https://github.com/christophreich1996/uniformer

[ICLR2022] official implementation of UniFormer
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[ICLR2022] official implementation of UniFormer
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# UniFormer

This repo is the official implementation of  ["UniFormer: Unifying Convolution and Self-attention for Visual Recognition"](https://arxiv.org/abs/2201.09450) and  ["UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning"](https://arxiv.org/abs/2201.04676). 

It currently includes code and models for the following tasks:

- [x] [Image Classification](image_classification)
- [x] [Video Classification](video_classification)
- [x] [Object Detection](object_detection)
- [x] [Semantic Segmentation](semantic_segmentation)
- [x] [Pose Estimation](pose_estimation)



## 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](image_classification).

## Updates

***05/24/2022***

1. Some bugs for video recognition have been fixed in [Nightcrawler](https://github.com/Andy1621/Nightcrawler). We successfully adapt UniFormer for [extreme dark video classification](https://codalab.lisn.upsaclay.fr/competitions/1112#results)! 
2. More demos for [Detection](https://huggingface.co/spaces/Andy1621/uniformer_image_detection) and [Segmentation](https://huggingface.co/spaces/Andy1621/uniformer_image_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](https://huggingface.co/Sense-X).

***02/3/2022***

Integrated into [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Have fun!
- [Image Demo](https://huggingface.co/spaces/Sense-X/uniformer_image_demo)
- [Video Demo](https://huggingface.co/spaces/Sense-X/uniformer_video_demo)

***01/21/2022***

[UniFormer for video](https://openreview.net/forum?id=nBU_u6DLvoK) is accepted by **ICLR2022 (8868, Top 3%**)!

***01/19/2022***

1. Pretrained models on ImageNet-1K with [Token Labeling](https://arxiv.org/abs/2104.10858).
2. Large resolution fine-tuning.

***01/18/2022***

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

***01/13/2022***

**\[Initial commits\]:**

1. Pretrained models on ImageNet-1K, Kinetics-400, Kinetics-600, Something-Something V1&V2.

2. The supported code and models for image classification and video classification are provided.

   

## Introduction

**UniFormer** (**Uni**fied trans**Former**) is introduce in [arxiv](https://arxiv.org/abs/2201.04676) (more details can be found in [arxiv](https://arxiv.org/abs/2201.09450)), 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. 

![teaser](figures/framework.png)

## Main results on ImageNet-1K

Please see [image_classification](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-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](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*      |

>  \#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.

## Main results on Something-Something video classification

Please see [video_classification](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 COCO object detection

Please see [object_detection](object_detection) for more details.

### Mask R-CNN

|         Backbone          | Lr Schd | box mAP | mask mAP | #params | FLOPs |
| :--------------- | :----- | :----- | :------ | :----- | :--- |
| 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  |

### 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  |

## Main results on ADE20K semantic segmentation

Please see [semantic_segmentation](semantic_segmentation) for more details.

### Semantic FPN

|         Backbone          | Lr Schd | mIoU | #params | FLOPs |
| :------------------- | :----- | :-- | :----- | :--- |
| 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  |

### 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 |

## Main results on COCO pose estimation

Please see [pose_estimation](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 use the following BibTeX entry for citation.

```latex
@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}
}
```

```latex
@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](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`).
Owner

Name: Christoph Reich
Login: ChristophReich1996
Kind: user
Location: Germany
Company: Technical University of Munich
Website: christophreich1996.github.io
Twitter: ChristophR1996
Repositories: 41
Profile: https://github.com/ChristophReich1996
ELLIS Ph.D. Student @ Technical University of Munich, Technische Universität Darmstadt & University of Oxford | Prev. NEC Labs
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