Dataset

This dataset was created by Imad Eddine Djerarda

Contents

A dataset of over 14.5k preprocessed and augmented eye movement images, optimized for training AI models in assistive technology, including wheelchair automation via gaze detection.

Cow Eye Dataset

## Overview

Cow Eye Dataset is a dataset for instance segmentation tasks - it contains Eye annotations for 1,071 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Redness Of Eyes

## Overview

Redness Of Eyes is a dataset for object detection tasks - it contains Red Eyes annotations for 104 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

👀 Open and Closed Eyes Dataset

Welcome to the Open and Closed Eyes Dataset! This dataset is designed to help researchers and developers in computer vision and machine learning tasks, particularly in recognizing and distinguishing between open and closed eyes in various contexts. Below, you'll find a detailed description of the dataset structure, categories, and how to interpret the data. 🌟

  📁 Dataset Structure

The dataset is stored in Parquet files, ensuring efficient… See the full description on the dataset page: https://huggingface.co/datasets/MichalMlodawski/closed-open-eyes.

Dataset

This dataset was created by Prasad V Patil

Released under CC0: Public Domain

Contents

The dataset comprises 10,000 open-eye images, 10,000 closed-eye images, and 10,000 resized closed-eye images with dimensions of 256x256 pixels. The open-eye images capture eyes in an open state, while the closed-eye images depict eyes in a closed state. The resized closed-eye images are a subset of the closed-eye images that have been uniformly resized to 256x256 pixels. This diverse collection of eye images is intended to serve as a comprehensive dataset for tasks such as eye detection or recognition, providing varied examples of different eye states for training and testing machine learning models.

2. Human Eye

## Overview

2. Human Eye is a dataset for object detection tasks - it contains Eye Iris Face annotations for 7,687 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This dataset contains blue fundus images used to differentiate between myopic (nearsighted) and normal eye conditions. Fundus images capture detailed views of the eye's interior, including key areas like the retina and optic disc. The images are labeled as either myopia or normal, making it useful for developing and testing machine learning models aimed at detecting eye diseases. It includes a total of 124,794 images, with 63,294 labeled as "Myopia" and 61,500 labeled as "Normal." The images are suitable for training machine learning models aimed at detecting and classifying myopia conditions in the eye.

Folder Structure:

Normal: Contains images of healthy eyes without myopia. Myopia: Contains images of eyes affected by myopia

Installed in the upper corner of an indoor 2-seater library study pod (dimensions: 2.20m × 1.05m × 2.10m).

The dataset used for pupil tracking with event cameras.

The Unity Eyes Eye Gaze Detection Dataset offers high-quality eye imagery and gaze annotations for training AI models in driver monitoring, attention tracking, and ADAS applications.

DOI This find is registered at Portable Antiquities of the Netherlands with number PAN-00113814

This dataset contains detailed eye-tracking measurements from patients with Parkinson’s disease (PD) who participated in a study on orthostatic hypotension (OH). Data were recorded as patients performed a series of gaze tasks, focusing on an object positioned first on the ceiling and then on the wall, repeating this process through three sequences of lying down and sitting up. The objective was to assess eye movement characteristics, gaze stability, and pupil dynamics under repeated orthostatic challenges. This study aims to investigate the relationship between PD, OH, and potential oculomotor and autonomic nervous system impairments.

There are two data sets of eye scans available. The first of these is a set fundus images of which the are c. 7.0 million. The other is a set of OCT scans of which there are c. 440, 000.

This dataset contains routine clinical ophthalmology data for every patient who have been seen at Queen Elizabeth Hospital and the Birmingham, Solihull and Black Country Diabetic Retinopathy screening program at University Hospitals Birmingham NHS Foundation Trust, with longitudinal follow-up for 15 years. Key data included are: • Total number of patients. • Demographic information (including age, sex and ethnicity) • Past ocular history • Intravitreal injections • Length of time since eye diagnosis • Visual acuity • The national screening diabetic grade category (seven categories from R0M0 to R3M1) • Reason for sight and severe sight impairment

Geography University Hospitals Birmingham is set within the West Midlands and it has a catchment population of circa 5.9million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.

Data source: Ophthalmology department at Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic screening scheme in Europe.

Pathway: The routine secondary care follow-up in the hospital eye services for all ophthalmic diseases at Queen Elizabeth Hospital. The Birmingham, Solihull and Black Country dataset is representative of the patient pathway for community screening and grading of diabetic eye disease.

Eye Normal Eye

## Overview

Eye Normal Eye is a dataset for object detection tasks - it contains Eye annotations for 269 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by ferdiu

Released under Attribution 4.0 International (CC BY 4.0)

Contents

2005-2016. This dataset includes data from the retired BRFSS Vision Module. From 2005-2011 the BRFSS employed a ten question vision module regarding vision impairment, access and utilization of eye care, and self-reported eye diseases. In 2013 and subsequently, one question in the core of BRFSS asks about vision: “Are you blind or do you have serious difficulty seeing, even when wearing glasses?” The latest data for this core question can be found in the Vision and Eye Health Surveillance System (VEHSS). VEHSS is intended to provide population estimates of vision loss function, eye diseases, health disparities, as well as barriers and facilitators to access to vision and eye care. This information can be used for designing, implementing, and evaluating vision and eye health prevention programs. To access the latest BRFSS data, (2013-2017) view the Behavioral Risk Factors – Vision and Eye Health Surveillance dataset (https://chronicdata.cdc.gov/Vision-Eye-Health/Behavioral-Risk-Factors-Vision-and-Eye-Health-Surv/vkwg-yswv).

Dataset

This dataset was created by Ritesh Patil

Contents

Transformed Fixation Number (TFN): Ratio between gazing point number in AOI and duration of time in AOI. A higher ratio means the more gazing point in AOI.Transformed Fixation Time (TFT): Ratio of Fixation Time between Duration Times in AOI. The higher of ratio, the longer of duration.Average Gaze Duration (TFN): Ratio between TFN and TFT. This is a method that is popularly found among other eye tracking researches and is always chosen to evaluate the intensity of gazing point.