dog-qiuqiu/MobileNet-Yolo

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1744 stars · 278 forks observed · 2026-08-28

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

A collection of ultra-lightweight YOLOv3-based object detection models (MobileNetV2-YOLOv3-Lite/Nano, YoloFace) designed for mobile and embedded inference with NCNN and MNN. It includes Darknet training configurations, pre-trained weights, and C samples for detection and landmark tasks.

Use cases

• run real-time object detection on mobile phones
• deploy a tiny face detection model under 500KB
• train a lightweight YOLOv3 model in Darknet and deploy with NCNN
• detect human pose keypoints on edge devices
• benchmark small detection models on ARM CPUs
• build an embedded camera app with low-FLOPS detection

When to choose

• you need extremely small and fast object detection models for mobile or embedded ARM devices
• you want NCNN or MNN deployment of YOLO-style detectors
• you need a minimal face detection model with tiny weight size

When to avoid

• you need the latest state-of-the-art detection models, since the project is no longer updated and points to Yolo-Fastest
• you require GPU training with Darknet group convolutions on Pascal GPUs, which is known to be problematic
• you need a maintained project with active support and recent releases

Facets

library · maturity abandoned

machine-learning computer-vision image-processing computer-vision deep-learning mobile-development cross-platform cpp python object-detection yolov3 mobilenetv2 face-detection ncnn mnn darknet lightweight-models edge-inference landmark-detection android linux

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