yformer/EfficientSAM

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2491 stars · 163 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

EfficientSAM is an efficient image segmentation model that leverages masked image pretraining to provide a lightweight alternative to Meta's Segment Anything Model (SAM). It supports point-prompt, box-prompt, and segment-everything modes, with checkpoints, TorchScript, and ONNX exports available.

Use cases

• segment objects in images with point or box prompts
• run a faster lighter alternative to Segment Anything
• export segmentation model to ONNX for deployment
• integrate AI-assisted segmentation into annotation tools like Labelme
• segment everything in an image automatically
• build grounded segmentation with text prompts

When to choose

• you need SAM-like promptable segmentation with lower compute cost
• you want ONNX or TorchScript exports for production deployment
• you need a permissively licensed (Apache-2.0) segmentation model

When to avoid

• you need video object tracking rather than image segmentation (see EfficientTAM)
• you require the absolute highest segmentation accuracy regardless of speed
• you need a turnkey GUI application rather than a model library

Facets

library · maturity active

machine-learning computer-vision image-processing computer-vision machine-learning image-processing artificial-intelligence python cross-platform segment-anything image-segmentation masked-image-pretraining onnx promptable-segmentation sam gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="yformer/EfficientSAM")