Qengineering/YoloV6-ncnn-Raspberry-Pi-4
YoloV6 for a bare Raspberry Pi using ncnn.
This project helps embedded systems developers deploy object detection capabilities on resource-constrained devices like the Raspberry Pi 4. It takes real-time video feeds or images and outputs bounding boxes around detected objects. This is ideal for developers building IoT solutions, surveillance systems, or robotics applications that need on-device visual processing.
No commits in the last 6 months.
Use this if you need to run efficient object detection directly on a Raspberry Pi 4, without relying on cloud processing or more powerful hardware.
Not ideal if you require extremely high frame rates or complex, multi-object tracking on a Raspberry Pi, as performance might be a limiting factor.
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11
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4
Language
C++
License
BSD-3-Clause
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Last pushed
Jun 12, 2024
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