{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dataset-for-eye-tracking-tasks","title":"Dataset for eye-tracking tasks","arxiv_id":null,"date":"2020-12-02","proceeding":null,"authors":["Ildar Rakhmatulin"],"abstract":"In recent years many different deep neural networks were developed, but due to a large number of layers\r\nin deep networks, their training requires a long time and a large number of datasets. Today is popular to\r\nuse trained deep neural networks for various tasks, even for simple ones in which such deep networks are\r\nnot required. The well-known deep networks such as YoloV3, SSD, etc. are intended for tracking and\r\nmonitoring various objects, therefore their weights are heavy and the overall accuracy for a specific task\r\nis low. Eye-tracking tasks need to detect only one object - an iris in a given area. Therefore, it is logical to\r\nuse a neural network only for this task. But the problem is the lack of suitable datasets for training the\r\nmodel. In the manuscript, we presented a dataset that is suitable for training custom models of\r\nconvolutional neural networks for eye-tracking tasks. Using data set data, each user can independently\r\npre-train the convolutional neural network models for eye-tracking tasks. This dataset contains annotated\r\n10,000 eye images in an extension of 416 by 416 pixels. The table with annotation information shows the\r\ncoordinates and radius of the eye for each image. This manuscript can be considered as a guide for the\r\npreparation of datasets for eye-tracking devices.","url_abs":"https://www.preprints.org/manuscript/202012.0047/v1","url_pdf":"https://www.researchgate.net/publication/346561315_Dataset_for_eye-tracking_tasks","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dataset-for-eye-tracking-tasks","repo_url":"https://github.com/Ildaron/5.eye_tracking_with_CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"non-maximum-suppression","method_name":"Non Maximum Suppression"},{"method_slug":"ssd","method_name":"SSD"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}