As a major breakthrough in the field of object detection, RF-DETR achieved 60+ APs on the standard benchmark test, the Microsoft COCO dataset, while maintaining real-time performance, an achievement that marks an important advancement in real object detection technology. Specific test data shows that the model processes each image with extremely low latency, fully meeting the stringent timing requirements of real-time application scenarios. This outstanding performance is mainly due to its optimized architectural design, which combines the Transformer's attention mechanism with an efficient decoding strategy to dramatically increase detection speed without loss of accuracy. In addition, the model also performs well in Roboflow's specially designed RF100-VL benchmark, demonstrating excellent generalization capabilities and real-world application potential. To validate this breakthrough performance, developers can easily reproduce the test results by downloading the pre-trained model directly on GitHub.
This answer comes from the articleRF-DETR: An Open Source Model for Real-Time Visual Object DetectionThe
