Focal Transformer [NeurIPS 2021 Spotlight]

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

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.

:film_strip: 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

Mask R-CNN

| 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 |

RetinaNet

| 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

• Please follow getstartedforimageclassification.md to get started for image classification.
• Please follow getstartedforobjectdetection.md to get started for object detection.
• Please follow getstartedforsemanticsegmentation.md to get started for semantic segmentation.

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

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