WDSS

Installation

To set up the environment, please follow these steps:

Create and activate a conda environment :

conda create -n wdss_env python=3.7
conda activate wdss_env

Install the required packages:
```
pip install -r requirements.txt
```

Datasets

You can download the pre-processed datasets from the following Baidu Netdisk link , and extraction code is 1234:

Vaihingen: [https://pan.baidu.com/s/1ONi-iKEdgIVXRBarZechVg]

Loveda: [https://pan.baidu.com/s/1LJpVGdl9aY006Md1RQtmVw ]

Potsdam: [https://pan.baidu.com/s/1qSPbHBkR5iCfRTwminY_HQ ]

After downloading and extracting, place the datasets in the ./Datasets directory.

Project Structure

The folder structure for the WDSS directory is as follows:

WDSS/
├── Datasets/
│   ├── Loveda/
│   │   ├── 3SGB/
│   │   ├── 3SGO/
│   │   ├── img/
│   │   ├── img_0.125/
│   │   ├── img_0.25/
│   │   ├── label/
│   │   ├── label_0.125/
│   │   ├── label_0.25/
│   │   ├── SGB/
│   │   └── SGO/
│   ├── Potsdam/
│   └── Vaihingen/
├── Result/
│   ├── loveda/
│   │   ├── 0.125/
│   │   │   ├── 1/
│   │   │   │   ├── img_PL/
│   │   │   │   └── lable_PL/
│   │   │   ├── 2/
│   │   ├── 0.25/
│   │   │   ├── 1/
│   │   │   │   ├── img_PL/
│   │   │   │   └── lable_PL/
│   │   │   ├── 2/
│   ├── potsdam/
│   └── vaihingen/
├── pretrain/
│   ├── sam_vit_h_4b8939.pth
│   ├── swinv2_base_patch4_window16_256.pth
├── model/
│   ├── FTUNetFormer.py
│   ├── FTUNetFormer_1.py
│   └── swintransformerv2.py
├── tools/
│   ├── image_cropping.py
│   └── SAM_wavelet.py
├── mask.py
├── requirements.txt
├── train_1.py
├── train_2.py
├── utils_1.py
├── utils_2.py
└── utils_mask.py

Usage

Two-Stage Self-Training

You can directly download our pre-processed datasets and use them for the second stage of training without generating SGO and SGB or performing the first stage of training to prepare pseudo-labels.

Second Stage Training

Modify the train_2.py and utils_2.py files according to your paths and datasets, and load 3SGO and 3SGB.
Navigate to the project directory and activate the environment:
```
conda activate wdss_env
```
Run the following command to start training:
```
python train_2.py
```

Complete Two-Stage Process

If you want to use your own datasets and perform the complete two-stage process, you can use image_cropping.py and SAM_wavelet.py provided in the tools folder for image cropping and wavelet-driven SAM-generated object reconstruction.

First Stage Training

Perform the first stage of training by loading SGO and SGB, and modifying the train_1.py and utils_1.py files.
Navigate to the project directory and activate the environment:
```
conda activate wdss_env
```
Run the following command to start training:
```
python train_1.py
```
Generate pseudo-labels using mask.py and utils_mask.py:
```
python mask.py
```

dehaozhou/WDSS