microsoft / Focal-Transformer

[NeurIPS 2021 Spotlight] Official code for "Focal Self-attention for Local-Global Interactions in Vision Transformers"

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Focal Transformer [NeurIPS 2021 Spotlight]

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This is the official implementation of our Focal Transformer -- "Focal Self-attention for Local-Global Interactions in Vision Transformers", by Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Xiyang Dai, Bin Xiao, Lu Yuan and Jianfeng Gao.

Introduction

focal-transformer-teaser

Our Focal Transfomer introduced a new self-attention mechanism called focal self-attention for vision transformers. In this new mechanism, each token attends the closest surrounding tokens at fine granularity but the tokens far away at coarse granularity, and thus can capture both short- and long-range visual dependencies efficiently and effectively.

With our Focal Transformers, we achieved superior performance over the state-of-the-art vision Transformers on a range of public benchmarks. In particular, our Focal Transformer models with a moderate size of 51.1M and a larger size of 89.8M achieve 83.6 and 84.0 Top-1 accuracy, respectively, on ImageNet classification at 224x224 resolution. Using Focal Transformers as the backbones, we obtain consistent and substantial improvements over the current state-of-the-art methods for 6 different object detection methods trained with standard 1x and 3x schedules. Our largest Focal Transformer yields 58.7/58.9 box mAPs and 50.9/51.3 mask mAPs on COCO mini-val/test-dev, and 55.4 mIoU on ADE20K for semantic segmentation.

🎞️ Video by The AI Epiphany

Next Generation Architecture

We had developed FocalNet, a next generation of architecture built based on the focal mechanism. It is much faster and more effective. Check it out at: https://github.com/microsoft/FocalNet!

Faster Focal Transformer

As you may notice, though the theoritical GFLOPs of our Focal Transformer is comparable to prior works, its wall-clock efficiency lags behind. Therefore, we are releasing a faster version of Focal Transformer, which discard all the rolling and unfolding operations used in our first version.

Model Pretrain Use Conv Resolution acc@1 acc@5 #params FLOPs Throughput (imgs/s) Checkpoint Config
Focal-T IN-1K No 224 82.2 95.9 28.9M 4.9G 319 download yaml
Focal-fast-T IN-1K Yes 224 82.4 96.0 30.2M 5.0G 483 download yaml
Focal-S IN-1K No 224 83.6 96.2 51.1M 9.4G 192 download yaml
Focal-fast-S IN-1K Yes 224 83.6 96.4 51.5M 9.4G 293 download yaml
Focal-B IN-1K No 224 84.0 96.5 89.8M 16.4G 138 download yaml
Focal-fast-B IN-1K Yes 224 84.0 96.6 91.2M 16.4G 203 download yaml

Benchmarking

Image Classification Throughput with Image Resolution

Model Top-1 Acc. GLOPs (224x224) 224x224 448x448 896 x 896
DeiT-Small/16 79.8 4.6 939 101 20
PVT-Small 79.8 3.8 794 172 31
CvT-13 81.6 4.5 746 125 14
ViL-Small 82.0 5.1 397 87 17
Swin-Tiny 81.2 4.5 760 189 48
Focal-Tiny 82.2 4.9 319 105 27
PVT-Medium 81.2 6.7 517 111 20
CvT-21 82.5 7.1 480 85 10
ViL-Medium 83.3 9.1 251 53 8
Swin-Small 83.1 8.7 435 111 28
Focal-Small 83.6 9.4 192 63 17
ViT-Base/16 77.9 17.6 291 57 8
Deit-Base/16 81.8 17.6 291 57 8
PVT-Large 81.7 9.8 352 77 14
ViL-Base 83.2 13.4 145 35 5
Swin-Base 83.4 15.4 291 70 17
Focal-Base 84.0 16.4 138 44 11

Image Classification on ImageNet-1K

Model Pretrain Use Conv Resolution acc@1 acc@5 #params FLOPs Checkpoint Config
Focal-T IN-1K No 224 82.2 95.9 28.9M 4.9G download yaml
Focal-T IN-1K Yes 224 82.7 96.1 30.8M 5.2G download yaml
Focal-S IN-1K No 224 83.6 96.2 51.1M 9.4G download yaml
Focal-S IN-1K Yes 224 83.8 96.5 53.1M 9.7G download yaml
Focal-B IN-1K No 224 84.0 96.5 89.8M 16.4G download yaml
Focal-B IN-1K Yes 224 84.2 97.1 93.3M 16.8G download yaml

Object Detection and Instance Segmentation on COCO

Backbone Pretrain Lr Schd #params FLOPs box mAP mask mAP
Focal-T ImageNet-1K 1x 49M 291G 44.8 41.0
Focal-T ImageNet-1K 3x 49M 291G 47.2 42.7
Focal-S ImageNet-1K 1x 71M 401G 47.4 42.8
Focal-S ImageNet-1K 3x 71M 401G 48.8 43.8
Focal-B ImageNet-1K 1x 110M 533G 47.8 43.2
Focal-B ImageNet-1K 3x 110M 533G 49.0 43.7
Backbone Pretrain Lr Schd #params FLOPs box mAP
Focal-T ImageNet-1K 1x 39M 265G 43.7
Focal-T ImageNet-1K 3x 39M 265G 45.5
Focal-S ImageNet-1K 1x 62M 367G 45.6
Focal-S ImageNet-1K 3x 62M 367G 47.3
Focal-B ImageNet-1K 1x 101M 514G 46.3
Focal-B ImageNet-1K 3x 101M 514G 46.9

Other detection methods

Backbone Pretrain Method Lr Schd #params FLOPs box mAP
Focal-T ImageNet-1K Cascade Mask R-CNN 3x 87M 770G 51.5
Focal-T ImageNet-1K ATSS 3x 37M 239G 49.5
Focal-T ImageNet-1K RepPointsV2 3x 45M 491G 51.2
Focal-T ImageNet-1K Sparse R-CNN 3x 111M 196G 49.0

Semantic Segmentation on ADE20K

Backbone Pretrain Method Resolution Iters #params FLOPs mIoU mIoU (MS)
Focal-T ImageNet-1K UPerNet 512x512 160k 62M 998G 45.8 47.0
Focal-S ImageNet-1K UPerNet 512x512 160k 85M 1130G 48.0 50.0
Focal-B ImageNet-1K UPerNet 512x512 160k 126M 1354G 49.0 50.5
Focal-L ImageNet-22K UPerNet 640x640 160k 240M 3376G 54.0 55.4

Getting Started

Citation

If you find this repo useful to your project, please consider to cite it with following bib:

@misc{yang2021focal,
    title={Focal Self-attention for Local-Global Interactions in Vision Transformers}, 
    author={Jianwei Yang and Chunyuan Li and Pengchuan Zhang and Xiyang Dai and Bin Xiao and Lu Yuan and Jianfeng Gao},
    year={2021},
    eprint={2107.00641},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Acknowledgement

Our codebase is built based on Swin-Transformer. We thank the authors for the nicely organized code!

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

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[NeurIPS 2021 Spotlight] Official code for "Focal Self-attention for Local-Global Interactions in Vision Transformers"

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