Introduction

This is the source code of our ACM MM 2017 paper "Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN", Please cite the following paper if you use our code.

Xiangteng He, Yuxin Peng and Junjie Zhao, "Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN", 25th ACM Multimedia Conference (ACM MM), pp. 627-635, Mountain View, CA, USA, Oct. 23-27, 2017. [PDF]

Dependency

Our code is based on early version of Faster R-CNN in MXNet, all the dependencies are the same as it.

Data Preparation

Here we use [CUB-200-2011] dataset for an example, we have organized the data as the form of [PASCAL VOC] dataset, which can be downloaded from link.

Download VGG16 pretrained model vgg16-0000.params from MXNet model gallery to model folder.

Usage

1. Start training by running python train_end2end.py --gpu GPUID. This will train the network on CUB-200-2011 train.
2. Start testing by running python test.py --gpu GPUID. This will test the VGG network on CUB-200-2011 test.

For more information, please refer to our ACM MM paper.

Welcome to our Laboratory Homepage for more information about our papers, source codes, and datasets.