Cycling Pose Estimation Dataset: 480 Annotated Frames from Real-World Bike Fitting Videos

Prior evaluations of pose estimation accuracy for cycling biomechanics have been conducted predominantly in controlled laboratory settings. This dataset was constructed to support evaluation under the real-world consumer filming conditions in which bike fitting applications are actually deployed. It contains 480 annotated frames drawn from 20 real-world cycling videos posted to r/bikefit on Reddit. Each video covers two full pedal rotations with 24 evenly-spaced frames extracted, giving consistent coverage across the pedal cycle. Participants span road, mountain, and time trial bikes, filmed from both sides, under varying lighting conditions and riding postures. Keypoint annotations were produced in CVAT using a 14-point skeleton covering the shoulder, elbow, wrist, hip, knee, ankle, and foot on both body sides, alongside a head bounding box. Occluded keypoints are included with visibility flags. Faces have been anonymised. Annotations are provided as a CVAT 1.1 XML file alongside a zip of the 480 frame images. The dataset was used to benchmark YOLO11, MediaPipe, HRNet, OpenPose, and ViTPose, as part of a BSc (Hons) thesis in Artificial Intelligence at the University of Malta. Ethics approval was granted by the Faculty Research Ethics Committee.