How to Train YOLOR on a Custom Dataset

YOLOR (You Only Learn One Representation) is a cutting-edge object detection model that builds on the YOLO architecture, integrating implicit and explicit knowledge during inference to make predictions about image contents. The model is trained using the COCO dataset, which includes various tasks such as object detection, instance segmentation, and image classification. This tutorial guides users through the process of setting up, training, and evaluating YOLOR on a custom dataset using Roboflow, highlighting the model's ability to combine multi-task implicit knowledge with task-specific explicit knowledge for enhanced speed and accuracy. Users are shown how to prepare their development environment, import data, train the model, and run inference on test images, with the option to export the trained weights for future use. YOLOR stands out for its speed, being significantly faster than Scaled-YOLOv4 and offering a notable performance improvement over models like PP-YOLOv2.