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.
Run the following file to run the project in jupyter notebook:
FCN.ipynb
https://github.com/udacity/CarND-Object-Detection-Lab
name: carnd-advdl-odlab
channels:
- https://conda.anaconda.org/menpo
- conda-forge
dependencies:
- python==3.6
- numpy
- matplotlib
- jupyter
- pillow
- scipy
- ffmpeg
- imageio==2.1.2
- pip:
- moviepy
- tensorflow-gpu==1.2
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
FCN.ipynb
project_tests.py
- Newest inference images from
runs
folder (all images from the most recent run)
in my case, the most recent data is put into 1511221281.479869 folder.