Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

7542 stars · 1548 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

An ultra-lightweight face detection model (~1MB FP32, ~300KB quantized) designed for edge computing devices, with slim and RFB variants trained on WIDER FACE. It ships pretrained weights plus inference code for PyTorch, ONNX, NCNN, MNN, Caffe, and OpenCV DNN.

Use cases

• detect faces in images on mobile or embedded devices
• run face detection on ARM boards with limited memory
• export a small face detection model to onnx for deployment
• quantize a face detector to int8 for faster inference
• add face detection to a camera app on android
• run face detection in c++ with ncnn or mnn

When to choose

• you need a tiny, fast face detector for edge or mobile hardware
• you want pretrained weights with multiple inference framework backends
• model size and flops matter more than state-of-the-art accuracy

When to avoid

• you need landmark detection, recognition, or attributes beyond face boxes
• you need the highest accuracy on hard faces and can afford larger models
• you need actively maintained code with recent updates

Facets

library · maturity maintenance

computer-vision machine-learning image-processing llm-inference computer-vision machine-learning embedded-systems cross-platform python cpp windows embedded cross-platform face-detection lightweight-model edge-computing onnx ncnn mnn caffe int8-quantization widerface pretrained-model linux macos android

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