A Method for Fine-Grained Aircraft Recognition Base on Web-Supervised Network

Introduction

This is the source code for our paper A Method for Fine-Grained Aircraft Recognition Base on Web-Supervised Network

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-sh.oss-cn-shanghai.aliyuncs.com/web-aircraft.tar.gz
    tar -xvf web-aircraft.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-aircraft aircraft
    

    • Modify CUDA_VISIBLE_DEVICES to proper cuda device id in run_train_resnet.sh

    • Modify data to the dataset you want to use in run_train_resnet.sh

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

    bash run_train_resnet.sh
    

  • We recommend you use Resnet-50 model, because it has better performance.