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.
No commits in the last 6 months.
Use this if you need to identify and track objects in real-time using a Raspberry Pi 4 for applications like security monitoring, robotics, or basic automation.
Not ideal if you require extremely high accuracy for very small objects or if you need to run complex deep learning models that demand significantly more processing power than a Raspberry Pi 4 can provide.
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97
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20
Language
C++
License
BSD-3-Clause
Category
Last pushed
Jun 04, 2024
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