ConvLarge models
model top1 top1_err top5 top5_err param_count img_size
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k 84.99 15.01 97.294 2.706 32.59 544
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k 84.772 15.228 97.344 2.656 32.59 480
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k 84.64 15.36 97.114 2.886 32.59 448
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k 84.314 15.686 97.102 2.898 32.59 384
mobilenetv4_conv_aa_large.e600_r384_in1k 83.824 16.176 96.734 3.266 32.59 480
mobilenetv4_conv_aa_large.e600_r384_in1k 83.244 16.756 96.392 3.608 32.59 384
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k 82.99 17.01 96.67 3.33 11.07 320
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k 82.364 17.636 96.256 3.744 11.07 256
model top1 top1_err top5 top5_err param_count img_size
efficientnet_b0.ra4_e3600_r224_in1k 79.364 20.636 94.754 5.246 5.29 256
efficientnet_b0.ra4_e3600_r224_in1k 78.584 21.416 94.338 5.662 5.29 224
mobilenetv1_100h.ra4_e3600_r224_in1k 76.596 23.404 93.272 6.728 5.28 256
mobilenetv1_100.ra4_e3600_r224_in1k 76.094 23.906 93.004 6.996 4.23 256
mobilenetv1_100h.ra4_e3600_r224_in1k 75.662 24.338 92.504 7.496 5.28 224
mobilenetv1_100.ra4_e3600_r224_in1k 75.382 24.618 92.312 7.688 4.23 224
  • Prototype of set_input_size() added to vit and swin v1/v2 models to allow changing image size, patch size, window size after model creation.
  • Improved support in swin for different size handling, in addition to set_input_size, always_partition and strict_img_size args have been added to __init__ to allow more flexible input size constraints
  • Fix out of order indices info for intermediate 'Getter' feature wrapper, check out or range indices for same.
  • Add several tiny < .5M param models for testing that are actually trained on ImageNet-1k
model top1 top1_err top5 top5_err param_count img_size crop_pct
test_efficientnet.r160_in1k 47.156 52.844 71.726 28.274 0.36 192 1.0
test_byobnet.r160_in1k 46.698 53.302 71.674 28.326 0.46 192 1.0
test_efficientnet.r160_in1k 46.426 53.574 70.928 29.072 0.36 160 0.875
test_byobnet.r160_in1k 45.378 54.622 70.572 29.428 0.46 160 0.875
test_vit.r160_in1k 42.0 58.0 68.664 31.336 0.37 192 1.0
test_vit.r160_in1k 40.822 59.178 67.212 32.788 0.37 160 0.875
  • Fix vit reg token init, thanks Promisery
  • Other misc fixes

June 24, 2024

  • 3 more MobileNetV4 hyrid weights with different MQA weight init scheme
model top1 top1_err top5 top5_err param_count img_size
mobilenetv4_hybrid_large.ix_e600_r384_in1k 84.356 15.644 96.892 3.108 37.76 448
mobilenetv4_hybrid_large.ix_e600_r384_in1k 83.990 16.010 96.702 3.298 37.76 384
mobilenetv4_hybrid_medium.ix_e550_r384_in1k 83.394 16.606 96.760 3.240 11.07 448
mobilenetv4_hybrid_medium.ix_e550_r384_in1k 82.968 17.032 96.474 3.526 11.07 384
mobilenetv4_hybrid_medium.ix_e550_r256_in1k 82.492 17.508 96.278 3.722 11.07 320
mobilenetv4_hybrid_medium.ix_e550_r256_in1k 81.446 18.554 95.704 4.296 11.07 256
  • florence2 weight loading in DaViT model
Assets 2
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