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carnd-t3-p2-Semantic-Segmentation

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added results

xxmfcx committed 7 years ago
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epoch size reduced to 20

xxmfcx committed 7 years ago
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finished

xxmfcx committed 7 years ago
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indent size reduced to 2

xxmfcx committed 7 years ago
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formatting

xxmfcx committed 7 years ago
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initial real

xxmfcx committed 7 years ago

README

The README file for this repository.

Semantic Segmentation

Introduction

In this project, you'll label the pixels of a road in images using a Fully Convolutional Network (FCN).

Results:

https://photos.app.goo.gl/3hWEXIKm1GFOoRZd2

Setup

Frameworks and Packages

Make sure you have the following is installed:

Dataset

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.

Start

Implement

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

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.

Submission

  1. Ensure you've passed all the unit tests.
  2. Ensure you pass all points on the rubric.
  3. 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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