Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification

Introduction

This is the source code for our paper Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification

Network Architecture

The architecture of our proposed peer-learning model is as follows

Installation

After creating a virtual environment of python 3.7, run pip install -r requirements.txt to install all dependencies

How to use

The code is currently tested only on GPU

• Data Preparation

  • Download data into project root directory and uncompress them using

    wget https://wsnfg.oss-cn-hongkong.aliyuncs.com/web-bird.tar.gz
    wget https://wsnfg.oss-cn-hongkong.aliyuncs.com/web-car.tar.gz
    wget https://wsnfg.oss-cn-hongkong.aliyuncs.com/web-aircraft.tar.gz
    tar -xvf web-bird.tar.gz
    tar -xvf web-car.tar.gz
    tar -xvf aircraft-car.tar.gz
    

• Source Code

  • If you want to train the whole network from begining using source code on the web fine-grained dataset, please follow subsequent steps

    • Choose a dataset, create soft link to dataset by

    ln -s web-bird bird
    ln -s web-car car
    ln -s web-aircraft aircraft
    

    • Modify CUDA_VISIBLE_DEVICES to proper cuda device id in cub200_run.sh, car196_run.sh, aircraft100_run.sh

    • Activate virtual environment(e.g. conda) and then run the script

    bash cub200_train.sh
    bash car196_run.sh
    bash aircraft100_run.sh