mirror of https://github.com/JDAI-CV/DCL.git
59 lines
1.7 KiB
Markdown
59 lines
1.7 KiB
Markdown
|
# Destruction and Construction Learning for Fine-grained Image Recognition
|
||
|
|
|
||
|
|
By Yue Chen, Yalong Bai, Wei Zhang, Tao Mei
|
||
|
|
|
||
|
|
### Introduction
|
||
|
|
|
||
|
|
This code is relative to the [DCL](https://arxiv.org/), which is accepted on CVPR 2019.
|
||
|
|
|
||
|
|
This DCL code in this repo is written based on Pytorch 0.4.0.
|
||
|
|
|
||
|
|
This code has been tested on Ubuntu 16.04.3 LTS with Python 3.6.5 and CUDA 9.0.
|
||
|
|
|
||
|
|
Yuo can use this public docker image as the test environment:
|
||
|
|
|
||
|
|
```shell
|
||
|
|
docker pull pytorch/pytorch:0.4-cuda9-cudnn7-devel
|
||
|
|
```
|
||
|
|
|
||
|
|
### Citing DCL
|
||
|
|
|
||
|
|
If you find this repo useful in your research, please consider citing:
|
||
|
|
|
||
|
|
@article{chen2019dcl,
|
||
|
|
title={Destruction and Construction Learning for Fine-grained Image Recognition},
|
||
|
|
author={Chen Yue and Bai, Yalong and Zhang Wei and Mei Tao},
|
||
|
|
journal={arXiv preprint arXiv:},
|
||
|
|
year={2019}
|
||
|
|
}
|
||
|
|
|
||
|
|
### Requirements
|
||
|
|
|
||
|
|
0. Pytorch 0.4.0
|
||
|
|
|
||
|
|
0. Numpy, Pillow, Pandas
|
||
|
|
|
||
|
|
0. GPU: P40, etc. (May have bugs on the latest V100 GPU)
|
||
|
|
|
||
|
|
### Datasets Prepare
|
||
|
|
|
||
|
|
0. Download CUB-200-2011 dataset form [Caltech-UCSD Birds-200-2011](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html)
|
||
|
|
|
||
|
|
0. Unzip the dataset file under the folder 'datasets'
|
||
|
|
|
||
|
|
0. Run ./datasets/CUB_pre.py to generate annotation files 'train.txt', 'test.txt' and image folder 'all' for CUB-200-2011 dataset
|
||
|
|
|
||
|
|
### Testing Demo
|
||
|
|
|
||
|
|
0. Download `CUB_model.pth` from [Google Drive](https://drive.google.com/file/d/1xWMOi5hADm1xMUl5dDLeP6cfjZit6nQi/view?usp=sharing).
|
||
|
|
|
||
|
|
0. Run `CUB_test.py`
|
||
|
|
|
||
|
|
### Training on CUB-200-2011
|
||
|
|
|
||
|
|
0. Run `train.py` to train and test the CUB-200-2011 datasets. Wait about half day for training and testing.
|
||
|
|
|
||
|
|
0. Hopefully it would give the evaluation results around ~87.8% acc after running.
|
||
|
|
|
||
|
|
**Support for other datasets will be updated later**
|