Update model config and README

README.md

@@ -11,9 +11,9 @@ datasets:
 
 A ResNet image classification model. Trained on ImageNet-1k by Ross Wightman.
 
-Trained with `timm` scripts using hyper-parameters inspired by the MobileNet-V4 paper with `timm` enhancements.
-
+Trained with `timm` scripts using hyper-parameters inspired by the MobileNet-V4 small, mixed with go-to hparams from `timm` and "ResNet Strikes Back".
 
+A collection of hparam (timm .yaml config files) for this training series can be found here: https://gist.github.com/rwightman/f6705cb65c03daeebca8aa129b1b94ad
 
 ## Model Details
 - **Model Type:** Image classification / feature backbone
@@ -25,6 +25,7 @@ Trained with `timm` scripts using hyper-parameters inspired by the MobileNet-V4
 - **Dataset:** ImageNet-1k
 - **Papers:**
   - PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
+  - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476
   - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385
   - MobileNetV4 -- Universal Models for the Mobile Ecosystem: https://arxiv.org/abs/2404.10518
 
@@ -157,22 +158,36 @@ output = model.forward_head(output, pre_logits=True)
 | [mobilenet_edgetpu_v2_m.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenet_edgetpu_v2_m.ra4_e3600_r224_in1k)               | 80.130 | 95.002 | 8.46        | 224      |
 | [mobilenetv4_conv_medium.e500_r256_in1k](http://hf.co/timm/mobilenetv4_conv_medium.e500_r256_in1k)                       | 79.928 | 95.184 | 9.72        | 256      |
 | [mobilenetv4_conv_medium.e500_r224_in1k](http://hf.co/timm/mobilenetv4_conv_medium.e500_r224_in1k)                       | 79.808 | 95.186 | 9.72        | 256      |
+| [resnetv2_34d.ra4_e3600_r224_in1k](http://hf.co/timm/resnetv2_34d.ra4_e3600_r224_in1k)                                   | 79.590 | 94.770 | 21.82       | 288      |
 | [mobilenetv4_conv_blur_medium.e500_r224_in1k](http://hf.co/timm/mobilenetv4_conv_blur_medium.e500_r224_in1k)             | 79.438 | 94.932 | 9.72        | 224      |
 | [efficientnet_b0.ra4_e3600_r224_in1k](http://hf.co/timm/efficientnet_b0.ra4_e3600_r224_in1k)                             | 79.364 | 94.754 | 5.29        | 256      |
 | [mobilenetv4_conv_medium.e500_r224_in1k](http://hf.co/timm/mobilenetv4_conv_medium.e500_r224_in1k)                       | 79.094 | 94.77  | 9.72        | 224      |
+| [resnetv2_34.ra4_e3600_r224_in1k](http://hf.co/timm/resnetv2_34.ra4_e3600_r224_in1k)                                     | 79.072 | 94.566 | 21.80       | 288      |
+| [resnet34.ra4_e3600_r224_in1k](http://hf.co/timm/resnet34.ra4_e3600_r224_in1k)                                           | 78.952 | 94.450 | 21.80       | 288      |
 | [efficientnet_b0.ra4_e3600_r224_in1k](http://hf.co/timm/efficientnet_b0.ra4_e3600_r224_in1k)                             | 78.584 | 94.338 | 5.29        | 224      |
+| [resnetv2_34d.ra4_e3600_r224_in1k](http://hf.co/timm/resnetv2_34d.ra4_e3600_r224_in1k)                                   | 78.268 | 93.952 | 21.82       | 224      |
+| [resnetv2_34.ra4_e3600_r224_in1k](http://hf.co/timm/resnetv2_34.ra4_e3600_r224_in1k)                                     | 77.636 | 93.528 | 21.80       | 224      |
 | [mobilenetv1_125.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv1_125.ra4_e3600_r224_in1k)                             | 77.600 | 93.804 | 6.27        | 256      |
-| [mobilenetv3_large_100.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv3_large_100.ra4_e3600_r224_in1k)                 | 77.164 | 93.336 | 5.48        | 256      |
+| [resnet34.ra4_e3600_r224_in1k](http://hf.co/timm/resnet34.ra4_e3600_r224_in1k)                                           | 77.448 | 93.502 | 21.80       | 224      |
+| [mobilenetv3_large_100.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv3_large_100.ra4_e3600_r224_in1k)                 | 77.164 | 93.336 | 5.48        | 256      |              
 | [mobilenetv1_125.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv1_125.ra4_e3600_r224_in1k)                             | 76.924 | 93.234 | 6.27        | 224      |
 | [mobilenetv1_100h.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv1_100h.ra4_e3600_r224_in1k)                           | 76.596 | 93.272 | 5.28        | 256      |
 | [mobilenetv3_large_100.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv3_large_100.ra4_e3600_r224_in1k)                 | 76.310 | 92.846 | 5.48        | 224      |
 | [mobilenetv1_100.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv1_100.ra4_e3600_r224_in1k)                             | 76.094 | 93.004 | 4.23        | 256      |
+| [resnetv2_18d.ra4_e3600_r224_in1k](http://hf.co/timm/resnetv2_18d.ra4_e3600_r224_in1k)                                   | 76.044 | 93.020 | 11.71       | 288      |
+| [resnet18d.ra4_e3600_r224_in1k](http://hf.co/timm/resnet18d.ra4_e3600_r224_in1k)                                         | 76.024 | 92.780 | 11.71       | 288      |
 | [mobilenetv1_100h.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv1_100h.ra4_e3600_r224_in1k)                           | 75.662 | 92.504 | 5.28        | 224      |
 | [mobilenetv1_100.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv1_100.ra4_e3600_r224_in1k)                             | 75.382 | 92.312 | 4.23        | 224      |
-| [mobilenetv4_conv_small.e2400_r224_in1k](http://hf.co/timm/mobilenetv4_conv_small.e2400_r224_in1k)                       | 74.616 | 92.072 | 3.77        | 256      |
+| [resnetv2_18.ra4_e3600_r224_in1k](http://hf.co/timm/resnetv2_18.ra4_e3600_r224_in1k)                                     | 75.340 | 92.678 | 11.69       | 288      |
+| [mobilenetv4_conv_small.e2400_r224_in1k](http://hf.co/timm/mobilenetv4_conv_small.e2400_r224_in1k)                       | 74.616 | 92.072 | 3.77        | 256      |      
+| [resnetv2_18d.ra4_e3600_r224_in1k](http://hf.co/timm/resnetv2_18d.ra4_e3600_r224_in1k)                                   | 74.412 | 91.936 | 11.71       | 224      |
+| [resnet18d.ra4_e3600_r224_in1k](http://hf.co/timm/resnet18d.ra4_e3600_r224_in1k)                                         | 74.322 | 91.832 | 11.71       | 224      |
 | [mobilenetv4_conv_small.e1200_r224_in1k](http://hf.co/timm/mobilenetv4_conv_small.e1200_r224_in1k)                       | 74.292 | 92.116 | 3.77        | 256      |
 | [mobilenetv4_conv_small.e2400_r224_in1k](http://hf.co/timm/mobilenetv4_conv_small.e2400_r224_in1k)                       | 73.756 | 91.422 | 3.77        | 224      |
+| [resnetv2_18.ra4_e3600_r224_in1k](http://hf.co/timm/resnetv2_18.ra4_e3600_r224_in1k)                                     | 73.578 | 91.352 | 11.69       | 224      |
 | [mobilenetv4_conv_small.e1200_r224_in1k](http://hf.co/timm/mobilenetv4_conv_small.e1200_r224_in1k)                       | 73.454 | 91.34  | 3.77        | 224      |
+| [mobilenetv4_conv_small_050.e3000_r224_in1k](http://hf.co/timm/mobilenetv4_conv_small_050.e3000_r224_in1k)               | 65.810 | 86.424 | 2.24        | 256      |
+| [mobilenetv4_conv_small_050.e3000_r224_in1k](http://hf.co/timm/mobilenetv4_conv_small_050.e3000_r224_in1k)               | 64.762 | 85.514 | 2.24        | 224      |
 
 ## Citation
 ```bibtex
@@ -187,6 +202,13 @@ output = model.forward_head(output, pre_logits=True)
 }
 ```
 ```bibtex
+@inproceedings{wightman2021resnet,
+  title={ResNet strikes back: An improved training procedure in timm},
+  author={Wightman, Ross and Touvron, Hugo and Jegou, Herve},
+  booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future}
+}
+```
+```bibtex
 @article{He2015,
   author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
   title = {Deep Residual Learning for Image Recognition},