In this project, you'll label the pixels of a road in images using a Fully Convolutional Network (FCN).
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.
Implement the code in the main.py
module indicated by the "TODO" comments.
The comments indicated with "OPTIONAL" tag are not required to complete.
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.
Submit the following in a zip file.
helper.py
main.py
project_tests.py
runs
folder (all images from the most recent run)VGG16
model is hardcoded into helper.py
. The model can be found hereVGG16
, but a fully convolutional version, which already contains the 1x1 convolutions to replace the fully connected layers. Please see this forum post for more information. A summary of additional points, follow. tf.layers
is not enough. Regularization loss terms must be manually added to your loss function. otherwise regularization is not implemented.If you are unfamiliar with GitHub , Udacity has a brief GitHub tutorial to get you started. Udacity also provides a more detailed free course on git and GitHub.
To learn about REAMDE files and Markdown, Udacity provides a free course on READMEs, as well.
GitHub also provides a tutorial about creating Markdown files.