YoloV7-ncnn-Raspberry-Pi-4 and YoloX-Tracking-ncnn-RPi_64-bit
These are competitor tools, as they are both distinct neural network architectures (YoloV7 and YoloX) implemented with ncnn for object detection on a bare Raspberry Pi 4.
About YoloV7-ncnn-Raspberry-Pi-4
Qengineering/YoloV7-ncnn-Raspberry-Pi-4
YoloV7 for a bare Raspberry Pi using ncnn.
This project helps operations engineers and hobbyists perform real-time object detection on live video feeds or image files using a Raspberry Pi 4. It takes a visual input, like a camera feed or image, and outputs identified objects within that visual, such as cars or people, at decent speeds directly on the device. It's designed for users who want to deploy computer vision solutions on cost-effective, embedded 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.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work