lwpyh / Unsupervised-Satellite-Image-Classfication-based-on-Partial-Domain-Adaptation

machine learning project group 5

Unsupervised-Satellite-Image-Classfication-based-on-Partial-Domain-Adaptation

machine learning project group 5

Code for course project Unsupervised Satellite Image Classification based on Partial Adversarial Domain Adaptation

Team member:Hu Jian; Clinton Elian Gandana;
Joel Dzidzorvi Kwame Disu; Chen Junjie; Zheng Cheng

Prerequisites

Linux or OSX

NVIDIA GPU + CUDA (may CuDNN) and corresponding PyTorch framework (version 0.3.1)

Python 2.7/3.5

Datasets

We use NWPU-RESISC45 and UC Merced Land dataset in our experiments. For our mession, "data/NWPU-RESISC45/NWPU-45.txt" is the source list file and "/data/UCMercedLand-share/UCMerced-19.txt" is the target list file.

Training and Evaluation

First, you can manually download the PyTorch pre-trained model introduced in `torchvision' library or if you have connected to the Internet, you can automatically downloaded them. Then, you can train the model for each dataset using the followling command.

cd src
python train_pada.py --gpu_id 2 --net ResNet50 --dset NWPU-RESISC45 --s_dset_path ../data/NWPU-RESISC45/NWPU-45.txt --t_dset_path ../data/UCMercedLand-share/UCMerced-19.txt --test_interval 500 --snapshot_interval 5000 --output_dir san1

You can set the command parameters to switch between different experiments.

• "gpu_id" is the GPU ID to run experiments.
• "dset" parameter is the dataset selection. In our experiments, it is NWPU-RESISC45
• "s_dset_path" is the source dataset list.
• "t_dset_path" is the target dataset list.
• "test_interval" is the interval of iterations between two test phase.
• "snapshot_interval" is the interval of iterations between two snapshot models.
• "output_dir" is the output directory of the log and snapshot.
• "net" sets the base network. For details of setting, you can see network.py.
  • For AlexNet, "net" is AlexNet.
  • For VGG, "net" is like VGG16. Detail names are in network.py.
  • For ResNet, "net" is like ResNet50. Detail names are in network.py.

Contribution of Our Members:

Hu Jian:build up the model framework,finish the part of train_PADA_pic.py and loss.py.
Clinton Elian Gandana:build up the Alexnet network in network.py.
Joel Dzidzorvi Kwame Disu:foucus on preprocss,finish the pre_process.py.
Chen Junjie:finish the comparation experiments,mainly focus on train_DaNN_pic.py and data_list.py.
Zheng Cheng:build up the comparation experiments in network,mainly focus on tran_ResNet_pic.py and lr_schedule.py.