ENet-Label-Torch is available now (a light-weight and effective lane detection model)
cardwing opened this issue · 0 comments
cardwing commented
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 |