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It is important in industry to define what we see and how well we see it. This simple yet powerful idea has driven recent developments in the Arm Research ML Lab.

This blog post is about sharing our experience in running PyTorch Mobile with NNAPI on various mobile devices. I hope that this will provide developers with a sense of how these models are executed on mobile devices through PyTorch with NNAPI.

This blog explains how developers can create fitness and exercise applications on Smart TVs for the end-user.

This blog explains how to get real time results with CNN inference for Augmented Reality (AR) on Mobile.

A single label is not enough. Label diversity can be introduced by creating several labels for each training example in a way that the ordinal structure allows.

This blog describes the process for building an augmented reality (AR) filter mobile app in Unity.