Detecting objects with MXNet – Faster R-CNN and YOLO
In this recipe, we will see how to use MXNet and GluonCV on a pre-trained model to detect objects from a dataset. We will see how to use GluonCV Model Zoo with two very important models for object detection – Faster R-CNN and YOLOv3.
In this recipe, we will compare the performance of these two pre-trained models to detect objects on the Penn-Fudan Pedestrians dataset.
Getting ready
As for previous chapters, in this recipe, we will be using a few matrix operations and linear algebra, but it will not be too difficult.
As we will unpack in this recipe, object detection combines classification and regression, and therefore, chapters and recipes where we explored the foundations of these topics are recommended to revisit. Furthermore, we will be detecting objects on image datasets. This recipe will combine what we learned in the following chapters:
- Understanding image datasets: load, manage, and visualize...
