| # Reversible Column Networks | |
| This repo is the official implementation of: | |
| ### [Reversible Column Networks](https://arxiv.org/abs/2212.11696) | |
| [Yuxuan Cai](https://nightsnack.github.io), [Yizhuang Zhou](https://scholar.google.com/citations?user=VRSGDDEAAAAJ), [Qi Han](https://hanqer.github.io), Jianjian Sun, Xiangwen Kong, Jun Li, [Xiangyu Zhang](https://scholar.google.com/citations?user=yuB-cfoAAAAJ) \ | |
| [MEGVII Technology](https://en.megvii.com)\ | |
| International Conference on Learning Representations (ICLR) 2023\ | |
| [\[arxiv\]](https://arxiv.org/abs/2212.11696) | |
| ## Updates | |
| ***2/10/2023***\ | |
| RevCol model weights released. | |
| ***1/21/2023***\ | |
| RevCol was accepted by ICLR 2023! | |
| ***12/23/2022***\ | |
| Initial commits: codes for ImageNet-1k and ImageNet-22k classification are released. | |
| ## To Do List | |
| - [x] ImageNet-1K and 22k Training Code | |
| - [x] ImageNet-1K and 22k Model Weights | |
| - [ ] Cascade Mask R-CNN COCO Object Detection Code & Model Weights | |
| - [ ] ADE20k Semantic Segmentation Code & Model Weights | |
| ## Introduction | |
| RevCol is composed of multiple copies of subnetworks, named columns respectively, between which multi-level reversible connections are employed. RevCol coud serves as a foundation model backbone for various tasks in computer vision including classification, detection and segmentation. | |
| <p align="center"> | |
| <img src="figures/title.png" width=100% height=100% | |
| class="center"> | |
| </p> | |
| ## Main Results on ImageNet with Pre-trained Models | |
| | name | pretrain | resolution | #params |FLOPs | acc@1 | pretrained model | finetuned model | | |
| |:---------------------:| :---: | :---: | :---: | :---: | :---: | :---: | :---: | | |
| | RevCol-T | ImageNet-1K | 224x224 | 30M | 4.5G | 82.2 | [baidu](https://pan.baidu.com/s/1iGsbdmFcDpwviCHaajeUnA?pwd=h4tj)/[github](https://github.com/megvii-research/RevCol/releases/download/checkpoint/revcol_tiny_1k.pth) | - | | |
| | RevCol-S | ImageNet-1K | 224x224 | 60M | 9.0G | 83.5 | [baidu](https://pan.baidu.com/s/1hpHfdFrTZIPB5NTwqDMLag?pwd=mxuk)/[github](https://github.com/megvii-research/RevCol/releases/download/checkpoint/revcol_small_1k.pth) | - | | |
| | RevCol-B | ImageNet-1K | 224x224 | 138M | 16.6G | 84.1 | [baidu](https://pan.baidu.com/s/16XIJ1n8pXPD2cXwnFX6b9w?pwd=j6x9)/[github](https://github.com/megvii-research/RevCol/releases/download/checkpoint/revcol_base_1k.pth) | - | | |
| | RevCol-B<sup>\*</sup> | ImageNet-22K | 224x224 | 138M | 16.6G | 85.6 |[baidu](https://pan.baidu.com/s/1l8zOFifgC8fZtBpHK2ZQHg?pwd=rh58)/[github](https://github.com/megvii-research/RevCol/releases/download/checkpoint/revcol_base_22k.pth)| [baidu](https://pan.baidu.com/s/1HqhDXL6OIQdn1LeM2pewYQ?pwd=1bp3)/[github](https://github.com/megvii-research/RevCol/releases/download/checkpoint/revcol_base_22k_1kft_224.pth)| | |
| | RevCol-B<sup>\*</sup> | ImageNet-22K | 384x384 | 138M | 48.9G | 86.7 |[baidu](https://pan.baidu.com/s/1l8zOFifgC8fZtBpHK2ZQHg?pwd=rh58)/[github](https://github.com/megvii-research/RevCol/releases/download/checkpoint/revcol_base_22k.pth)| [baidu](https://pan.baidu.com/s/18G0zAUygKgu58s2AjCBpsw?pwd=rv86)/[github](https://github.com/megvii-research/RevCol/releases/download/checkpoint/revcol_base_22k_1kft_384.pth)| | |
| | RevCol-L<sup>\*</sup> | ImageNet-22K | 224x224 | 273M | 39G | 86.6 |[baidu](https://pan.baidu.com/s/1ueKqh3lFAAgC-vVU34ChYA?pwd=qv5m)/[github](https://github.com/megvii-research/RevCol/releases/download/checkpoint/revcol_large_22k.pth)| [baidu](https://pan.baidu.com/s/1CsWmcPcwieMzXE8pVmHh7w?pwd=qd9n)/[github](https://github.com/megvii-research/RevCol/releases/download/checkpoint/revcol_large_22k_1kft_224.pth)| | |
| | RevCol-L<sup>\*</sup> | ImageNet-22K | 384x384 | 273M | 116G | 87.6 |[baidu](https://pan.baidu.com/s/1ueKqh3lFAAgC-vVU34ChYA?pwd=qv5m)/[github](https://github.com/megvii-research/RevCol/releases/download/checkpoint/revcol_large_22k.pth)| [baidu](https://pan.baidu.com/s/1VmCE3W3Xw6-Lo4rWrj9Xzg?pwd=x69r)/[github](https://github.com/megvii-research/RevCol/releases/download/checkpoint/revcol_large_22k_1kft_384.pth)| | |
| ## Getting Started | |
| Please refer to [INSTRUCTIONS.md](INSTRUCTIONS.md) for setting up, training and evaluation details. | |
| ## Acknowledgement | |
| This repo was inspired by several open source projects. We are grateful for these excellent projects and list them as follows: | |
| - [timm](https://github.com/rwightman/pytorch-image-models) | |
| - [Swin Transformer](https://github.com/microsoft/Swin-Transformer) | |
| - [ConvNeXt](https://github.com/facebookresearch/ConvNeXt) | |
| - [beit](https://github.com/microsoft/unilm/tree/master/beit) | |
| ## License | |
| RevCol is released under the [Apache 2.0 license](LICENSE). | |
| ## Contact Us | |
| If you have any questions about this repo or the original paper, please contact Yuxuan at caiyuxuan@megvii.com. | |
| ## Citation | |
| ``` | |
| @inproceedings{cai2022reversible, | |
| title={Reversible Column Networks}, | |
| author={Cai, Yuxuan and Zhou, Yizhuang and Han, Qi and Sun, Jianjian and Kong, Xiangwen and Li, Jun and Zhang, Xiangyu}, | |
| booktitle={International Conference on Learning Representations}, | |
| year={2023}, | |
| url={https://openreview.net/forum?id=Oc2vlWU0jFY} | |
| } | |
| ``` | |