RF-DETR: Open source real-time object detection model, high-precision and rapid identification

RF-DETR is an open source, state-of-the-art real-time object detection model developed by the Roboflow team, aiming to solve the lack of speed and accuracy of the YOLO series. It not only matches or even surpasses the previous real-time models in speed, but also achieves a qualitative leap in accuracy.

RF-DETR is the first real-time model to achieve more than 60% average precision mean (mAP) on COCO datasets, demonstrating its strong strength. It achieves low latency on GPUs and is suitable for application scenarios that require fast response, such as autonomous driving, industrial quality inspection, intelligent security, etc.

RF-DETR adopts a Transformer-based architecture, which can better model global information and achieve higher recognition accuracy in complex scenarios. Unlike the YOLO model, it does not require non-maximum suppression (NMS), which improves operating efficiency. The Roboflow team achieved excellent performance and powerful domain adaptability by combining LW-DETR with a pre-trained DINOv2 backbone network.

RF-DETR chooses open source and follows the Apache 2.0 license agreement, which allows developers to use, modify or even apply to commercial projects freely. The Roboflow team provides Colab Notebook to help developers fine-tune their custom datasets. In the future, the Roboflow platform will also provide more convenient RF-DETR model training and deployment support.

Currently, the Roboflow team has launched two model sizes: RF-DETR-base (29 million parameters) and RF-DETR-large (128 million parameters), which supports multi-resolution training, and developers can find the best balance point between accuracy and latency.

Project: https://top.aibase.com/tool/rf-detr