Meta's Segment Anything Model: Amazing Results on Complex Buildings

The Test: Low-Quality Architectural Image

I tested Meta's Segment Anything Model (SAM) on a challenging low-quality image containing:

• Buildings with hundreds of ornate windows

• Complex decorative frames with inward/outward protrusions

• Overlapping cars and objects

• Poor image resolution

SAM's Incredible Performance

What SAM Achieved:

• Detected every single window individually despite poor quality

• Recognized ornate frames and decorative elements as separate segments

• Successfully separated overlapping cars

• Created clean, professional-quality edges on all complex details

Most Impressive: SAM understood architectural hierarchy - distinguishing window frames from glass, decorative elements from structure, and different protrusion depths.

Why SAM Excels

• Universal Training: Trained on 1 billion masks across all object types

• Zero-Shot Learning: Segments objects it's never seen before

• Advanced Architecture: Understands spatial relationships and context

• Massive Dataset: 11 million diverse images for robust learning

Bottom Line

SAM represents a major breakthrough in image segmentation. Its ability to handle complex architectural details and overlapping objects while maintaining professional precision is remarkable.

My Verdict: SAM is currently the best segmentation tool available. For anyone working with detailed imagery, the results will surprise you.