# Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization

This branch is developed for fine-grained recognition, the related paper is as follows:

    Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization
    Ruyi Ji, Longyin Wen, Libo Zhang, Dawei Du, Yanjun Wu, Chen Zhao, Xianglong Liu, Feiyue Huang

[Note:] This is our initial version. For the stability of the model and the reduction of parameters, we have made appropriate modifications.

### Files
- Original Caffe library
- Sum Pooling layer
  * src/caffe/proto/caffe.proto
  * include/caffe/layers/sum_pooling_layer.hpp
  * src/caffe/layers/sum_pooling_layer.cpp
  * src/caffe/layers/sum_pooling_layer.cu
- L2 Normalize layer
  * include/caffe/layers/l2_normalize_layer.hpp
  * src/caffe/layers/l2_normalize_layer.cpp
  * src/caffe/layers/l2_normalize_layer.cu
- Signed Sqrt layer
  * include/caffe/layers/signed_sqrt_layer.hpp
  * src/caffe/layers/signed_sqrt_layer.cpp
  * src/caffe/layers/signed_sqrt_layer.cu
- Example
  * example/acnet/datasets
  * example/acnet/logs
  * example/acnet/model-zoo
  * example/acnet/prototxts
  * example/acnet/snapshots

### Train model
1. The Installation completely the same as [Caffe](http://caffe.berkeleyvision.org/). Please follow the [installation instructions](http://caffe.berkeleyvision.org/installation.html). Make sure you have correctly installed before using our code.
2. Download the [CUB dataset](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html).
3. Preprocess the CUB dataset and creat list for training set and validation set, place them in example/acnet/datasets/.
4. Initialize the parameters of model with [init model](https://drive.google.com/file/d/16vdy_VybR-OUhb4KZN7p4hEo1T1cSOBf/view?usp=sharing).
5. Train the new added layers.

   	   bash ./run_first_stage.sh #num_gpu

6. Fine tune the whole network.

       bash ./run_second_stage.sh #num_gpu


### Citation
If the code helps your research, please consider to cite our work:

    @article{1,
      title={Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization},
      author={Ruyi Ji, Longyin Wen, Libo Zhang, Dawei Du, Yanjun Wu, Chen Zhao, Xianglong Liu, Feiyue Huang},
      journal={arXiv preprint arXiv:1909.11378},
      year={2019}
    }