Process For Annotation and Labeling

Collecting, Annotating and Labelling, Validating the volumes and volumes of images and videos

With the recent launch of the self driving cars and trucks, the field of autonomous navigation has never been more exciting. What were once research projects in laboratories are now commercially available products. One of the main tasks that any such vehicle must perform well is the task of following the rules of the road. Identifying the traffic lights in the midst of everything is the one of the most important tasks.
One of the main tasks that any such vehicle must perform well is the task of following the rules of the road. Identifying the traffic lights in the midst of everything is the one of the most important tasks.
Thankfully due to the recent advancements in Deep Learning and the ease of use of different Deep Learning Frameworks like Caffe and TensorFlow that can utilize the immense power of GPUs to speed up the computations, this task has become really simple.

Process

01.

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:

02.

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:

03.

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.