Mutual-Channel Loss for Fine-Grained Image Classification

Mutual-Channel-Loss

Code release for The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification (TIP 2020)
DOI

Changelog

2020/09/14 update the code: CUB-200-2011_ResNet18.py Training with ResNet18 (TRAINED FROM SCRATCH).
2020/04/19 add the hyper-parameter fine-tune results.
2020/04/18 clean the code for better understanding.

Dataset

CUB-200-2011

Requirements

python 3.6
PyTorch 1.2.0
torchvision

Training

Download datasets
Train: python CUB-200-2011.py, the alpha and beta are the hyper-parameters of the MC-Loss
Description : PyTorch CUB-200-2011 Training with VGG16 (TRAINED FROM SCRATCH).

Hyper-parameter

Loss = ce_loss + alpha_1 * L_dis + beta_1 * L_div
Mutual-Channel-Loss-1
The figure is plot by NNI.

Other versions

Other unofficial implements can be found in the following:

Kurumi233: This repo integrate the MC-Loss into a class. code
darcula1993: This repo implement the tf version of the MC-Loss. code

Citation

If you find this paper useful in your research, please consider citing:

@ARTICLE{9005389, 
author={D. {Chang} and Y. {Ding} and J. {Xie} and A. K. {Bhunia} and X. {Li} and Z. {Ma} and M. {Wu} and J. {Guo} and Y. {Song}}, 
journal={IEEE Transactions on Image Processing}, 
title={The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification}, 
year={2020}, volume={29}, number={}, pages={4683-4695}, 
doi={10.1109/TIP.2020.2973812}, 
ISSN={1941-0042}, 
month={},}