Tensorflow version of SCNN in CULane.

Installation

conda create -n tensorflow_gpu pip python=3.5
source activate tensorflow_gpu
pip install --upgrade tensorflow-gpu==1.3.0
pip3 install -r lane-detection-model/requirements.txt 

Download VGG-16

Download the vgg.npy here and put it in lane-detection-model/data.

Pre-trained model for testing

Download the pre-trained model here.

Test

cd lane-detection-model
CUDA_VISIBLE_DEVICES="0" python tools/test_lanenet.py --weights_path path/to/model_weights_file --image_path path/to/image_name_list

Note that path/to/image_name_list should be like test_img.txt. Now, you get the probability maps from our model. To get the final performance, you need to follow SCNN to get curve lines from probability maps as well as calculate precision, recall and F1-measure.

Train

cd lane-detection-model
CUDA_VISIBLE_DEVICES="0" python tools/train_lanenet.py --net vgg --dataset_dir path/to/CULane-dataset/

Note that path/to/CULane-dataset/ should contain files like train_gt.txt and val_gt.txt.