ENet-Label-Torch is available now (a light-weight and effective lane detection model)

[cardwing]

Our ENet-Label-Torch has been released. More details can be found in my repo.

Key features:

(1) ENet-label is a light-weight lane detection model based on ENet and adopts self attention distillation (more details can be found in our paper which will be published soon).

(2) It has 20 × fewer parameters and runs 10 × faster compared to the state-of-the-art SCNN, and achieves 72.0 (F1-measure) on CULane testing set (better than SCNN which achieves 71.6).

(Do not hesitate to try our model!!!)

Performance on CULane testing set (F1-measure):

Category	SCNN-Torch	SCNN-Tensorflow	ENet-Label-Torch
Normal	90.6	90.2	90.7
Crowded	69.7	71.9	70.8
Night	66.1	64.6	65.9
No line	43.4	45.8	44.7
Shadow	66.9	73.8	70.6
Arrow	84.1	83.8	85.8
Dazzle light	58.5	59.5	64.4
Curve	64.4	63.4	65.4
Crossroad	1990	4137	2729
Total	71.6	71.3	72.0
Runtime(ms)	133.5	--	13.4
Parameter(M)	20.72	--	0.98