This article explains how to run inference on a YOLOv8 object detection model using Docker and create a REST API to orchestrate the process. It includes code implementation and a detailed README in the author's GitHub repository for running the API via REST with Docker.
This article guides readers through running the latest YOLO v10 object detection model on different hardware, specifically on a Raspberry Pi 5, and a computer. The article discusses the importance of computer vision in ML applications, the versatility of YOLO, and the cross-platform code presented in the article.
This article provides a step-by-step guide on fine-tuning the Florence-2 model for object detection tasks, including loading the pre-trained model, fine-tuning with a custom dataset, and evaluating the model's performance.
