Real-Time Keypoint Detection with RF-DETR

RF-DETR Keypoint is a novel real-time keypoint detection model that outperforms the YOLO26x-pose on both accuracy and speed on the COCO Keypoints dataset. This model, part of the RF-DETR family, is designed to be NAS-trained, allowing it to adapt to various input resolutions for optimal speed or accuracy without retraining. It introduces a probabilistic approach to keypoint detection, predicting a full distribution over each keypoint’s location, thus addressing hyperparameter issues that affect most keypoint models. Unlike other models, RF-DETR Keypoint learns keypoint bandwidths dynamically, eliminating the need for hand-tuned parameters and making it adaptable to custom datasets. The model leverages a full-covariance 2D Gaussian distribution for keypoint uncertainty, which remains usable downstream, enhancing applications such as tracking and camera calibration. This preview is released under the Apache 2.0 license, promoting open use without restrictive obligations, and aims to gather real-world feedback to refine the final model suite.

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