We started the final chapter of this book by learning about SVM models and how to train them to classify groups of similar data. We learned how SVM can be used in conjunction with the HOG descriptor to learn about one or more specific objects and then detect and classify them in new images. After learning about SVM models, we moved on to using ANN models, which offer much more power in cases where we have multiple columns in both the input and output of the training samples. This chapter also included a complete guide on how to train and use Haar and LBP cascade classifiers. We are now familiar with the usage of official OpenCV tools that can be used to prepare a training dataset from scratch and then train a cascade classifier using that dataset. Finally, we ended this chapter and this book by learning about the usage of pre-trained deep learning object detection models in OpenCV.