HBP-pytorch

 

Overview

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A third-party reimplementation of Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition in Pytorch.

The related paper is as follows:

Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition[C]
Chaojian Yu, Xinyi Zhao, Qi Zheng, Peng Zhang, Xinge You*
European Conference on Computer Vision. 2018.

Official Caffe implementation of Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition is HERE.

Preparation

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Dataset

• CUB-200-2011
• If you don't have a dataset, you can still execute the program and the program will automatically download the dataset.

Requirement

• pip install visdom pytorch torchvision

Usage

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for example:

• CUDA_VISIBLE_DEVICES=0,1 python HBP_fc.py --base_lr 1.0 --batch_size 12 --epochs 120 --weight_decay 0.000005 | tee 'hbp_fc.log'
• CUDA_VISIBLE_DEVICES=0,1 python HBP_all.py --base_lr 0.001 --batch_size 24 --epochs 200 --weight_decay 0.0005 --model 'HBP_fc_epoch_*.pth' | tee 'hbp_all.log'

Result

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file	acc
HBP_fc	80.42
HBP_fc_new	79.79
HBP_all	80.42

Note that HBP_fc_new.py may be the closest to the original implementation. But it still doesn't work well.

Last

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Based on my code and experimental results, it is far from the result of the original author. So you can use it as a reference for learning.

This code borrows from HERE. If you have any suggestions please contact me, I am still continue to improve the results.

Happy coding.