https://github.com/StriveZs/ALPS

ALPS: An Auto-Labeling and Pre-training Scheme for Remote Sensing Segmentation With Segment Anything Model
https://github.com/StriveZs/ALPS

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ALPS: An Auto-Labeling and Pre-training Scheme for Remote Sensing Segmentation With Segment Anything Model

• Host: GitHub
• URL: https://github.com/StriveZs/ALPS
• Owner: StriveZs
• Created: 2024-06-02T06:37:32.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-08-20T02:31:41.000Z (5 months ago)
• Last Synced: 2024-08-21T05:58:53.024Z (5 months ago)
• Language: Python
• Size: 18.6 KB
• Stars: 7
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# ALPS

This is the offical implement of our ALPS framwork.

Paper: "ALPS: An Auto-Labeling and Pre-training Scheme for Remote Sensing Segmentation With Segment Anything Model"

[Paper Link](https://arxiv.org/abs/2406.10855)

## Usage

### Environment

- Python 3.8.5

- Pytorch 1.12.1+cu113

- Torchvision 0.13.1+cu113

- Torchaudio 0.12.1+cu113

- scikit-learn 1.3.2

- segment-anything 1.0

Install Segment Anything:

```

git clone [email protected]:facebookresearch/segment-anything.git

cd segment-anything; pip install -e .

```

Click the links below to download the checkpoint for the corresponding model type.

- vit_h: [ViT-H SAM model](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth)

- vit_l: [ViT-L SAM model](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth)

- vit_b: [ViT-B SAM model](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth)

### Getting Started

1. Follow the structure below to organize the dataset:

```

├──dataset_path

│   ├── img_dir

│   │   ├── train

│   │   │   ├── xxx{img_suffix}

│   │   │   ├── yyy{img_suffix}

│   │   │   ├── zzz{img_suffix}

│   │   │   ├── ....

│   │   ├── val

│   │   │   ├── xxx{img_suffix}

│   │   │   ├── yyy{img_suffix}

│   │   │   ├── zzz{img_suffix}

│   │   │   ├── ....

│   │   ├── test

│   │   │   ├── xxx{img_suffix}

│   │   │   ├── yyy{img_suffix}

│   │   │   ├── zzz{img_suffix}

│   │   │   ├── ....

```

2. First, download the pre-trained SAM model checkpoint. Then, you can use the command line below to perform automatic labeling on your own datasets:

```

python main.py --root_dir .../dataset_path --image_suffix .png --sam_checkpoint .../sam_vit_h_4b8939.pth --model_type vit_h --number_clusters xx --vis True

```

3. Additionally, our framework supports more customized parameter settings:

- 'process_list': This can be set to ‘train’, ‘val’, or ‘test’.

- 'threshold': This is the area size threshold used in the filtering gate module.

- 'label_dir': This is the path where the pseudo-labels are saved.

- 'device': This can be set to ‘cuda’ or ‘cpu’.

- 'batch_size': This is the batch size used in online K-means.

- More parameter settings can be found in the main.py file.