By Yue Chen, Yalong Bai, Wei Zhang, Tao Mei
Speical thanks to Yuanzhi Liang for code refactoring.
Our solution for the FGVC Challenge 2019 (The Sixth Workshop on Fine-Grained Visual Categorization in CVPR 2019) is updated!
With ensemble of several DCL based classification models, we won:
- First Place in iMaterialist Challenge on Product Recognition
- First Place in Fieldguide Challenge: Moths & Butterflies
- Second Place in iFood - 2019 at FGVC6
This project is a DCL pytorch implementation of Destruction and Construction Learning for Fine-grained Image Recognition, CVPR2019.
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Python 3.6
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Pytorch 0.4.0 or 0.4.1
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CUDA 8.0 or higher
For docker environment:
docker pull pytorch/pytorch:0.4-cuda9-cudnn7-develFor conda environment:
conda create --name DCL file conda_list.txtFor more backbone supports in DCL, please check pretrainmodels and install:
pip install pretrainedmodels-
Download correspond dataset to folder 'datasets'
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Data organization: eg. CUB
All the image data are in './datasets/CUB/data/' e.g. './datasets/CUB/data/*.jpg'
The annotation files are in './datasets/CUB/anno/' e.g. './dataset/CUB/data/train.txt'
In annotations:
name_of_image.jpg label_num\ne.g. for CUB in repository:
Black_Footed_Albatross_0009_34.jpg 0 Black_Footed_Albatross_0014_89.jpg 0 Laysan_Albatross_0044_784.jpg 1 Sooty_Albatross_0021_796339.jpg 2 ...
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.
Run train.py to train DCL.
For training CUB / STCAR / AIR from scratch
python train.py --data CUB --epoch 360 --backbone resnet50 \
--tb 16 --tnw 16 --vb 512 --vnw 16 \
--lr 0.0008 --lr_step 60 \
--cls_lr_ratio 10 --start_epoch 0 \
--detail training_descibe --size 512 \
--crop 448 --cls_mul --swap_num 7 7For training CUB / STCAR / AIR from trained checkpoint
python train.py --data CUB --epoch 360 --backbone resnet50 \
--tb 16 --tnw 16 --vb 512 --vnw 16 \
--lr 0.0008 --lr_step 60 \
--cls_lr_ratio 10 --start_epoch $LAST_EPOCH \
--detail training_descibe4checkpoint --size 512 \
--crop 448 --cls_mul --swap_num 7 7For training FGVC product datasets from scratch
python train.py --data product --epoch 60 --backbone senet154 \
--tb 96 --tnw 32 --vb 512 --vnw 32 \
--lr 0.01 --lr_step 12 \
--cls_lr_ratio 10 --start_epoch 0 \
--detail training_descibe --size 512 \
--crop 448 --cls_2 --swap_num 7 7For training FGVC datasets from trained checkpoint
python train.py --data product --epoch 60 --backbone senet154 \
--tb 96 --tnw 32 --vb 512 --vnw 32 \
--lr 0.01 --lr_step 12 \
--cls_lr_ratio 10 --start_epoch $LAST_EPOCH \
--detail training_descibe4checkpoint --size 512 \
--crop 448 --cls_2 --swap_num 7 7To achieve the similar results of paper, please use the default parameter settings.
Please cite our CVPR19 paper if you use DCL in your work:
@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}
}