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https://github.com/megvii-research/RevCol

Official Code of Paper "Reversible Column Networks" "RevColv2"
https://github.com/megvii-research/RevCol

cnn computer-vision iclr2023 mae pytorch transformer vit

Last synced: about 2 months ago
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Official Code of Paper "Reversible Column Networks" "RevColv2"

Host: GitHub
URL: https://github.com/megvii-research/RevCol
Owner: megvii-research
License: apache-2.0
Created: 2022-12-22T01:54:56.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2023-09-06T03:46:38.000Z (over 1 year ago)
Last Synced: 2024-08-01T03:41:38.299Z (4 months ago)
Topics: cnn, computer-vision, iclr2023, mae, pytorch, transformer, vit
Language: Python
Homepage:
Size: 541 KB
Stars: 247
Watchers: 12
Forks: 10
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-llm-and-aigc - RevCol - research/RevCol?style=social"/> : "Reversible Column Networks". (**[arXiv 2023](https://arxiv.org/abs/2212.11696)**). (Summary)
awesome-llm-and-aigc - RevCol - research/RevCol?style=social"/> : "Reversible Column Networks". (**[arXiv 2023](https://arxiv.org/abs/2212.11696)**). (Summary)

README

        # 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) 

### [RevColV2: Exploring Disentangled Representations in Masked Image Modeling](https://arxiv.org/abs/2309.01005)

[Qi Han](https://hanqer.github.io), [Yuxuan Cai](https://nightsnack.github.io), [Xiangyu Zhang](https://scholar.google.com/citations?user=yuB-cfoAAAAJ) \

[MEGVII Technology](https://en.megvii.com)\

[\[arxiv\]](https://arxiv.org/abs/2309.01005) 

## Updates

**9/06/2023***\

RevColv2 will be released soon!

**3/15/2023***\

RevCol Huge checkpoint for segmentation released! Add visualization tools.

**3/9/2023***\

Detection, Segmentation Code and Model Weights Released.

***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

- [x] Cascade Mask R-CNN COCO Object Detection Code & Model Weights

- [x] 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.







## 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^\* | 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^\* | 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^\* | 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^\* | 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)|

| RevCol-H^\*+  | Megdata-168M | pretrain 224 / finetune 640 | 2.1B | 2537 | 90.0 |[huggingface](https://huggingface.co/LarryTsai/RevCol/blob/main/revcol_models/classification/revcol_huge_megdata.pth)|[huggingface](https://huggingface.co/LarryTsai/RevCol/blob/main/revcol_models/classification/revcol_huge_megdata_in1k.pth)|

[+]: Note that we use a slightly different model on RevCol-H with one more branch from the bottom level to the top one. Later experiments prove that this connection is unnecessary, however, consider RevCol-H's training cost, we do not retrain it.

## 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 [email protected].

## 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}

}

```