CarND Object Detection Lab

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.

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

• TensorFlow object detection model zoo
• Driving video

Tips

• Some windows users have reported the driving video as playable only in Jupyter Notebook operating in Chrome browser, and not in media player or Jupyter Notebook operating in other browsers. In contrast the post-segmentation video appears to be operating across players and browsers.