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NTS-Net

This is a PyTorch implementation of the ECCV2018 paper "Learning to Navigate for Fine-grained Classification" (Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, Liwei Wang).

Requirements

  • python 3+
  • pytorch 0.4+
  • numpy
  • datetime

Datasets

Download the CUB-200-2011 datasets and put it in the root directory named CUB_200_2011, You can also try other fine-grained datasets.

Train the model

If you want to train the NTS-Net, just run python train.py. You may need to change the configurations in config.py. The parameter PROPOSAL_NUM is M in the original paper and the parameter CAT_NUM is K in the original paper. During training, the log file and checkpoint file will be saved in save_dir directory. You can change the parameter resume to choose the checkpoint model to resume.

Test the model

If you want to test the NTS-Net, just run python test.py. You need to specify the test_model in config.py to choose the checkpoint model for testing.

Model

We also provide the checkpoint model trained by ourselves, you can download it from here. If you test on our provided model, you will get a 87.6% test accuracy.

Reference

If you are interested in our work and want to cite it, please acknowledge the following paper:

@inproceedings{Yang2018Learning,
author = {Yang, Ze and Luo, Tiange and Wang, Dong and Hu, Zhiqiang and Gao, Jun and Wang, Liwei},
title = {Learning to Navigate for Fine-grained Classification},
booktitle = {ECCV},
year = {2018}
}

About

This is a PyTorch implementation of the ECCV2018 paper "Learning to Navigate for Fine-grained Classification" (Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, Liwei Wang).

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