How to Train DETR on a Custom Dataset

RF-DETR is a cutting-edge real-time object detection model developed by Roboflow, notable for achieving a score of over 60 on the COCO benchmark and excelling on the RF100-VL benchmark. Unlike traditional models that use anchor boxes, DETR approaches object detection as a direct set prediction problem, utilizing a transformer-based backbone that allows for end-to-end training and eliminates the need for non-maximum suppression. Its design leverages a self-attention mechanism to capture local and global dependencies within images, making it effective for applications such as autonomous driving, retail, industrial automation, and security surveillance. A comprehensive guide is available, demonstrating how to train and evaluate the DETR model on custom datasets using PyTorch Lightning, with detailed steps provided in an accompanying Jupyter notebook. The process includes downloading custom datasets in COCO format, setting up the model, creating data loaders, training, testing, and saving the model for future use.