The training samples consist of three components, a binary segmentation label file, a instance segmentation label file and the original image. The binary segmentation uses 255 to represent the lane field and 0 for the rest. The instance use different pixel value to represent different lane field and 0 for the rest.
Image scaling will be done according to config file in similar manner for all images.
Generating tensorflow records file
python tools/make_tusimple_tfrecords.py
Set the path till "dataset\train_set" in config.py file
"dataset\train_set" directory should consist of following files:
"train_img" - Folder containing all the ground truth images
"train_seg_img" - Folder containing all the binary label images
"val_img" - Folder containing all the ground truth images
"val_seg_img" - Folder containing all the binary label images
txt_file_gen_SCNN.py
- generate txt files from image dataset, saved in dataset/train_set/seg_label/list
"train_gt.txt"
"val_gt.txt"
"test_gt.txt"
- tools/train.py --exp_dir ./experiments/exp0
saved models - \experiments\exp0\
Number of epochs
- \experiments\exp0\cfg.json
"MAX_EPOCHES": 60
python tools/train_lanenet_tusimple.py
- \tools\demo_test.py -i E:\Abhishek\Lane_Detection\CULane\parth\SCNN\SCNN_Pytorch-master\demo\demo.jpg -w E:\Abhishek\Lane_Detection\CULane\parth\SCNN\SCNN_Pytorch-master\experiments\exp0\exp0_best.pth
- EVALUATE ON CUSTOM TEST DATASET :
test_tusimple.py --exp_dir ./experiments/exp0 (keep one category of test data at a time)
- \dataset\Evaluate\
- Generate csv files with results
