YoloV8-ncnn-Raspberry-Pi-4 and YoloFastestV2-ncnn-Raspberry-Pi-4

These are ecosystem siblings, representing two different object detection models, YOLOv8 and YoloFastestV2, both optimized by the same maintainer for deployment on Raspberry Pi hardware using the ncnn inference framework.

YoloV8-ncnn-Raspberry-Pi-4
40
Emerging
YoloFastestV2-ncnn-Raspberry-Pi-4
38
Emerging
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 14/25
Maintenance 0/25
Adoption 7/25
Maturity 16/25
Community 15/25
Stars: 118
Forks: 13
Downloads: —
Commits (30d): 0
Language: C++
License: BSD-3-Clause
Stars: 40
Forks: 7
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 YoloFastestV2-ncnn-Raspberry-Pi-4

Qengineering/YoloFastestV2-ncnn-Raspberry-Pi-4

YoloFastestV2 for a bare Raspberry Pi 4

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 YoloV6-ncnn-Raspberry-Pi-4 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 YoloV6-ncnn-Raspberry-Pi-4

Scores updated daily from GitHub, PyPI, and npm data. How scores work