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.

🎞️ 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 get_started_for_image_classification.md to get started for image classification.
• Please follow get_started_for_object_detection.md to get started for object detection.
• Please follow get_started_for_semantic_segmentation.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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