Learn how to orchestrate object detection inference via an API with Docker“YOLO Inference with Docker via API” project structure. Image by author.This article will explain how to run inference on a YOLOv8 object detection model using docker, and how to create a REST API through which to orchestrate the inference process. To this end, this article is divided into three sections: how to run YOLOv8 inference, how to implement the API, and how to run both in a Docker container.Along the article, the code implementation of all the concepts and components needed for the project will be shown. The full code can also be found in my GitHub repository.To go deeper into the code and its structure, and to be able to run the inference via REST API with Docker easily with a few commands, the README file in the repository explains in detail the steps to follow, how to get the API documentation and the structure of the project.YOLO was born to address the difficulty of balancing training time and accuracy, as well as to achieve object detection by combining object localization and classification in a single step instead of separately, which were problems that the most popular models/architectures at the time had [1]. Since this article does not…