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# SAM 2 Few-Shot/Zero-Shot Segmentation Research
This repository contains research on combining Segment Anything Model 2 (SAM 2) with minimal supervision for domain-specific segmentation tasks.
## Research Overview
The goal is to study how SAM 2 can be adapted to new object categories in specific domains (satellite imagery, fashion, robotics) using:
- **Few-shot learning**: 1-10 labeled examples per class
- **Zero-shot learning**: No labeled examples, using text prompts and visual similarity
## Key Research Areas
### 1. Domain Adaptation
- **Satellite Imagery**: Buildings, roads, vegetation, water bodies
- **Fashion**: Clothing items, accessories, patterns
- **Robotics**: Industrial objects, tools, safety equipment
### 2. Learning Paradigms
- **Prompt Engineering**: Optimizing text prompts for SAM 2
- **Visual Similarity**: Using CLIP embeddings for zero-shot transfer
- **Meta-learning**: Learning to adapt quickly to new domains
### 3. Evaluation Metrics
- IoU (Intersection over Union)
- Dice Coefficient
- Boundary Accuracy
- Domain-specific metrics
## Project Structure
```
├── data/ # Dataset storage
├── models/ # Model implementations
├── experiments/ # Experiment configurations
├── utils/ # Utility functions
├── notebooks/ # Jupyter notebooks for analysis
├── results/ # Experiment results and visualizations
└── requirements.txt # Dependencies
```
## Quick Start
1. **Install dependencies**:
```bash
pip install -r requirements.txt
```
2. **Download SAM 2**:
```bash
python scripts/download_sam2.py
```
3. **Run few-shot experiment**:
```bash
python experiments/few_shot_satellite.py
```
4. **Run zero-shot experiment**:
```bash
python experiments/zero_shot_fashion.py
```
## Research Papers
This work builds upon:
- [SAM 2: Segment Anything Model 2](https://arxiv.org/abs/2311.15796)
- [CLIP: Learning Transferable Visual Representations](https://arxiv.org/abs/2103.00020)
- [Few-shot Learning for Semantic Segmentation](https://arxiv.org/abs/1709.03410)
## Contributing
Please read our contributing guidelines and code of conduct before submitting pull requests.
## License
MIT License - see LICENSE file for details. |