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«DCL»复现了论文Destruction and Construction Learning for Fine-Grained Image Recognition
更详细的训练数据可以查看：
DCL设计了新的细粒度分类框架，通过联合训练分类网络和解构模块（区域融合机制和对抗学习网络）以及重构模块（区域对齐网络），实现了更好的性能增益，同时在推理时没有计算开销
当前实现基于 JDAI-CV/DCL。
$ pip install -r requirements.txt
- 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
- zhujian - Initial work - zjykzj
@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}
}
欢迎任何人的参与！打开issue或提交合并请求。
注意:
GIT提交，请遵守Conventional Commits规范- 语义版本化，请遵守Semantic Versioning 2.0.0规范
README编写，请遵守standard-readme规范
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