In this tutorial, we will learn the workings of Luminoth by using it in practice to solve a real world object detection problem.
As our case study, we will be building a model able to recognize cars, pedestrians, and other objects which a self-driving car would need to detect in order to properly function. We will have our model ready for that and see it how to apply it to images and video. We will not, however, add any tracking capabilities.
To follow along easier and not invest many hours each time we want to run the training process, we will build a small toy dataset and show how things go from there, giving tips on the things you need to look at when training a model with a larger dataset.
First, check the :ref:`usage/installation` section and make sure you have a working install.
.. toctree:: :maxdepth: 2 01-first-steps 02-building-custom-traffic-dataset 03-training-the-model 04-visualizing-the-training-process 05-evaluating-models 06-creating-own-checkpoints 07-using-luminoth-from-python