- Passed tests.test_load_vgg(load_vgg, tf)
- Passed tests.test_layers(layers)
- Passed tests.test_optimize(optimize)
- Passed tests.test_train_nn(train_nn)
- On average, the model decreases loss over time.
- The number of epoch equals 50 and batch size equals 2 (since any larger number result in out of memory error on the AWS GPU instance).
- Inference result on three test images:
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.
- 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 (all images from the most recent run)
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