ERFNet-CULane-PyTorch

Requirements

• PyTorch 0.3.0.
• Matlab (for tools/prob2lines), version R2014a or later.
• Opencv (for tools/lane_evaluation), version 2.4.8 (later 2.4.x should also work).

Before Start

Please follow list to put CULane in the desired folder. We'll call the directory that you cloned ERFNet-CULane-PyTorch as $ERFNet_ROOT.

Testing

1. Download our trained models to ./trained

   cd $ERFNet_ROOT/trained

   The trained model has already been there.

2. Run test script

   cd $ERFNet_ROOT
   sh ./test_erfnet.sh

   Testing results (probability map of lane markings) are saved in experiments/predicts/ by default.

3. Get curve line from probability map

   cd tools/prob2lines
   matlab -nodisplay -r "main;exit"  # or you may simply run main.m from matlab interface

   The generated line coordinates would be saved in tools/prob2lines/output/ by default.

4. Calculate precision, recall, and F-measure

   cd $ERFNet_ROOT/tools/lane_evaluation
   make
   sh Run.sh   # it may take over 30min to evaluate

   Note: Run.sh evaluate each scenario separately while run.sh evaluate the whole. You may use calTotal.m to calculate overall performance from all senarios.
   By now, you should be able to reproduce the result (F1-measure: 73.1).

Training

1. Download the pre-trained model

   cd $ERFNet_ROOT/pretrained

   The pre-trained model has already been there.
2. Training ERFNet model

   cd $ERFNet_ROOT
   sh ./train_erfnet.sh

   The training process should start and trained models would be saved in trained by default.
   Then you can test the trained model following the Testing steps above. If your model position or name is changed, remember to set them to yours accordingly.