GazeML

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GazeML is a Research & Education tool that provides a Tensorflow-based framework for high-performance gaze estimation. It integrates re-implementations of published deep learning algorithms for eye tracking.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is GazeML?

GazeML is a deep learning framework built on Tensorflow, designed for training high-performance gaze estimation models. It provides a robust platform for researchers and developers to implement and test various gaze estimation algorithms. The framework currently integrates re-implementations of published algorithms such as ELG (Eye region Landmarks based Gaze Estimation) and DPG (Deep Pictorial Gaze Estimation). While it may work on various platforms, it has been specifically tested on Ubuntu 16.04. Users can install dependencies, acquire pre-trained weights, and run webcam demos, making it a practical tool for advancing research in eye tracking and human-computer interaction.

Best used for

Ideal for developers and data scientists who need to train and test deep learning models for gaze estimation, re-implement published eye tracking algorithms, and develop applications requiring precise eye tracking. Especially valuable for academic research and prototyping in computer vision and human-computer interaction.

Common actions

estimate gaze

train deep learning models

implement eye tracking

research computer vision

face swapping"AI Agents"github copilotdeepfakeautomated workflowopen-sourcelow-code/no-codeworkflowscollaboration

Capabilities

Key features

Tensorflow-based framework
ELG model integration
DPG model integration
Pre-trained weights
Webcam demo

Target Audience

developerdata scientistprofessorstudent

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What deep learning models are integrated into GazeML?

GazeML currently integrates re-implementations of two published algorithms: ELG (Eye region Landmarks based Gaze Estimation) and DPG (Deep Pictorial Gaze Estimation). These models are provided as a foundation for high-performance gaze estimation within the framework.

What are the system requirements for running GazeML?

While GazeML may work on various platforms, it has been primarily tested and verified on an Ubuntu 16.04 system. Users should also ensure they have Tensorflow installed separately, as the framework allows for custom wheel files if necessary.

Can I use GazeML for real-time gaze estimation?

Yes, GazeML includes a webcam demo feature that allows users to run and test gaze estimation in real-time. This is useful for quick prototyping and demonstrating the capabilities of the integrated models.

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