# tasn

**Repository Path**: jackeyguo/tasn

## Basic Information

- **Project Name**: tasn
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-10-12
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TASN
Code (MXNet version) for our cvpr'19 paper "Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition"

![alt text](https://user-images.githubusercontent.com/35843017/59253558-14314380-8c61-11e9-9c90-c8de5442ccad.jpg)

Prerequisites
-------
cuda version = 8.0
cudnn5.0
nccl
libopenblas
liblapack
libopencv 

Install
-------
First clone this repository:

    sudo git clone https://github.com/Heliang-Zheng/TASN.git
    cd TASN/tasn-mxnet

Then, please follow https://mxnet.incubator.apache.org/install/build_from_source.html to compile and install mxnet.

Or download pre-build mxnet (with cuda 8.0): https://drive.google.com/open?id=1Sfpw0x5XLqBFWAt99-zKOp4jAbOxm5Ws and install by:

    cd TASN/tasn-mxnet/example/tasn
    sudo bash install.sh


Train TASN
-------
1) get into the tasn dir:

        cd TASN/tasn-mxnet/example/tasn

2) download data and pretrained model (on ImageNet):

        sudo bash init.sh

3) set your nccl path in train.sh

4) run :

        sudo bash train.sh

Experiments settings:
on CUB-200-2011 dataset : http://www.vision.caltech.edu/visipedia/CUB-200-2011.html

CNN input resolution: 224*224

Accuracy: 87.0%

Just changing the scale of AttSampler() in train.py from 224/512 to 336/512 to obtain the accuracy of 88.0%


Model:
-------
cub_224_87 https://drive.google.com/open?id=1uw9MVNVZqBTppN4TBbHB10CxoQonsTx9

cub_336_88 https://drive.google.com/open?id=1qQo8o2C5JpwxJGhrfk2xHM-f6kpxDKd1


Added files:
-------
example/tasn/*

src/operator/contrib/att_sampler-inl.h

src/operator/contrib/att_sampler.cc

src/operator/contrib/att_sampler.cu

PyTorch versioin
-------
On going.

Add master net (85.5%)

+ part net (86.2%) without distilling.

Thank https://github.com/ShenghaiRong for reimplementing Attention sampler for pytorch verion.


I would be very busy in the nearly future and cannot find time to finish the reimplement of pytorch version.
If anyone can tune and finish the reimplement, feel free to create a pull request.

Other Implements
-------
Attention sampler implementation (free from rebuilding mxnet):

https://github.com/wkcn/AttentionSampler


Reference
-------
@inproceedings{zheng2019looking,
  title={Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition},
  author={Zheng, Heliang and Fu, Jianlong and Zha, Zheng-Jun and Luo, Jiebo},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5012--5021},
  year={2019}
}