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license: apache-2.0 |
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# Reversible Column Networks |
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This repo is the official implementation of: |
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### [Reversible Column Networks](https://arxiv.org/abs/2212.11696) |
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[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) \ |
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[MEGVII Technology](https://en.megvii.com)\ |
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International Conference on Learning Representations (ICLR) 2023\ |
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[\[arxiv\]](https://arxiv.org/abs/2212.11696) |
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## Updates |
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**3/15/2023***\ |
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RevCol Huge checkpoint for segmentation released! Add visualization tools. |
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**3/9/2023***\ |
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Detection, Segmentation Code and Model Weights Released. |
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***2/10/2023***\ |
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RevCol model weights released. |
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***1/21/2023***\ |
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RevCol was accepted by ICLR 2023! |
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***12/23/2022***\ |
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Initial commits: codes for ImageNet-1k and ImageNet-22k classification are released. |
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## To Do List |
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- [x] ImageNet-1K and 22k Training Code |
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- [x] ImageNet-1K and 22k Model Weights |
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- [x] Cascade Mask R-CNN COCO Object Detection Code & Model Weights |
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- [x] ADE20k Semantic Segmentation Code & Model Weights |
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## Introduction |
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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. |
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## Main Results on ImageNet with Pre-trained Models |
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| name | pretrain | resolution | #params |FLOPs | acc@1 | pretrained model | finetuned model | |
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|:---------------------:| :---: | :---: | :---: | :---: | :---: | :---: | :---: | |
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| 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) | - | |
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| 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) | - | |
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| 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) | - | |
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| 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)| |
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| 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)| |
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| 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)| |
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| 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)| |
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| RevCol-H<sup>\*+</sup> | 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)| |
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[+]: 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. |
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## Getting Started |
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Please refer to [INSTRUCTIONS.md](INSTRUCTIONS.md) for setting up, training and evaluation details. |
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## Acknowledgement |
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This repo was inspired by several open source projects. We are grateful for these excellent projects and list them as follows: |
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- [timm](https://github.com/rwightman/pytorch-image-models) |
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- [Swin Transformer](https://github.com/microsoft/Swin-Transformer) |
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- [ConvNeXt](https://github.com/facebookresearch/ConvNeXt) |
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- [beit](https://github.com/microsoft/unilm/tree/master/beit) |
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## License |
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RevCol is released under the [Apache 2.0 license](LICENSE). |
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## Contact Us |
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If you have any questions about this repo or the original paper, please contact Yuxuan at [email protected]. |
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## Citation |
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``` |
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@inproceedings{cai2022reversible, |
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title={Reversible Column Networks}, |
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author={Cai, Yuxuan and Zhou, Yizhuang and Han, Qi and Sun, Jianjian and Kong, Xiangwen and Li, Jun and Zhang, Xiangyu}, |
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booktitle={International Conference on Learning Representations}, |
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year={2023}, |
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url={https://openreview.net/forum?id=Oc2vlWU0jFY} |
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} |
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``` |
