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Lane Detection On Bdd100K Val

Métriques

Accuracy (%)
IoU (%)

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Accuracy (%)
IoU (%)
Paper TitleRepository
Enet-SAD36.616.02Learning Lightweight Lane Detection CNNs by Self Attention Distillation
YOLOPv287.827.25YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
TwinLiteNetPlus-Small75.829.3TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation
TwinLiteNetPlus-Nano70.223.3TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation
TwinLiteNet77.831.08TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars
A-YOLOM(s)84.928.8You Only Look at Once for Real-time and Generic Multi-Task
TwinLiteNetPlus-Large81.934.2TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation
TwinLiteNetPlus-Medium79.132.3TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation
YOLOP70.526.2YOLOP: You Only Look Once for Panoptic Driving Perception
HybridNets85.431.6HybridNets: End-to-End Perception Network
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