chongzhou96/EdgeSAM

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1173 stars · 61 forks observed · 2026-08-28

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

EdgeSAM is the official PyTorch implementation of a distilled, accelerated variant of the Segment Anything Model (SAM) designed for on-device deployment. It distills SAM's ViT encoder into a CNN-based architecture with prompts in the loop, achieving 40x speedup over SAM and over 30 FPS on an iPhone 14.

Use cases

• run segment anything model on mobile devices
• fast interactive image segmentation with box and point prompts
• distill SAM into a lightweight CNN model
• deploy segmentation model on iPhone with CoreML
• speed up SAM inference on edge devices
• annotate images with a fast SAM variant
• export SAM-like model to ONNX for web demos

When to choose

• you need SAM-quality promptable segmentation on resource-constrained or mobile hardware
• you want faster inference than MobileSAM with better accuracy
• you are deploying interactive segmentation on iOS or edge devices

When to avoid

• you need the full accuracy of the original ViT-based SAM on server hardware
• you need segmentation without box or point prompts
• you need a non-research license for commercial use

Facets

library · maturity active

machine-learning computer-vision image-processing computer-vision image-processing machine-learning artificial-intelligence python cross-platform segment-anything knowledge-distillation on-device-ai coreml onnx sam interactive-segmentation edge-devices ios

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markdown · JSON · MCP: product_card(name="chongzhou96/EdgeSAM")