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+
+# ResMLP: Feedforward networks for image classification with data-efficient training
+
+This repository contains PyTorch evaluation code, training code and pretrained models for the following projects:
+* [DeiT](README.md) (Data-Efficient Image Transformers) 
+* [CaiT](README_cait.md) (Going deeper with Image Transformers)
+* [ResMLP](README_resmlp.md) (ResMLP: Feedforward networks for image classification with data-efficient training)
+
+ResMLP obtain good performance given its simplicity:
+
+<p align="center">
+  <img width="900"  src=".github/resmlp.png">
+</p>
+
+For details see [ResMLP: Feedforward networks for image classification with data-efficient training](https://arxiv.org/abs/2105.03404) by Hugo Touvron, Piotr Bojanowski, Mathilde Caron, Matthieu Cord, Alaaeldin El-Nouby, Edouard Grave, Armand Joulin, Gabriel Synnaeve, Jakob Verbeek and Hervé Jégou.
+
+If you use this code for a paper please cite:
+
+```
+@article{touvron2021resmlp,
+  title={ResMLP: Feedforward networks for image classification with data-efficient training},
+  author={Hugo Touvron and Piotr Bojanowski and Mathilde Caron and M. Cord and Alaaeldin El-Nouby and Edouard Grave and Armand Joulin and Gabriel Synnaeve and Jakob Verbeek and Herv'e J'egou},
+  journal={arXiv preprint arXiv:2105.03404},
+  year={2021},
+}
+```
+
+# Model Zoo
+
+We provide baseline ResMLP models pretrained on ImageNet1k 2012, using the distilled version of our method:
+
+| name | acc@1 | res | FLOPs| #params | url |
+| --- | --- | --- | --- | --- | --- | 
+| ResMLP-S12 | 77.8 | 224 |3B| 15M| [model](https://dl.fbaipublicfiles.com/deit/resmlp_12_dist.pth) |
+| ResMLP-S24| 80.8 | 224 |  6B |30M | [model](https://dl.fbaipublicfiles.com/deit/resmlp_24_dist.pth) |
+| ResMLP-S36 | 81.1 | 224 |  23B |116M | [model](https://dl.fbaipublicfiles.com/deit/resmlp_36_dist.pth) |
+| ResMLP-B24 |83.6 | 224 |  100B |129M | [model](https://dl.fbaipublicfiles.com/deit/resmlpB_24_dist.pth) |
+
+Model pretrained on ImageNet-22k with finetuning on ImageNet1k 2012:
+
+| name | acc@1 | res | FLOPs| #params | url |
+| --- | --- | --- | --- | --- | --- | 
+| ResMLP-B24 |84.4 | 224 |  100B |129M | [model](https://dl.fbaipublicfiles.com/deit/resmlpB_24_22k.pth) |
+
+Models pretrained with DINO without finetuning:
+
+| name | acc@1 (knn)| res | FLOPs| #params | url |
+| --- | --- | --- | --- | --- | --- | 
+| ResMLP-S12 | 62.6 | 224 |3B| 15M| [model](https://dl.fbaipublicfiles.com/deit/resmlp_12_dino.pth) |
+| ResMLP-S24| 69.4 | 224 |  6B |30M | [model](https://dl.fbaipublicfiles.com/deit/resmlp_24_dino.pth) |
+
+The models are also available via torch hub.
+Before using it, make sure you have the pytorch-image-models package [`timm==0.3.2`](https://github.com/rwightman/pytorch-image-models) by [Ross Wightman](https://github.com/rwightman) installed. 
+
+# Evaluation transforms
+
+ResMLP employs a slightly different pre-processing, in particular a crop-ratio of 0.9 at test time. To reproduce the results of our paper please use the following pre-processing:
+
+```
+def get_test_transforms(input_size):
+    mean, std = [0.485, 0.456, 0.406],[0.229, 0.224, 0.225]    
+    transformations = {}
+    Rs_size=int(input_size/0.9)
+    transformations= transforms.Compose(
+        [transforms.Resize(Rs_size, interpolation=3),
+         transforms.CenterCrop(input_size),
+         transforms.ToTensor(),
+         transforms.Normalize(mean, std)])
+    return transformations
+ ```  
+
+# License
+This repository is released under the Apache 2.0 license as found in the [LICENSE](LICENSE) file.
+
+# Contributing
+We actively welcome your pull requests! Please see [CONTRIBUTING.md](.github/CONTRIBUTING.md) and [CODE_OF_CONDUCT.md](.github/CODE_OF_CONDUCT.md) for more info.