Reversible Column Networks

This repo is the official implementation of:

Reversible Column Networks

Yuxuan Cai, Yizhuang Zhou, Qi Han, Jianjian Sun, Xiangwen Kong, Jun Li, Xiangyu Zhang
MEGVII Technology
International Conference on Learning Representations (ICLR) 2023
[arxiv]

RevColV2: Exploring Disentangled Representations in Masked Image Modeling

Qi Han, Yuxuan Cai, Xiangyu Zhang
MEGVII Technology
[arxiv]

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

ImageNet-1K and 22k Training Code
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.

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/github	-
RevCol-S	ImageNet-1K	224x224	60M	9.0G	83.5	baidu/github	-
RevCol-B	ImageNet-1K	224x224	138M	16.6G	84.1	baidu/github	-
RevCol-B^*	ImageNet-22K	224x224	138M	16.6G	85.6	baidu/github	baidu/github
RevCol-B^*	ImageNet-22K	384x384	138M	48.9G	86.7	baidu/github	baidu/github
RevCol-L^*	ImageNet-22K	224x224	273M	39G	86.6	baidu/github	baidu/github
RevCol-L^*	ImageNet-22K	384x384	273M	116G	87.6	baidu/github	baidu/github
RevCol-H^*+	Megdata-168M	pretrain 224 / finetune 640	2.1B	2537	90.0	huggingface	huggingface

[+]: 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 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:

License

RevCol is released under the Apache 2.0 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}
}

megvii-research/RevCol

Reversible Column Networks