Convolution Nerual Network (CNN) has been used in many visual tasks. You may find the networks for varying types of visual tasks share similar set of feature extraction layer, which is referred as backbone. Researchers typically use backbone which has been succesful in ImageNet competion and combine them with different loss functions to solve different type of visual tasks.

If you want to run deep learning inference for some embedded system, there are several possiblities now, e.g., Rapberry Pi.

Visual localization aims to estimate the localization, which is usually the the coordinate (orientation and localization) in the world coordindately, given one or multiple images.

ResNet

In this post, we will introduce some neural networks which are suitable for running on mobile devices.

The trained network is typically too large to run efficiently on mobile device. For example, VGG16 used for image classification has more 130 Million parameter (about 600 MB on model size) and requires about 31 billion operations to classify an image, which is way to expensive to be done on mobile.