In the previous section, we discussed the concepts of the model and algorithm we will be using in this chapter. In this section, we will be moving one step closer to realizing the example project for this chapter by converting a trained Keras model of Tiny YOLO to Core ML using Apple's Core ML Tools Python package; but, before doing so, we will quickly discuss the model and the data it was trained on.
YOLO was conceived on a neural network framework called darknet, which is currently not supported by the default Core ML Tools package; fortunately, the authors of YOLO and darknet have made the architecture and weights of the trained model publicly available on their website at https://pjreddie.com/darknet/yolov2/. There are a few variations of YOLO that have been trained on either the dataset from Common Objects in Context (COCO), which consists of 80 classes, or The PASCAL Visual Object Classes (VOC) Challenge 2007, which consists of 20 classes.
