image-classification video-classification object-detection semantic-segmentation pose-estimation

UniFormer

💬 This repo is the official implementation of:

TPAMI2023: UniFormer: Unifying Convolution and Self-attention for Visual Recognition
ICLR2022: UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning

🤖 It currently includes code and models for the following tasks:

🌟 Other popular repos:

UniFormerV2: The first model to achieve 90% top-1 accuracy on Kinetics-400.
Unmasked Teacher: Using only public sources for pre-training in 6 days on 32 A100 GPUs, our scratch-built ViT-L/16 achieves state-of-the-art performances on various video tasks.
Ask-Anything: Ask anything in video and image!

⚠️ Note!!!!!

For downstream tasks:

We forget to freeze BN in backbone, which will further improve the performance.
We have verified that Token Labeling can largely help the downstream tasks. Have a try if you utilize UniFormer for competition or application.
The head_dim of some models are 32, which will lead to large memory cost but little improvement for downstream tasks. Those models with head_dim=64 are released released in image_classification.

🔥 Updates

05/19/2023

The extension version has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 🎉🎉🎉. In revision, we explore the simple yet effective lightweight design: Hourglass UniFormer. Based on that, we propose the efficient UniFormer-XS and UniFormer-XXS:

For image tasks, they surpass MobileViT, PVTv2 and EfficientNet.
For video tasks, they surpass X3D and MoViNet.
Try our 🚀fast demo🚀 on CPU!

11/20/2022

We have released UniFormerV2, which aims to arming the pre-trained ViTs with efficient UniFormer designs. It can save a lot of reaining resources and achieve powerful performance on 8 popular benchmarks. Please have a try! 🎉🎉

10/26/2022

We have provided the code for video visualizations, please see video_classification/vis.

05/24/2022

Some bugs for video recognition have been fixed in Nightcrawler. We successfully adapt UniFormer for extreme dark video classification! 🎉🎉
More demos for Detection and Segmentation are provided. 👏😄

03/6/2022

Some models with head_dim=64 are released, which can save memory cost for downstream tasks.

02/9/2022

Some popular models and demos are updated in hugging face.

02/3/2022

Integrated into using Gradio. Have fun!

01/21/2022

UniFormer for video is accepted by ICLR2022 (8868, Top 3%)!

01/19/2022

Pretrained models on ImageNet-1K with Token Labeling.
Large resolution fine-tuning.

01/18/2022

The supported code and models for COCO object detection.
The supported code and models for ADE20K semantic segmentation.
The supported code and models for COCO pose estimation.

01/13/2022

Pretrained models on ImageNet-1K, Kinetics-400, Kinetics-600, Something-Something V1&V2.
The supported code and models for image classification and video classification are provided.

📖 Introduction

UniFormer (Unified transFormer) is introduce in arxiv (more details can be found in arxiv), which can seamlessly integrate merits of convolution and self-attention in a concise transformer format. We adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation.

Without any extra training data, our UniFormer achieves 86.3 top-1 accuracy on ImageNet-1K classification. With only ImageNet-1K pre-training, it can simply achieve state-of-the-art performance in a broad range of downstream tasks. Our UniFormer obtains 82.9/84.8 top-1 accuracy on Kinetics-400/600, and 60.9/71.2 top-1 accuracy on Something-Something V1/V2 video classification tasks. It also achieves 53.8 box AP and 46.4 mask AP on COCO object detection task, 50.8 mIoU on ADE20K semantic segmentation task, and 77.4 AP on COCO pose estimation task. Moreover, we build an efficient UniFormer with a concise hourglass design of token shrinking and recovering, which achieves 2-4× higher throughput than the recent lightweight models.

General Framework

Efficient Framework

Different Downstream Tasks

Main results on ImageNet-1K

Please see image_classification for more details.

More models with large resolution and token labeling will be released soon.

Model	Pretrain	Resolution	Top-1	#Param.	FLOPs
UniFormer-XXS	ImageNet-1K	128x128	76.8	10.2M	0.43G
UniFormer-XXS	ImageNet-1K	160x160	79.1	10.2M	0.67G
UniFormer-XXS	ImageNet-1K	192x192	79.9	10.2M	0.96G
UniFormer-XXS	ImageNet-1K	224x224	80.6	10.2M	1.3G
UniFormer-XS	ImageNet-1K	192x192	81.5	16.5M	1.4G
UniFormer-XS	ImageNet-1K	224x224	82.0	16.5M	2.0G
UniFormer-S	ImageNet-1K	224x224	82.9	22M	3.6G
UniFormer-S†	ImageNet-1K	224x224	83.4	24M	4.2G
UniFormer-B	ImageNet-1K	224x224	83.9	50M	8.3G
UniFormer-S+TL	ImageNet-1K	224x224	83.4	22M	3.6G
UniFormer-S†+TL	ImageNet-1K	224x224	83.9	24M	4.2G
UniFormer-B+TL	ImageNet-1K	224x224	85.1	50M	8.3G
UniFormer-L+TL	ImageNet-1K	224x224	85.6	100M	12.6G
UniFormer-S+TL	ImageNet-1K	384x384	84.6	22M	11.9G
UniFormer-S†+TL	ImageNet-1K	384x384	84.9	24M	13.7G
UniFormer-B+TL	ImageNet-1K	384x384	86.0	50M	27.2G
UniFormer-L+TL	ImageNet-1K	384x384	86.3	100M	39.2G

Main results on Kinetics video classification

Please see video_classification for more details.

Model	Pretrain	#Frame	Sampling Stride	FLOPs	K400 Top-1	K600 Top-1
UniFormer-S	ImageNet-1K	16x1x4	4	167G	80.8	82.8
UniFormer-S	ImageNet-1K	16x1x4	8	167G	80.8	82.7
UniFormer-S	ImageNet-1K	32x1x4	4	438G	82.0	-
UniFormer-B	ImageNet-1K	16x1x4	4	387G	82.0	84.0
UniFormer-B	ImageNet-1K	16x1x4	8	387G	81.7	83.4
UniFormer-B	ImageNet-1K	32x1x4	4	1036G	82.9	84.5*

Model	Pretrain	#Frame	Resolution	FLOPs	K400 Top-1
UniFormer-XXS	ImageNet-1K	4x1x1	128	1.0G	63.2
UniFormer-XXS	ImageNet-1K	4x1x1	160	1.6G	65.8
UniFormer-XXS	ImageNet-1K	8x1x1	128	2.0G	68.3
UniFormer-XXS	ImageNet-1K	8x1x1	160	3.3G	71.4
UniFormer-XXS	ImageNet-1K	16x1x1	128	4.2G	73.3
UniFormer-XXS	ImageNet-1K	16x1x1	160	6.9G	75.1
UniFormer-XXS	ImageNet-1K	32x1x1	160	15.4G	77.9
UniFormer-XS	ImageNet-1K	32x1x1	192	34.2G	78.6

#Frame = #input_frame x #crop x #clip

* Since Kinetics-600 is too large to train (>1 month in single node with 8 A100 GPUs), we provide model trained in multi node (around 2 weeks with 32 V100 GPUs), but the result is lower due to the lack of tuning hyperparameters.

* For UniFormer-XS and UniFormer-XXS, we use sparse sampling.

Main results on Something-Something video classification

Please see video_classification for more details.

Model	Pretrain	#Frame	FLOPs	SSV1 Top-1	SSV2 Top-1
UniFormer-S	K400	16x3x1	125G	57.2	67.7
UniFormer-S	K600	16x3x1	125G	57.6	69.4
UniFormer-S	K400	32x3x1	329G	58.8	69.0
UniFormer-S	K600	32x3x1	329G	59.9	70.4
UniFormer-B	K400	16x3x1	290G	59.1	70.4
UniFormer-B	K600	16x3x1	290G	58.8	70.2
UniFormer-B	K400	32x3x1	777G	60.9	71.1
UniFormer-B	K600	32x3x1	777G	61.0	71.2

#Frame = #input_frame x #crop x #clip

Main results on UCF101 and HMDB51 video classification

Please see video_classification for more details.

Model	Pretrain	#Frame	Sampling Stride	FLOPs	UCF101 Top-1	HMDB51 Top-1
UniFormer-S	K400	16x3x5	4	625G	98.3	77.5

#Frame = #input_frame x #crop x #clip

* We only report the results in the first split. As for the results in our paper, we run the model in 3 training/validation splits and average the results.

Main results on COCO object detection

Please see object_detection for more details.

Mask R-CNN

Backbone	Lr Schd	box mAP	mask mAP	#params	FLOPs
UniFormer-XXS	1x	42.8	39.2	29.4M	-
UniFormer-XS	1x	44.6	40.9	35.6M	-
UniFormer-S_h14	1x	45.6	41.6	41M	269G
UniFormer-S_h14	3x+MS	48.2	43.4	41M	269G
UniFormer-B_h14	1x	47.4	43.1	69M	399G
UniFormer-B_h14	3x+MS	50.3	44.8	69M	399G

* The FLOPs are measured at resolution 800×1280.

Cascade Mask R-CNN

Backbone	Lr Schd	box mAP	mask mAP	#params	FLOPs
UniFormer-S_h14	3x+MS	52.1	45.2	79M	747G
UniFormer-B_h14	3x+MS	53.8	46.4	107M	878G

* The FLOPs are measured at resolution 800×1280.

Main results on ADE20K semantic segmentation

Please see semantic_segmentation for more details.

Semantic FPN

Backbone	Lr Schd	mIoU	#params	FLOPs
UniFormer-XXS	80K	42.3	13.5M	-
UniFormer-XS	80K	44.4	19.7M	-
UniFormer-S_h14	80K	46.3	25M	172G
UniFormer-B_h14	80K	47.0	54M	328G
UniFormer-S_w32	80K	45.6	25M	183G
UniFormer-S_h32	80K	46.2	25M	199G
UniFormer-S	80K	46.6	25M	247G
UniFormer-B_w32	80K	47.0	54M	310G
UniFormer-B_h32	80K	47.7	54M	350G
UniFormer-B	80K	48.0	54M	471G

* The FLOPs are measured at resolution 512×2048.

UperNet

Backbone	Lr Schd	mIoU	MS mIoU	#params	FLOPs
UniFormer-S_h14	160K	46.9	48.0	52M	947G
UniFormer-B_h14	160K	48.9	50.0	80M	1085G
UniFormer-S_w32	160K	46.6	48.4	52M	939G
UniFormer-S_h32	160K	47.0	48.5	52M	955G
UniFormer-S	160K	47.6	48.5	52M	1004G
UniFormer-B_w32	160K	49.1	50.6	80M	1066G
UniFormer-B_h32	160K	49.5	50.7	80M	1106G
UniFormer-B	160K	50.0	50.8	80M	1227G

* The FLOPs are measured at resolution 512×2048.

Main results on COCO pose estimation

Please see pose_estimation for more details.

Top-Down

Backbone	Input Size	AP	AP⁵⁰	AP⁷⁵	AR^M	AR^L	AR	FLOPs
UniFormer-S	256x192	74.0	90.3	82.2	66.8	76.7	79.5	4.7G
UniFormer-S	384x288	75.9	90.6	83.4	68.6	79.0	81.4	11.1G
UniFormer-S	448x320	76.2	90.6	83.2	68.6	79.4	81.4	14.8G
UniFormer-B	256x192	75.0	90.6	83.0	67.8	77.7	80.4	9.2G
UniFormer-B	384x288	76.7	90.8	84.0	69.3	79.7	81.4	14.8G
UniFormer-B	448x320	77.4	91.1	84.4	70.2	80.6	82.5	29.6G

⭐ Cite Uniformer

If you find this repository useful, please give us stars and use the following BibTeX entry for citation.

@misc{li2022uniformer,
      title={UniFormer: Unifying Convolution and Self-attention for Visual Recognition}, 
      author={Kunchang Li and Yali Wang and Junhao Zhang and Peng Gao and Guanglu Song and Yu Liu and Hongsheng Li and Yu Qiao},
      year={2022},
      eprint={2201.09450},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{li2022uniformer,
      title={UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning}, 
      author={Kunchang Li and Yali Wang and Peng Gao and Guanglu Song and Yu Liu and Hongsheng Li and Yu Qiao},
      year={2022},
      eprint={2201.04676},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

License

This project is released under the MIT license. Please see the LICENSE file for more information.

Contributors and Contact Information

UniFormer is maintained by Kunchang Li.

For help or issues using UniFormer, please submit a GitHub issue.

For other communications related to UniFormer, please contact Kunchang Li (kc.li@siat.ac.cn).

About

[ICLR2022] official implementation of UniFormer

image-classification video-classification object-detection semantic-segmentation pose-estimation

Apache License 2.0

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