CQULab / JGDN

JGDN

Our model is based on the paper of "Exploring a Fine-grained Multiscale Method for Cross-modal Remote Sensing Image Retrieval". Thanks to the great contribution.

1. Environment

This is a demo on the datasets of RSITMD, RSICD, Sydney and UCM for our paper. We finish experiments on a server with one NVIDIA GeForce 1080Ti GPU.

We recommended the following dependencies.

Python 3
PyTorch > 0.3
Numpy
h5py
nltk
yaml

2. Relevant data

Please preprocess dataset to appropriate the input format or you can download the data we preprocessed from the pan.baidu.com.

RSICD images (Password:NIST)   https://pan.baidu.com/s/1lH5m047P9m2IvoZMPsoDsQ
RISTMD images (Password:NIST) https://pan.baidu.com/share/init?surl=gDj38mzUL-LmQX32PYxr0Q

3. Train the new model

Please modify the parameters in the directory of ./option to suit your situation.

Run the train.py to train your own model.

4. Test the trained model

Run the test.py to test your trained model.