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"

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} }