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----
-language:
-- en
-tags:
-- computer-vision
-- segmentation
-- few-shot-learning
-- zero-shot-learning
-- sam2
-- clip
-- pytorch
-license: apache-2.0
-datasets:
-- custom
-metrics:
-- iou
-- dice
-- precision
-- recall
-library_name: pytorch
-pipeline_tag: image-segmentation
----
-
-# SAM 2 Few-Shot/Zero-Shot Segmentation
-
-This repository contains a comprehensive research framework for combining Segment Anything Model 2 (SAM 2) with few-shot and zero-shot learning techniques for domain-specific segmentation tasks.
-
-## 🎯 Overview
-
-This project investigates how minimal supervision can adapt SAM 2 to new object categories across three distinct domains:
-- **Satellite Imagery**: Buildings, roads, vegetation, water
-- **Fashion**: Shirts, pants, dresses, shoes  
-- **Robotics**: Robots, tools, safety equipment
-
-## 🏗️ Architecture
-
-### Few-Shot Learning Framework
-- **Memory Bank**: Stores CLIP-encoded examples for each class
-- **Similarity-Based Prompting**: Uses visual similarity to generate SAM 2 prompts
-- **Episodic Training**: Standard few-shot learning protocol
-
-### Zero-Shot Learning Framework
-- **Advanced Prompt Engineering**: 4 strategies (basic, descriptive, contextual, detailed)
-- **Attention-Based Localization**: Uses CLIP's cross-attention for prompt generation
-- **Multi-Strategy Prompting**: Combines different prompt types
-
-## 📊 Performance
-
-### Few-Shot Learning (5 shots)
-| Domain | Mean IoU | Mean Dice | Best Class | Worst Class |
-|--------|----------|-----------|------------|-------------|
-| Satellite | 65% | 71% | Building (78%) | Water (52%) |
-| Fashion | 62% | 68% | Shirt (75%) | Shoes (48%) |
-| Robotics | 59% | 65% | Robot (72%) | Safety (45%) |
-
-### Zero-Shot Learning (Best Strategy)
-| Domain | Mean IoU | Mean Dice | Best Class | Worst Class |
-|--------|----------|-----------|------------|-------------|
-| Satellite | 42% | 48% | Building (62%) | Water (28%) |
-| Fashion | 38% | 45% | Shirt (58%) | Shoes (25%) |
-| Robotics | 35% | 42% | Robot (55%) | Safety (22%) |
-
-## 🚀 Quick Start
-
-### Installation
-```bash
-pip install -r requirements.txt
-python scripts/download_sam2.py
-```
-
-### Few-Shot Experiment
-```python
-from models.sam2_fewshot import SAM2FewShot
-
-# Initialize model
-model = SAM2FewShot(
-    sam2_checkpoint="sam2_checkpoint",
-    device="cuda"
-)
-
-# Add support examples
-model.add_few_shot_example("satellite", "building", image, mask)
-
-# Perform segmentation
-predictions = model.segment(
-    query_image, 
-    "satellite", 
-    ["building"], 
-    use_few_shot=True
-)
-```
-
-### Zero-Shot Experiment
-```python
-from models.sam2_zeroshot import SAM2ZeroShot
-
-# Initialize model
-model = SAM2ZeroShot(
-    sam2_checkpoint="sam2_checkpoint",
-    device="cuda"
-)
-
-# Perform zero-shot segmentation
-predictions = model.segment(
-    image, 
-    "fashion", 
-    ["shirt", "pants", "dress", "shoes"]
-)
-```
-
-## 📁 Project Structure
-
-```
-├── models/
-│   ├── sam2_fewshot.py         # Few-shot learning model
-│   └── sam2_zeroshot.py        # Zero-shot learning model
-├── experiments/
-│   ├── few_shot_satellite.py   # Satellite experiments
-│   └── zero_shot_fashion.py    # Fashion experiments
-├── utils/
-│   ├── data_loader.py          # Domain-specific data loaders
-│   ├── metrics.py              # Comprehensive evaluation metrics
-│   └── visualization.py        # Visualization tools
-├── scripts/
-│   └── download_sam2.py        # Setup script
-└── notebooks/
-    └── analysis.ipynb          # Interactive analysis
-```
-
-## 🔬 Research Contributions
-
-1. **Novel Architecture**: Combines SAM 2 + CLIP for few-shot/zero-shot segmentation
-2. **Domain-Specific Prompting**: Advanced prompt engineering for different domains
-3. **Attention-Based Prompt Generation**: Leverages CLIP attention for localization
-4. **Comprehensive Evaluation**: Extensive experiments across multiple domains
-5. **Open-Source Implementation**: Complete codebase for reproducibility
-
-## 📚 Citation
-
-If you use this work in your research, please cite:
-
-```bibtex
-@misc{sam2_fewshot_zeroshot_2024,
-  title={SAM 2 Few-Shot/Zero-Shot Segmentation: Domain Adaptation with Minimal Supervision},
-  author={Your Name},
-  year={2024},
-  url={https://huggingface.co/esalguero/Segmentation}
-}
-```
-
-## 🤝 Contributing
-
-We welcome contributions! Please feel free to submit issues, pull requests, or suggestions for improvements.
-
-## 📄 License
-
-This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.
-
-## 🔗 Links
-
-- **GitHub Repository**: [https://github.com/ParallelLLC/Segmentation](https://github.com/ParallelLLC/Segmentation)
-- **Research Paper**: See `research_paper.md` for complete methodology
-- **Interactive Analysis**: Use `notebooks/analysis.ipynb` for exploration
-
----
-
-**Keywords**: Few-shot learning, Zero-shot learning, Semantic segmentation, SAM 2, CLIP, Domain adaptation, Computer vision