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Drivable Area Detection On Bdd100K Val
Metrics
Params (M)
mIoU
Results
Performance results of various models on this benchmark
| Paper Title | Repository | |||
|---|---|---|---|---|
| YOLOPv2 | 38.9 | 93.2 | YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception | |
| TwinLiteNetPlus-Large | 1.94 | 92.9 | TwinLiteNetPlus: A Real-Time Multi-Task Segmentation Model for Autonomous Driving | |
| TwinLiteNetPlus-Medium | 0.48 | 92.0 | TwinLiteNetPlus: A Real-Time Multi-Task Segmentation Model for Autonomous Driving | |
| YOLOP | 7.9 | 91.5 | YOLOP: You Only Look Once for Panoptic Driving Perception | |
| TwinLiteNet | 0.43 | 91.3 | TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars | |
| A-YOLOM(s) | - | 91 | You Only Look at Once for Real-time and Generic Multi-Task | |
| TwinLiteNetPlus-Small | 0.12 | 90.6 | TwinLiteNetPlus: A Real-Time Multi-Task Segmentation Model for Autonomous Driving | |
| HybridNets | 12.8 | 90.5 | HybridNets: End-to-End Perception Network | |
| TwinLiteNetPlus-Nano | 0.03 | 87.3 | TwinLiteNetPlus: A Real-Time Multi-Task Segmentation Model for Autonomous Driving |
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