# SAMPolyBuild

**Repository Path**: labiao/SAMPolyBuild

## Basic Information

- **Project Name**: SAMPolyBuild
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-10-15
- **Last Updated**: 2024-12-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# SAMPolyBuild
Adapting the Segment Anything Model (SAM) for Polygonal Building Extraction

## Installation
Conda virtual environment is recommended for installation. Please choose the appropriate version of torch and torchvision according to your CUDA version.
```shell
conda create -n sampoly python=3.10 -y
source activate sampoly # or conda activate sampoly
pip install torch==2.0.0+cu117 torchvision==0.15.1+cu117 --index-url https://download.pytorch.org/whl/cu117
pip install -r requirements.txt
```
Download the SAM vit_b model from [here](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth) and place it in the 'segment_anything' folder.

## Dataset Preparation
### SpaceNet Vegas Dataset
We converted the original images of the SpaceNet dataset to 8-bit and the annotations to coco format, and divided them into training, validation, and test sets in the ratio of 8:1:1, which are available for download from [here](https://aistudio.baidu.com/datasetdetail/269168). Place the train, val, test folders in the 'dataset/spacenet' folder.

### Custom Dataset
The custom dataset should be in the following format, or change the **train_dataset_pth**, **val_dataset_pth** in the train.py and **dataset_pth** in the test.py to the corresponding path.
```
dataset
├── dataset_name
    ├── train
    |    ├── images
    |    ├── ann.json
    ├── val
    |    ├── images
    |    ├── ann.json
    ├── test
        ├── images
        ├── ann.json
```

## Training
Single gpu:
```shell
python train.py --config configs/prompt_instance_spacenet.json --gpus 0
```
Multi gpus:
```shell
python train.py --config configs/prompt_instance_spacenet.json --gpus 0 1 --distributed
```

## Testing
Evaluate the model on the test set, and save the results:
```shell
python test.py
```
You need to change the **--task_name** to the corresponding training task name, and the other arguments will be set automatically according to training configuration.


## Acknowledgement
This project is developed based on the [Segment Anything Model (SAM)](https://github.com/facebookresearch/segment-anything) project. We thank the authors for their contributions.

## License

This project is licensed under the [Apache 2.0 license](LICENSE).

## Contact
If you have any questions, please contact wangchenhao22@mails.ucas.ac.cn.