In this project the pixels of a road in images are labelled using a Fully Convolutional Network (FCN). The network uses the architecture described in Long et al. and is trained on the Kitti Road dataset.
Some of the results are shown below:
The code performs a hyperparameter search using 200 epochs for training each network. A test of the trained network on road conditions very different to the training data can be found here project_video
Make sure you have the following is installed:
Download the Kitti Road dataset from here. Extract the dataset in the data
folder. This will create the folder data_road
with all the training a test images.
Run the following command to run the project:
python main.py
Note If running this in Jupyter Notebook system messages, such as those regarding test status, may appear in the terminal rather than the notebook.
- Ensure you've passed all the unit tests.
- Ensure you pass all points on the rubric.
- Submit the following in a zip file.
helper.py
main.py
project_tests.py
- Newest inference images from
runs
folder