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@WangLibo1995
WangLibo1995 committed May 12, 2022
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Expand Up @@ -137,154 +137,6 @@ python GeoSeg/loveda_test.py -c GeoSeg/config/loveda/dcswin.py -o fig_results/lo
If you find this project useful in your research, please consider citing [our papers](https://WangLibo1995.github.io).


## Acknowledgement

We wish **GeoSeg** could serve the growing research of remote sensing by providing a unified benchmark
and inspiring researchers to develop their own segmentation networks. Many thanks the following projects's contributions to **GeoSeg**.
- [pytorch lightning](https://www.pytorchlightning.ai/)
- [timm](https://github.com/rwightman/pytorch-image-models)
- [pytorch-toolbelt](https://github.com/BloodAxe/pytorch-toolbelt)
- [ttach](https://github.com/qubvel/ttach)
- [catalyst](https://github.com/catalyst-team/catalyst)
- [mmsegmentation](https://github.com/open-mmlab/mmsegmentation)
=======
[Welcome to my homepage!](https://WangLibo1995.github.io)

## Introduction

**GeoSeg** is an open-source semantic segmentation toolbox based on PyTorch, [pytorch lightning](https://www.pytorchlightning.ai/) and [timm](https://github.com/rwightman/pytorch-image-models),
which mainly focuses on using advanced CNNs and Vision Transformers for remote sensing image segmentation.


## Major Features

- Unified Benchmark

we provide a unified training script for various segmentation methods.

- Simple and Effective

Thanks to **pytorch lightning** and **timm** , the code is easy for further development.

- Support More Remote Sensing Datasets

- [ISPRS Vaihingen and Potsdam](https://www.isprs.org/education/benchmarks/UrbanSemLab/default.aspx)
- [UAVid](https://uavid.nl/)
- [LoveDA](https://codalab.lisn.upsaclay.fr/competitions/421)
- [WHU building](http://gpcv.whu.edu.cn)
- [Inria Aerial Image Labelling](https://project.inria.fr/aerialimagelabeling/)
- More datasets will be supported in the future.

- Multi-scale Training and Testing
- Inference on Huge Remote Sensing Images

## Supported Networks

- Vision Transformer

- [UNetFormer](http://arxiv.org/abs/2109.08937)
- [DC-Swin](https://ieeexplore.ieee.org/abstract/document/9681903)
- [BANet](https://www.mdpi.com/2072-4292/13/16/3065)

- CNN

- [MANet](https://ieeexplore.ieee.org/abstract/document/9487010)
- [ABCNet](https://www.sciencedirect.com/science/article/pii/S0924271621002379)
- [A2FPN](https://www.tandfonline.com/doi/full/10.1080/01431161.2022.2030071)

## Folder Structure

Prepare the following folders to organize this repo:
```none
airs
├── GeoSeg (code)
├── pretrain_weights (save the pretrained weights like vit, swin, etc)
├── model_weights (save the model weights)
├── fig_results (save the masks predicted by models)
├── lightning_logs (CSV format training logs)
├── data
│ ├── LoveDA
│ │ ├── Train
│ │ │ ├── Urban
│ │ │ │ ├── images_png (original images)
│ │ │ │ ├── masks_png (original masks)
│ │ │ │ ├── masks_png_convert (converted masks used for training)
│ │ │ │ ├── masks_png_convert_rgb (original rgb format masks)
│ │ │ ├── Rural
│ │ │ │ ├── images_png
│ │ │ │ ├── masks_png
│ │ │ │ ├── masks_png_convert
│ │ │ │ ├── masks_png_convert_rgb
│ │ ├── Val (the same with Train)
│ │ ├── test
│ ├── uavid
│ │ ├── uavid_train (original)
│ │ ├── uavid_val (original)
│ │ ├── uavid_test (original)
│ │ ├── train (processed)
│ │ ├── val (processed)
│ ├── vaihingen
│ │ ├── train_images (original)
│ │ ├── train_masks (original)
│ │ ├── train_masks_eroded (original)
│ │ ├── val_images (original)
│ │ ├── val_masks (original)
│ │ ├── val_masks_eroded (original)
│ │ ├── test_images (original)
│ │ ├── test_masks (original)
│ │ ├── test_masks_eroded (original)
│ │ ├── train (processed)
│ │ ├── val (processed)
│ │ ├── test (processed)
│ ├── potsdam (the same with vaihingen)
```

## Install

Open the folder **airs** using **Linux Terminal** and create python environment:
```
conda create -n airs python=3.8
conda activate airs
conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install -r GeoSeg/requirements.txt
```
## Data Preprocessing

**LoveDA**
```
python GeoSeg/tools/loveda_mask_convert.py --mask-dir data/LoveDA/Train/Rural/masks_png --output-mask-dir data/LoveDA/Train/Rural/masks_png_convert
python GeoSeg/tools/loveda_mask_convert.py --mask-dir data/LoveDA/Train/Urban/masks_png --output-mask-dir data/LoveDA/Train/Urban/masks_png_convert
python GeoSeg/tools/loveda_mask_convert.py --mask-dir data/LoveDA/Val/Rural/masks_png --output-mask-dir data/LoveDA/Val/Rural/masks_png_convert
python GeoSeg/tools/loveda_mask_convert.py --mask-dir data/LoveDA/Val/Urban/masks_png --output-mask-dir data/LoveDA/Val/Urban/masks_png_convert
```
More datasets are updating.

## Training

**LoveDA**
```
python GeoSeg/train_supervision.py -c GeoSeg/config/loveda/unetformer.py
```

## Validation

**LoveDA**
```
python GeoSeg/loveda_test.py -c GeoSeg/config/loveda/unetformer.py -o fig_results/loveda/unetformer_val --rgb --val -t 'd4'
```

## Testing

**LoveDA**
```
python GeoSeg/loveda_test.py -c GeoSeg/config/loveda/unetformer.py -o fig_results/loveda/unetformer_test --rgb -t 'd4'
```
## Citation

If you find this project useful in your research, please consider cite [our papers](https://WangLibo1995.github.io).


## Acknowledgement

We wish **GeoSeg** could serve the growing research of remote sensing by providing a unified benchmark
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