Lane Detection on BDD100K val

Metric: Params (M) (higher is better)

LeaderboardDataset

Loading chart...

Results

Submit a result

Sort:

#	Model↕	Params (M)▼	Extra Data	Paper	Date↕	Code
1	YOLOPv2	38.9	No	YOLOPv2: Better, Faster, Stronger for Panoptic D...	2022-08-24	Code
2	HybridNets	12.8	No	HybridNets: End-to-End Perception Network	2022-03-17	Code
3	YOLOP	7.9	No	YOLOP: You Only Look Once for Panoptic Driving P...	2021-08-25	Code
4	TriLiteNet-base	2.35	No	-	-	Code
5	TwinLiteNetPlus-Large	1.94	No	TwinLiteNetPlus: A Stronger Model for Real-time ...	2024-03-25	Code
6	TwinLiteNetPlus-Medium	0.48	No	TwinLiteNetPlus: A Stronger Model for Real-time ...	2024-03-25	Code
7	TwinLiteNet	0.43	No	TwinLiteNet: An Efficient and Lightweight Model ...	2023-07-20	Code
8	TwinLiteNetPlus-Small	0.12	No	TwinLiteNetPlus: A Stronger Model for Real-time ...	2024-03-25	Code
9	TwinLiteNetPlus-Nano	0.03	No	TwinLiteNetPlus: A Stronger Model for Real-time ...	2024-03-25	Code

#1YOLOPv2SOTA
38.9
Params (M)· 2022-08-24
YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception Code
#2HybridNetsSOTA
12.8
Params (M)· 2022-03-17
HybridNets: End-to-End Perception Network Code
#3YOLOPSOTA
7.9
Params (M)· 2021-08-25
YOLOP: You Only Look Once for Panoptic Driving Perception Code
#4TriLiteNet-base
2.35
Params (M)
No paperCode
#5TwinLiteNetPlus-Large
1.94
Params (M)· 2024-03-25
TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation Code
#6TwinLiteNetPlus-Medium
0.48
Params (M)· 2024-03-25
TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation Code
#7TwinLiteNet
0.43
Params (M)· 2023-07-20
TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars Code
#8TwinLiteNetPlus-Small
0.12
Params (M)· 2024-03-25
TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation Code
#9TwinLiteNetPlus-Nano
0.03
Params (M)· 2024-03-25
TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation Code