meta-pytorch/segment-anything-fast

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1321 stars · 80 forks observed · 2026-08-28

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

A fast, batched offline inference-oriented fork of Meta's Segment Anything (SAM) image segmentation model. It applies optimizations like bfloat16, torch.compile with max-autotune, custom Triton SDPA kernels, and quantization as a drop-in replacement for segment-anything.

Use cases

• run segment anything faster on A100 GPUs
• batch offline inference for image segmentation
• drop-in replacement for segment-anything with speedups
• accelerate SAM with torch.compile and bfloat16
• segment large batches of images quickly
• optimize vision transformer inference with Triton kernels

When to choose

• you already use segment-anything and need faster batched inference on NVIDIA GPUs
• you can use PyTorch nightly and A100-class hardware
• you want automatic optimizations like compile, quantization, and sparsity

When to avoid

• you need training or fine-tuning rather than inference
• you are not on NVIDIA GPU hardware and cannot tolerate compile overhead
• you need the full upstream segment-anything feature set or long-term stability

Facets

library · maturity active

machine-learning llm-inference image-processing gpu-computing computer-vision image-processing deep-learning artificial-intelligence python cross-platform segment-anything sam image-segmentation torch-compile triton-kernels inference-optimization bfloat16 quantization gpu linux

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markdown · JSON · MCP: product_card(name="meta-pytorch/segment-anything-fast")