RF-DETR: A SOTA Real-Time Object Detection Model

RF-DETR is a cutting-edge, real-time object detection model that surpasses existing models on real-world datasets and is the first to achieve over 60 mean Average Precision (mAP) on the COCO dataset. Available under an open-source Apache 2.0 license, RF-DETR's architecture is based on detection transformers (DETR) and is designed for high-speed and accurate performance, even on limited computing resources. The model is optimized for adaptability across various domains and datasets, achieving competitive performance with smaller sizes compared to other transformer-based models. RF-DETR is accessible on GitHub and can be fine-tuned via a Colab Notebook or Roboflow Train, which uses a custom checkpoint for improved mAP scores. By integrating a pre-trained DINOv2 backbone, RF-DETR demonstrates superior adaptability and efficiency, particularly in complex scenes with overlapping objects, and it addresses the need for models that can perform well with fewer training images. The release of RF-DETR aims to advance the field of computer vision while encouraging community collaboration to enhance visual understanding and application.