Step 1: Gather the data

As with any machine learning exercise, we first need to gather our data on which we will train the model. The simulator images look something like this:

Real data image:

Step 2: Label and annotate the images

The next step is to manually annotate the images for the network. There are many open source tools available for this like LabelImg, Sloth, etc. The annotation tools create a yaml file that looks something like this:

1. Do git clone https://github.com/tensorflow/models.git inside the tensorflow directory.

2. We will work with python 2, so activate respective virtual environment.

3. Follow the instructions at this page for installing some simple dependencies.

4. Go to research directory - cd tensorflow/models/research/ and run following commands:

   python setup.py build
   python setup.py install
   python slim/setup.py build
   python slim/setup.py install
   protoc object_detection/protos/*.proto --python_out=.
   export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim

5. Now that installation is done, test it:

   python object_detection/builders/model_builder_test.py

python data_conversion_udacity_sim.py --output_path data/sim_data.record

python data_conversion_udacity_real.py --output_path data/real_data.record

Credits

To save the time and effort, I followed the post from https://becominghuman.ai/@Vatsal410 and https://medium.com/@anthony_sarkis, where they have shared shared his annotated data-set openly available for all to use. Thank you to them for that.