Learn the right way to orchestrate object detection inference via an API with Docker
This text will explain the right way to run inference on a YOLOv8 object detection model using docker, and the right way to create a REST API through which to orchestrate the inference process. To this end, this text is split into three sections: the right way to run YOLOv8 inference, the right way to implement the API, and the right way to run each in a Docker container.
Along the article, the code implementation of all of the concepts and components needed for the project will likely be shown. The complete code can be present in my GitHub repository.
To go deeper into the code and its structure, and to give you the option to run the inference via REST API with Docker easily with a number of commands, the README file within the repository explains intimately the steps to follow, the right way to get the API documentation and the structure of the project.
YOLO was born to deal with the issue of balancing training time and accuracy, in addition to to attain object detection by combining object localization and classification in a single step as an alternative of individually, which were problems that the most well-liked models/architectures on the time had [1]. Since this text doesn’t…
