Process For Annotation and Labeling
Collecting, Annotating and Labelling, Validating the volumes and volumes of images and videos
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:
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:
Training the Model
For training the model with the API, we first need to convert our data into the TFRecord format. This format basically takes your images and the yaml file of annotations and combines them into one that can be given as input for training. The starter code is provided on the tensorflow’s Github page.