Pytorch implementation of "Learning Lightweight Lane Detection CNNs by Self Attention Distillation (ICCV 2019)"

Demo trained with CULane dataset & tested with \driver_193_90frame\06051123_0635.MP4

gpu_runtime: 0.016253232955932617 FPS: 61

total_runtime: 0.017553091049194336 FPS: 56 on RTX 2080 TI

Category	40k episode (before SAD)	60k episode (after SAD)
Image
Lane

• pytorch
• tensorflow (for tensorboard)
• opencv

• CULane

  CULane dataset path (click to expand)

  CULane_path
  ├─ driver_100_30frame
  ├─ driver_161_90frame
  ├─ driver_182_30frame
  ├─ driver_193_90frame
  ├─ driver_23_30frame
  ├─ driver_37_30frame
  ├─ laneseg_label_w16
  ├─ laneseg_label_w16_test
  └─ list
  

• TuSimple

  TuSimple dataset path (click to expand)

  Tusimple_path
  ├─ clips
  ├─ label_data_0313.json
  ├─ label_data_0531.json
  ├─ label_data_0601.json
  └─ test_label.json
  

• BDD100K

  • Download modified labels and lists generated by author's method (train, val and list) and put the BDD100K dataset in the desired folder.
  BDD100K dataset path (click to expand)

  BDD100K_path
  ├─ images
  │  ├─ 10k
  │  └─ 100k
  ├─ list
  │  ├─ test_gt_bdd.txt
  │  ├─ train_gt_bdd.txt
  │  └─ val_gt_bdd.txt
  ├─ train_label
  │  └─ final_train
  └─ val_label
     └─ final_val
  

• VPGNet

You need to change the correct dataset path in ./config.py

Dataset_Path = dict(
    CULane = "/workspace/CULANE_DATASET",
    Tusimple = "/workspace/TUSIMPLE_DATASET",
    BDD100K = "/workspace/BDD100K_DATASET",
    VPGNet = "/workspace/VPGNet_DATASET"
)

First, change some hyperparameters in ./experiments/*/cfg.json

{
  "model": "enet_sad",               <- "scnn" or "enet_sad"
  "dataset": {
    "dataset_name": "CULane",        <- "CULane" or "Tusimple"
    "batch_size": 12,
    "resize_shape": [800, 288]       <- [800, 288] with CULane, [640, 368] with Tusimple, and [640, 360] with BDD100K
                                        This size is defined in the ENet-SAD paper, any size is fine if it is a multiple of 8.
  },
  ...
}

And then, start training with train.py

python train.py --exp_dir ./experiments/exp1

If you write your own code using ENet-SAD model, you can init ENet_SAD with some parameters.

class ENet_SAD(nn.Module):
    """
    Generate the ENet-SAD model.
    Keyword arguments:
    - input_size (tuple): Size of the input image to segment.
    - encoder_relu (bool, optional): When ``True`` ReLU is used as the
    activation function in the encoder blocks/layers; otherwise, PReLU
    is used. Default: False.
    - decoder_relu (bool, optional): When ``True`` ReLU is used as the
    activation function in the decoder blocks/layers; otherwise, PReLU
    is used. Default: True.
    - sad (bool, optional): When ``True``, SAD is added to model;
    otherwise, SAD is removed. Default: False.
    - weight_share (bool, optional): When ``True``, weights are shared
    with encoder blocks (E2, E3 and E4); otherwise, encoders will each
    learn with their own weights. Default: True.
    """

Will continue to be updated.

• TuSimple dataset

  Category	ENet-SAD Pytorch	ENet-SAD paper
  Accuracy	93.93%	96.64%
  FP	0.2279	0.0602
  FN	0.0838	0.0205

• CULane dataset (F1-measure, FP measure for crossroad)

  Category	ENet-SAD Pytorch	ENet-SAD paper
  Normal	86.8	90.1
  Crowded	65.3	68.8
  Night	54.0	66.0
  No line	37.3	41.6
  Shadow	52.4	65.9
  Arrow	78.2	84.0
  Dazzle light	51.0	60.2
  Curve	58.6	65.7
  Crossroad	2278	1995
  Total	65.5	70.8

• BDD100K

  Category	ENet-SAD Pytorch	ENet-SAD paper
  Accuracy	37.09%	36.56%
  IoU	15.14	16.02

This repo is built upon official implementation ENet-SAD and based on PyTorch-ENet, SCNN_Pytorch.