https://github.com/XiaohanYu-GU/MaskCOV

Datasets and Code for MaskCOV (accepted in Pattern Recognition)
https://github.com/XiaohanYu-GU/MaskCOV

categorization ultra-fine-grained visual

Last synced: 2 months ago
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Datasets and Code for MaskCOV (accepted in Pattern Recognition)

• Host: GitHub
• URL: https://github.com/XiaohanYu-GU/MaskCOV
• Owner: XiaohanYu-GU
• Created: 2021-05-26T02:45:00.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-10-28T07:14:11.000Z (about 3 years ago)
• Last Synced: 2024-08-01T15:25:20.565Z (5 months ago)
• Topics: categorization, ultra-fine-grained, visual
• Language: Python
• Homepage:
• Size: 46.9 KB
• Stars: 7
• Watchers: 2
• Forks: 4
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        

# MaskCOV

![image](https://user-images.githubusercontent.com/9549469/119601776-36f89a80-be2d-11eb-8535-effb67a1f9e3.png)

## Basic information

This work is published in Pattern Recognition. Please cite the following paper should you consider to use this code.

* Xiaohan Yu, Yang Zhao, Yongsheng Gao, Shengwu Xiong (2021). MaskCOV: A Random Mask Covariance Network for Ultra-Fine-Grained Visual Categorization. In Pattern Recognition.

@article{yu2021maskcov,

  title={MaskCOV: A Random Mask Covariance Network for Ultra-Fine-Grained Visual Categorization},

  author={Yu, Xiaohan and Zhao, Yang and Gao, Yongsheng and Xiong, Shengwu},

  journal={Pattern Recognition},

  pages={108067},

  year={2021},

  publisher={Elsevier}

}

## Source Download

Please find our code in the folder PR_MaskCOV. The ultra-fine-grained image dataset, UFG, used in this paper can be downloaded via "https://github.com/XiaohanYu-GU/Ultra-FGVC".

### How to use

install pytorch 1.6.0, python 3.7, cuda 10.1, cudnn7.6.3 and any necessary python package that is required.

Use the following order to run the training code in a default setting.

"sh main.sh"

Or revise the hyper-parameters (batch size, learning rate) in config.py if needed and then run "sh main.sh".

### Note

For Cotton80 subset, the batch size is recommended to be 8. For the remaining subsets, the batch size is recommended to be 16.

### Acknowledgement

The code is revised based on source code provided by DCL (see "https://github.com/JDAI-CV/DCL"). We sincerely thank their contribution.

## Author contact info

*Xiaohan Yu*, *[email protected]*