dog-qiuqiu/Yolo-Fastest

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

2103 stars · 434 forks observed · 2026-08-28

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

Yolo-Fastest is an ultra-lightweight YOLO-based object detection algorithm and model zoo, with only ~250 MFLOPs and a 666KB ncnn model. It includes a modified darknet training framework and is optimized for real-time inference on ARM mobile and embedded devices like Raspberry Pi.

Use cases

• run real-time object detection on a Raspberry Pi
• deploy a tiny yolo model on Android mobile devices
• detect objects on embedded boards with very limited compute
• train a lightweight custom object detector with darknet
• run single object detection at 30fps on edge hardware

When to choose

• you need the smallest, fastest object detection model for resource-constrained devices
• you target ARM mobile or embedded inference with ncnn
• simple single-class or single-object detection is sufficient

When to avoid

• you need high accuracy on complex multi-object scenes (mAP is only ~24-34%)
• you need CPU inference via darknet, which is not optimized
• you need a well-maintained project - the successor Yolo-FastestV2 is recommended instead

Facets

library · maturity maintenance

machine-learning computer-vision deep-learning llm-inference computer-vision machine-learning embedded-systems deep-learning cross-platform embedded cpp python object-detection yolo ncnn ultra-lightweight edge-devices real-time darknet model-zoo android linux gpu

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