In this article, I explain how to build a tool to detect faces in a picture. This article is a sort of how-to design and implements a tool by using a neural network. For the design part, I describe how to: build the business model thanks to a neural network; adapt the network to the specific domain of face detection by changing its knowledge; use the resulting domain with a go-based infrastructure; code a little application in Go to communicate with the outside world.

In the previous post, I made an introduction and a POC to interact with ONNX models and Go. I have decoded the information to reconstruct a graph. Now I propose to expand the principle and to create a proper execution backend based on Gorgonia. This post is a bit more technical than the previous one because all the concepts needed to work should be present in the last article. Decoding the tensor In machine learning, the fundamental element of a computation graph is a Tensor.