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).
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):