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Models/Yolov8-nano

Yolov8-nano

Reported on 6 benchmarks across 6 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology4 results

3DonMSCOCO
AP 0.5· uses extra data· 2023-10-31
47.2
best: 50.3 (Cooperative Foundational Models)
YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes arXiv:2311.00073
2D ClassificationonMSCOCO
AP 0.5· uses extra data· 2023-10-31
47.2
best: 50.3 (Cooperative Foundational Models)
YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes arXiv:2311.00073
2D Object DetectiononMSCOCO
AP 0.5· uses extra data· 2023-10-31
47.2
best: 50.3 (Cooperative Foundational Models)
YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes arXiv:2311.00073
16konMSCOCO
AP 0.5· uses extra data· 2023-10-31
47.2
best: 50.3 (Cooperative Foundational Models)
YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes arXiv:2311.00073

Computer Vision2 results

Object DetectiononMSCOCO
AP 0.5· uses extra data· 2023-10-31
47.2
best: 50.3 (Cooperative Foundational Models)
YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes arXiv:2311.00073
Open Vocabulary Object DetectiononMSCOCO
AP 0.5· uses extra data· 2023-10-31
47.2
best: 50.3 (Cooperative Foundational Models)
YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes arXiv:2311.00073