Manually annotated frames: Seyfourian P., et al. (2026), Pupil-DLC: an open-source deep learning pipeline for scalable, marker-less tracking of pupil dynamics across conscious and unconscious states.
Abstract
This dataset contains all the manually annotated frames used to train the General Model of the Pupil-DLC pipeline described in the manuscript titled "Pupil-DLC: an open-source deep learning pipeline for scalable, markerless tracking of pupil dynamics across conscious and unconscious states" by Parsa Seyfourian, Lydia C. Marks, Leslie D. Claar, Yasmeen Nahas, Miles Keating, Christof Koch & Irene Rembado.
Fields of Study & Subjects
Gaze Tracking and Assistive Technology
Retinal Imaging and Analysis
Video Surveillance and Tracking Methods
Publication details
- Journal / source
- Figshare
- Publication date
- 6 February 2026
- Document type
- Dataset