YoloFastestV2-ncnn-Raspberry-Pi-4 and YoloX-Tracking-ncnn-RPi_64-bit
These are competitors: both provide optimized YOLO models with ncnn for a bare Raspberry Pi 4, but one offers tracking and a newer YoloX model while the other focuses on the YoloFastestV2 architecture.
About YoloFastestV2-ncnn-Raspberry-Pi-4
Qengineering/YoloFastestV2-ncnn-Raspberry-Pi-4
YoloFastestV2 for a bare Raspberry Pi 4
This project helps you identify and locate multiple objects within images or video feeds using a Raspberry Pi 4. It takes visual data as input and outputs bounding boxes around detected objects with their labels. This is ideal for hobbyists, educators, or small-scale automation enthusiasts building custom vision systems on low-cost hardware.
About YoloX-Tracking-ncnn-RPi_64-bit
Qengineering/YoloX-Tracking-ncnn-RPi_64-bit
YoloX with tracking for a bare Raspberry Pi 4 using ncnn.
This project helps you accurately track multiple individual objects moving within a video stream, even when they temporarily block each other. It takes a video file or live camera feed as input and outputs the same video with bounding boxes and unique IDs for each detected object, allowing you to follow their paths over time. It's designed for engineers or hobbyists building custom computer vision applications on a Raspberry Pi 4.
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