HBP-pytorch
Overview
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
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
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
| 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
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.