Image Segmentation with SAM

Implement image segmentation using Meta's Segment Anything Model.

Project requirements:
- Segmentation type: [AUTOMATIC/PROMPTED/INTERACTIVE]
- Image domain: [DESCRIBE YOUR IMAGES]
- Output format: [MASKS/POLYGONS/BOUNDING BOXES]
- Use case: [ANNOTATION/PREPROCESSING/ANALYSIS]

SAM implementation:
1. Model setup:
   - Model size selection (ViT-B/L/H)
   - Checkpoint loading
   - Device configuration
2. Automatic segmentation:
   - Everything mode
   - Point grid configuration
   - Filtering by area/confidence
3. Prompted segmentation:
   - Point prompts (positive/negative)
   - Box prompts
   - Mask prompts
4. Post-processing:
   - Mask refinement
   - Merging overlapping masks
   - Format conversion
5. Batch processing:
   - Efficient batching
   - Memory management
   - Progress tracking
6. Integration:
   - Annotation tool interface
   - API for programmatic use
   - Video frame processing
7. Fine-tuning (if needed):
   - Adapter training
   - Domain-specific optimization

Optimize for inference speed and accuracy.