RTMDet is an efficient real-time object detector that exceeds the YOLO series, featuring a model architecture with large-kernel depth-wise convolutions and soft labels in dynamic label assignment. It is easily extensible for instance segmentation and rotated object detection tasks.
This model uses the RTMDet-Nano variant trained specifically for person detection. It is designed to work with
RTMPose in a two-stage pipeline for real-time human pose estimation: RTMDet first detects persons in the image, then RTMPose estimates the keypoints for each detected person.
