Segmenting objects in images with MXNet – PSPNet and DeepLab-v3
In this recipe, we will see how to use MXNet and GluonCV on a pre-trained model, segmenting objects in images from a dataset. This means that we will be able to split objects into different classes, such as person, cat, and dog. When framing the problem as segmentation, the expected output is an image of the same size as the input image, with each pixel value being the classified label (we will analyze how this works in the following sections). We will see how to use GluonCV Model Zoo with two very important models for semantic segmentation – PSPNet and DeepLab-v3.
In this recipe, we will compare the performance of these two pre-trained models to segment objects semantically on the dataset introduced in the previous chapter, Penn-Fudan Pedestrians, as its ground-truth also includes segmentation masks.
Getting ready
As with previous chapters, in this recipe, we will use a few matrix operations and...
