How to Detect Objects with YOLOv5

YOLOv5, a computer vision model released in 2020, is designed for object detection, allowing users to train models for tasks such as identifying wooden pallets in manufacturing facilities. Despite being largely replaced by YOLOv8, YOLOv5 maintains an active community with numerous models in production use. This guide explains how to deploy YOLOv5 models using Roboflow, including uploading models, deploying them on images and video streams, and running inference via the Inference SDK. The process involves creating a Roboflow project, uploading a dataset, training a model using a provided notebook, and deploying the model with Inference as either a microservice or integrated into Python code. The guide provides step-by-step instructions for running inference on images and live video feeds, utilizing features like bounding box annotation and real-time object tracking with the supervision Python package.