RangiLyu/nanodet

Health v2 · maintenance only

Adoption not part of the score

6252 stars · 1118 forks observed · 2026-08-28

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

NanoDet-Plus is a super fast, lightweight anchor-free object detection model implemented in PyTorch, with model sizes as small as 980KB (INT8) and real-time performance on mobile ARM CPUs. It supports easy deployment to multiple inference backends including ncnn, MNN, and OpenVINO, with an Android demo included.

Use cases

• run real-time object detection on mobile devices
• deploy a tiny object detection model on edge hardware
• train a lightweight anchor-free detector on limited GPU memory
• detect objects in images with a small footprint model
• convert a detection model to ncnn or OpenVINO for deployment
• build an Android app with on-device object detection
• benchmark lightweight object detection models on COCO

When to choose

• you need real-time object detection on mobile or embedded ARM CPUs
• model size and latency are critical constraints
• you want an easy-to-deploy detector with ncnn/MNN/OpenVINO support
• you have limited GPU memory for training a detection model

When to avoid

• you need maximum accuracy and can afford large models like YOLO-X or DINO
• you need segmentation, keypoints, or other tasks beyond bounding-box detection
• you only run inference on high-end servers where model size doesn't matter
• you need frequent updates or active community support

Facets

library · maturity stable

machine-learning deep-learning computer-vision image-processing deep-learning computer-vision image-processing mobile-development python cpp cross-platform object-detection anchor-free lightweight-models edge-deployment ncnn on-device-inference pytorch model-zoo real-time-inference android gpu

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