> ML_LIBRARY // SEGMENT-ANYTHING_v1.0
Segment Anything (SAM)
Meta FAIR — Meta's promptable foundation model for zero-shot image and video segmentation.
computer-visionv2.1Apache-2.0qualified
Model Training
Accelerators:
CPUCUDAROCMMPS
Distributed Training:Yes
Model Inference
Inference Accelerators:
CPUCUDAROCMMPS
Deployment Targets:server
Quantization:FP16, BF16, INT8 via ONNX
What It Does
- +Zero-shot segmentation from points, bounding boxes, or free-form masks
- +Continuous video object mask tracking and temporal memory propagation (SAM 2)
- +Automated whole-image mask generation for data labeling pipelines
- +Decoupled heavy image encoder and ultra-lightweight prompt decoder for fast user interaction
What It Does Not Do
- -Assign semantic category labels to segmented objects (pure geometric segmentation)
- -Run at 60 FPS on edge microcontrollers
- -Replace optical character recognition text readers
>Suitable Work Types
- Interactive video object rotoscoping and background replacement
- Accelerated data labeling for computer vision training sets
- Surgical and scientific image boundary delineation
>Unsuitable Work Types
- Semantic categorization where classification labels are required without CLIP pairing
- Low-power battery edge IoT sensors
Data Residency Implications
Runs locally on internal GPU infrastructure. Zero cloud exposure.
Security Considerations
Permissive Apache-2.0 license enables commercial derivative software and automated annotation platforms.
Operational Profile & Known Limitations
Maturity:mature
Learning Curve:moderate
Ops Complexity:moderate
Cost Tier:high-compute
> Known Limitations:
- Heavy Vision Transformer (ViT-H/L) backbone requires significant GPU VRAM (>=8GB) for the image encoder phase.
Associated Incident Patterns (Incidentpedia)
Enforce safeguards and monitoring to guard against these documented real-world failure modes:
> Primary Evidence & Benchmark Citations
Segment Anything 2 GitHub Repositoryofficial-docs • >=2.0.0, <=2.1.x
2026-09-25HIGH