RF-DETR is an open source, advanced real-time object detection model developed by the Roboflow team, aiming to solve the shortcomings in speed and accuracy of existing models. If you are looking for a faster and more accurate solution than the YOLO series, RF-DETR is undoubtedly the ideal choice for you.
RF-DETR is not only committed to becoming a leader in real-time recognition, but also chooses an open source method, allowing developers to use it for free and customize it to create their own efficient detection tools.
Imagine your intelligent surveillance system can instantly capture every key object in the video stream like an experienced detective, and it is incredibly fast. RF-DETR is such an efficient "detective". Not only does it surpass the previous real-time models in speed, it also achieves a qualitative leap in accuracy.
According to official data, RF-DETR is the first real-time model to achieve more than 60% average precision mean (mAP) on COCO datasets. The COCO dataset is known as the "Olympic" in the computer vision industry. The fact that such achievements are fully proves the strong strength of RF-DETR.
More importantly, RF-DETR does not sacrifice speed while ensuring high accuracy. It achieves extremely low latency on the GPU, making real-time recognition truly possible. This is undoubtedly a huge boon for application scenarios that require rapid response, such as autonomous driving, industrial quality inspection, intelligent security, etc. You can imagine that the efficiency improvement will be immeasurable when your robot recognizes and grabs target objects at lightning speed.
For a long time, the YOLO series of models based on CNN have dominated the field of real-time object detection. However, with the development of technology, RF-DETR, as a member of the DETR (Detection Transformer) family, adopts a Transformer-based architecture. This architecture can better model global information, thereby achieving higher recognition accuracy in complex scenarios.
Unlike the YOLO model, the DETR architecture does not require non-maximum suppression (NMS) to filter bounding boxes, which improves overall operating efficiency to a certain extent. The Roboflow team introduced the concept of "total delay" in the evaluation, fairly comparing the performance of different models. The results show that RF-DETR performs excellently in both speed and accuracy, and is strictly Pareto optimal on the COCO dataset compared to the YOLO model.
It is worth mentioning that RF-DETR does not completely abandon the advantages of CNN. In fact, many advanced DETR variants cleverly blend the advantages of CNN and Transformer. RF-DETR achieves excellent performance and powerful domain adaptability by combining LW-DETR with a pre-trained DINOv2 backbone network. This means that RF-DETR can perform well whether it is common object recognition or more specialized fields, such as aerospace images, industrial environments, natural scenery, etc.
Most exciting is that RF-DETR chose open source, following the Apache 2.0 license agreement. This means developers are free to use, modify, and even apply it to commercial projects without having to worry about copyright issues. The Roboflow team not only provides model code, but also thoughtfully prepares Colab Notebook, which teaches you how to fine-tune on custom datasets. In the future, the Roboflow platform will also provide more convenient RF-DETR model training and deployment support.
At present, the Roboflow team has launched two model sizes: RF-DETR-base (29 million parameters) and RF-DETR-large (128 million parameters) to meet the application scenarios of different computing power needs. Additionally, RF-DETR supports multi-resolution training, which means you can flexibly adjust the resolution of your model at runtime, finding the best balance between precision and latency.
Project: https://top.aibase.com/tool/rf-detr