metadata
license: apache-2.0
base_model:
- facebook/sam3
tags:
- mlx
SAM3 MLX Examples
Example scripts demonstrating how to use SAM3 MLX for segmentation tasks.
Click-Based Segmentation
Segment objects by clicking on them with positive/negative points.
Basic Usage
# Segment with a single positive click
python click_segment.py --image photo.jpg --point 512,384
# Segment with multiple points
python click_segment.py --image photo.jpg --point 512,384 --point 600,400
# Use positive (+) and negative (-) points for refinement
python click_segment.py --image photo.jpg --point +512,384 --point -100,100
# Save visualization
python click_segment.py --image photo.jpg --point 512,384 --output result.png
# Get single best mask instead of 3 masks
python click_segment.py --image photo.jpg --point 512,384 --single-mask
Requirements
pip install pillow matplotlib mlx
Performance
On Apple Silicon with MLX:
- Model initialization: ~2-3s
- Single inference: <200ms (target performance)
- Multiple masks: 3 predictions per inference
Box-Based Segmentation
Coming soon: Segment using bounding box prompts.
Mask-Based Refinement
Coming soon: Refine existing masks with additional mask prompts.
Batch Processing
Coming soon: Process multiple images efficiently.