mirror of https://github.com/JDAI-CV/DCL.git
140 lines
4.3 KiB
Markdown
140 lines
4.3 KiB
Markdown
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# Destruction and Construction Learning for Fine-grained Image Recognition
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By Yue Chen, Yalong Bai, Wei Zhang, Tao Mei
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Speical thanks to [Yuanzhi Liang](https://github.com/akira-l) for code refactoring.
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## UPDATE Jun. 21
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Our solution for the FGVC Challenge 2019 (The Sixth Workshop on Fine-Grained Visual Categorization in CVPR 2019) is updated!
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With ensemble of several DCL based classification models, we won:
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- **First Place** in [iMaterialist Challenge on Product Recognition](https://www.kaggle.com/c/imaterialist-product-2019/leaderboard)
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- **First Place** in [Fieldguide Challenge: Moths & Butterflies](https://www.kaggle.com/c/fieldguide-challenge-moths-and-butterflies/leaderboard)
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- **Second Place** in [iFood - 2019 at FGVC6](https://www.kaggle.com/c/ifood-2019-fgvc6/leaderboard)
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## Introduction
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This project is a DCL pytorch implementation of [*Destruction and Construction Learning for Fine-grained Image Recognition*](http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Destruction_and_Construction_Learning_for_Fine-Grained_Image_Recognition_CVPR_2019_paper.html), CVPR2019.
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## Requirements
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1. Python 3.6
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2. Pytorch 0.4.0 or 0.4.1
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3. CUDA 8.0 or higher
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For docker environment:
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```shell
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docker: pull pytorch/pytorch:0.4-cuda9-cudnn7-devel
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```
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For conda environment:
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```shell
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conda create --name DCL file conda_list.txt
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```
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For more backbone supports in DCL, please check [pretrainmodels](https://github.com/Cadene/pretrained-models.pytorch) and install:
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```shell
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pip install pretrainedmodels
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```
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## Datasets Prepare
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1. Download correspond dataset to folder 'datasets'
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2. Data organization: eg. CUB
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All the image data are in './datasets/CUB/data/'
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e.g. './datasets/CUB/data/*.jpg'
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The annotation files are in './datasets/CUB/anno/'
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e.g. './dataset/CUB/data/train.txt'
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In annotations:
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```shell
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name_of_image.jpg label_num\n
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```
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e.g. for CUB in repository:
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```shell
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Black_Footed_Albatross_0009_34.jpg 0
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Black_Footed_Albatross_0014_89.jpg 0
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Laysan_Albatross_0044_784.jpg 1
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Sooty_Albatross_0021_796339.jpg 2
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...
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```
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Some examples of datasets like CUB, Stanford Car, etc. are already given in our repository. You can use DCL to your datasets by simply converting annotations to train.txt/val.txt/test.txt and modify the class number in `config.py` as in line67: numcls=200.
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## Training
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Run `train.py` to train DCL.
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For training CUB / STCAR / AIR from scratch
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```shell
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python train.py --data CUB --epoch 360 --backbone resnet50 \
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--tb 16 --tnw 16 --vb 512 --vnw 16 \
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--lr 0.008 --lr_step 60 \
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--cls_lr_ratio 10 --start_epoch 0 \
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--detail training_descibe --size 512 \
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--crop 448 --cls_mul --swap_num 7 7
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```
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For training CUB / STCAR / AIR from trained checkpoint
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```shell
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python train.py --data CUB --epoch 360 --backbone resnet50 \
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--tb 16 --tnw 16 --vb 512 --vnw 16 \
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--lr 0.008 --lr_step 60 \
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--cls_lr_ratio 10 --start_epoch $LAST_EPOCH \
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--detail training_descibe4checkpoint --size 512 \
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--crop 448 --cls_mul --swap_num 7 7
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```
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For training FGVC product datasets from scratch
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```shell
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python train.py --data product --epoch 60 --backbone senet154 \
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--tb 96 --tnw 32 --vb 512 --vnw 32 \
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--lr 0.01 --lr_step 12 \
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--cls_lr_ratio 10 --start_epoch 0 \
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--detail training_descibe --size 512 \
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--crop 448 --cls_2 --swap_num 7 7
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```
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For training FGVC datasets from trained checkpoint
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```shell
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python train.py --data product --epoch 60 --backbone senet154 \
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--tb 96 --tnw 32 --vb 512 --vnw 32 \
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--lr 0.01 --lr_step 12 \
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--cls_lr_ratio 10 --start_epoch $LAST_EPOCH \
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--detail training_descibe4checkpoint --size 512 \
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--crop 448 --cls_2 --swap_num 7 7
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```
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## Citation
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Please cite our CVPR19 paper if you use DCL in your work:
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```
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@InProceedings{Chen_2019_CVPR,
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author = {Chen, Yue and Bai, Yalong and Zhang, Wei and Mei, Tao},
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title = {Destruction and Construction Learning for Fine-Grained Image Recognition},
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booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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month = {June},
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year = {2019}
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}
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```
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