# ermig1979/Simd

C++ image processing and machine learning library with using of SIMD: SSE, AVX, AVX-512, AMX  for x86/x64, NEON, SVE for ARM, HVX for Hexagon

Repository: https://github.com/ermig1979/Simd
Canonical: https://ross.abutalabs.com/products/simd
Homepage: http://ermig1979.github.io/Simd
Language: C++
License: MIT
License Family: permissive
Topics: simd, avx, neon, image-processing, c-plus-plus, simd-library, arm, lbp, haar-cascade, avx512, machine-learning, neural-network, amx, sse, hexagon, hvx, sve
Last push: 2026-08-26T12:22:54+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 96, longevity 100
- inputs: {"age_days": 4179, "days_push": 7, "days_rel": 30, "gap_med": 30.0, "n_releases_24m": 23}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2265, forks 453 (observed 2026-08-28T04:06:32.616880+00:00)

## What it is
Simd Library is a free open-source C++ image processing and machine learning library with a C API and Python wrapper. Its algorithms are hand-optimized with SIMD CPU extensions including SSE, AVX, AVX-512, AMX (x86/x64), NEON, SVE/SVE2 (ARM), and HVX (Hexagon).

## Use cases
- accelerate image scaling, filtering, and pixel format conversion with SIMD
- detect motion in video streams
- perform object detection with HAAR and LBP cascades
- run neural network inference on CPU with SIMD optimizations
- extract statistical information from images
- optimize C++ image pipelines for x86, ARM, and Hexagon targets

## When to choose
- you need maximum CPU performance for image processing in C/C++
- you target multiple architectures (x86/x64, ARM, Hexagon) with SIMD-optimized code
- you need lightweight CPU-based object detection or neural network inference without heavy frameworks

## When to avoid
- you need GPU acceleration
- you want a high-level Python-first computer vision framework
- you need a broad ecosystem of pretrained models and community tooling like OpenCV

## Facets
- artifact type: library
- maturity: active
- function: image-processing, machine-learning, computer-vision, deep-learning
- domain: image-processing, computer-vision, machine-learning, performance
- platform: windows, cpp, python, cross-platform
- tags: simd, sse, avx, avx-512, amx, neon, sve, hexagon-hvx, haar-cascade, object-detection, motion-detection, neural-network-inference, c-api, linux

## Member repositories
- ermig1979/Simd (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:32.616880+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:42:24.403203+00:00, confidence not recorded.
  - readme: https://github.com/ermig1979/Simd (fetched 2026-08-28T04:06:32.616880+00:00, sha 1070f2147a05)
  - homepage: http://ermig1979.github.io/Simd (fetched 2026-08-29T10:22:39.028133+00:00, sha 5f57fccb4749)
- Data as of 2026-08-30T08:39:29.467469+00:00.