Using a data set previously taken from the traffic with images categorized into 2 classes: with pits and without pits, to be used in training using the Matlab Deep Network Designer.Using different ...

The Deep Network Designer (see figure) provides a way to use pretrained models including SqueezeNet, Inception-v3, ResNet-101, GoogLeNet, and VGG-19, as well as developing new models.

The Deep Network Designer app can now train networks for image classification, semantic segmentation, multiple-input, out-of-memory, image-to-image regression, and other workflows.

MathWorks has introduced Release 2018b of MATLAB and Simulink which contains significant enhancements for deep learning, along with new capabilities and bug fixes across the product families.

Starting in R2020b, Deep Learning Toolbox supports Simulink with Image Classification and Network Prediction blocks to help simulate and generate code for deep learning models. Updates to the ...

The Deep Network Designer can be used to create networks for computer vision, signal, and text applications. It can also generate MATLAB code.