ppogg/YOLOv5-Lite

Health v2 · maintenance only

Adoption not part of the score

2487 stars · 430 forks observed · 2026-08-28

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

YOLOv5-Lite is a lightweight object detection model family evolved from YOLOv5, with models as small as ~900KB (int8) that run 10-15+ FPS on a Raspberry Pi 4B. It includes training code in PyTorch plus deployment support for inference backends like ncnn, MNN, TensorRT, OpenVINO, and TFLite on edge devices.

Use cases

• run object detection on a raspberry pi
• deploy a small yolo model on android
• detect objects in real time on embedded devices
• train a lightweight yolo model in pytorch
• convert a detection model to ncnn or mnn for mobile inference
• run yolo inference with tensorrt or openvino
• quantize a detection model to int8 for edge deployment

When to choose

• you need real-time object detection on CPU-only or low-power hardware like Raspberry Pi or Android phones
• model size and memory footprint are critical constraints
• you want prebuilt export paths to ncnn, MNN, TensorRT, OpenVINO, or TFLite

When to avoid

• you need maximum accuracy on high-end GPUs where full YOLOv5 or YOLOv8 is better
• you need segmentation, pose, or other vision tasks beyond bounding-box detection
• you require a permissive license since this is GPL-3.0

Facets

library · maturity active

machine-learning deep-learning computer-vision image-processing computer-vision machine-learning deep-learning embedded-systems cross-platform cpp python object-detection yolo yolov5 ncnn mnn tensorrt openvino tflite onnx raspberry-pi edge-deployment model-compression quantization lightweight-model shufflenet linux android

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markdown · JSON · MCP: product_card(name="ppogg/YOLOv5-Lite")