YoloV8-ncnn-Raspberry-Pi-4 and YoloV6-ncnn-Raspberry-Pi-4
These are competitors: both provide optimized YOLO object detection models for bare Raspberry Pi devices using the ncnn neural network inference framework, but for different versions of the YOLO algorithm (v8 vs. v6).
Maintenance
0/25
Adoption
10/25
Maturity
16/25
Community
14/25
Maintenance
0/25
Adoption
5/25
Maturity
9/25
Community
15/25
Stars: 118
Forks: 13
Downloads: —
Commits (30d): 0
Language: C++
License: BSD-3-Clause
Stars: 11
Forks: 4
Downloads: —
Commits (30d): 0
Language: C++
License: BSD-3-Clause
Stale 6m
No Package
No Dependents
Stale 6m
No Package
No Dependents
About YoloV8-ncnn-Raspberry-Pi-4
Qengineering/YoloV8-ncnn-Raspberry-Pi-4
YoloV8 for a bare Raspberry Pi 4 or 5
About YoloV6-ncnn-Raspberry-Pi-4
Qengineering/YoloV6-ncnn-Raspberry-Pi-4
YoloV6 for a bare Raspberry Pi using ncnn.
Related comparisons
YoloV8-ncnn-Raspberry-Pi-4 and YoloV7-ncnn-Raspberry-Pi-4
YoloV8-ncnn-Raspberry-Pi-4 and YoloX-Tracking-ncnn-RPi_64-bit
YoloV8-ncnn-Raspberry-Pi-4 and YoloFastestV2-ncnn-Raspberry-Pi-4
YoloV8-ncnn-Raspberry-Pi-4 and YoloX-Tracking-ncnn-RPi_64-bit
YoloV8-ncnn-Raspberry-Pi-4 and YoloFastestV2-ncnn-Raspberry-Pi-4
YoloV8-ncnn-Raspberry-Pi-4 and YoloV5-ncnn-Raspberry-Pi-4
Scores updated daily from GitHub, PyPI, and npm data. How scores work