Optimizing inference for image segmentation

In the previous recipe, we saw how we can leverage MXNet and Gluon to optimize the inference of our models, applying different techniques, such as improving the runtime performance using hybridization; how using half-precision (float16) in combination with AMP can strongly reduce our inference times; and how to take advantage of further optimizations with data types such as Int8 quantization.

Now, we can revisit a problem we have been working with throughout the book, image segmentation. We have worked with this task in recipes from previous chapters. In Recipe 4, Segmenting objects semantically with MXNet Model Zoo – PSPNet and DeepLabv3, from Chapter 5, Analyzing Images with Computer Vision, we introduced the task and the datasets that we will be using in this recipe, MS COCO and Penn-Fudan Pedestrian, and learned how to use pre-trained models from GluonCV Model Zoo.

Furthermore, in Recipe 3, Improving performance for segmenting...