10. Object Detection Lab

This optional lab exercise is an opportunity to practice rapid object detection suitable for implementation in autonomous vehicles. The current lab is focused on a general understanding of used SSD for object detection, as applied to detecting other vehicles from a driving video. The proficiency and understanding developed in this lab can ultimately be used to detect other relevant objects, such as Traffic lights.

In lab this you will:

  • Learn about MobileNets and separable depthwise convolutions.
  • The SSD (Single Shot Detection) architecture used for object detection
  • Use pretrained TensorFlow object detection inference models to detect objects
  • Use different architectures and weigh the tradeoffs.
  • Apply an object detection pipeline to a video.

Clone the GitHub repository at https://github.com/udacity/CarND-Object-Detection-Lab , open the notebook and work through it!

Requirements

Install environment with Anaconda : 

conda env create -f environment.yml

Change TensorFlow pip installation from tensorflow-gpu to tensorflow if you don't have a GPU available.

The environment should be listed via conda info --envs : 

# conda environments:
#
carnd-advdl-odlab        /usr/local/anaconda3/envs/carnd-advdl-odlab
root                  *  /usr/local/anaconda3

Further documentation on working with Anaconda environments .

Particularly useful sections:

https://conda.io/docs/using/envs.html#change-environments-activate-deactivate
https://conda.io/docs/using/envs.html#remove-an-environment

Resources

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