Table of Contents

Table of Contents
Background
Installation
Usage
Maintainers
Thanks
Contributing
License

Background

Differ with other attention-based or part-based fine-classification methods, DCL adds an Destruction Module (Region Confusion Mechanism and Adversarial Learning Network) and Construction Module (Region Align Network) in training, and only use backbone network in infer. Improve the accuracy of the model without affecting the reasoning speed.

Current project implementation is based on JDAI-CV/DCL.

Installation

$ pip install -r requirements.txt

Usage

Train

$ CUDA_VISIBLE_DEVICES=0,1,2,3 python tools/train.py -cfg=configs/cub/r50_cub_448_e100_sgd_dcl_5x5_g4.yaml

Test

$ CUDA_VISIBLE_DEVICES=0,1,2,3 python tools/test.py -cfg=configs/cub/r50_cub_448_e100_sgd_dcl_5x5_g4.yaml

Maintainers

zhujian - Initial work - zjykzj

Thanks

@InProceedings{Chen_2019_CVPR,
author = {Chen, Yue and Bai, Yalong and Zhang, Wei and Mei, Tao},
title = {Destruction and Construction Learning for Fine-Grained Image Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

Contributing

Anyone's participation is welcome! Open an issue or submit PRs.

Small note:

Git submission specifications should be complied with Conventional Commits
If versioned, please conform to the Semantic Versioning 2.0.0 specification
If editing the README, please conform to the standard-readme specification.

License

Apache License 2.0 © 2021 zjykzj

License

ZJCV/DCL

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