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What is Tictag?

Tictag offers an exceptional mobile data platform that ensures high-quality datasets with an astonishing accuracy rate of 99%, effectively streamlining the process of obtaining machine learning data. The platform combines an intuitive design with engaging gamified elements, fostering the creation of superior datasets, all while backed by Truetag's stringent quality control measures. This innovative approach signifies the forefront of technology in data labeling. Tictag excels in collecting and annotating even the most intricate datasets with remarkable precision, catering to AI and ML needs while providing quick turnaround times. The labeling process has undergone a transformation, achieving unmatched speed and simplicity. With Tictag's advanced Truetag quality control, you can trust that your data will be tailored to meet your precise specifications right from the start. Furthermore, by utilizing Tictag, you not only fulfill your data requirements but also open doors for individuals seeking side income or aiming to learn new skills. In this way, Tictag enhances your AI capabilities while simultaneously fostering community skill development and economic opportunities. This dual impact illustrates how Tictag is not just about data; it’s about empowering people as well.

What is Supervisely?

Our leading-edge platform designed for the entire computer vision workflow enables a transformation from image annotation to accurate neural networks at speeds that can reach ten times faster than traditional methods. With our outstanding data labeling capabilities, you can turn your images, videos, and 3D point clouds into high-quality training datasets. This not only allows you to train your models effectively but also to monitor experiments, visualize outcomes, and continuously refine model predictions, all while developing tailored solutions in a cohesive environment. The self-hosted option we provide guarantees data security, offers extensive customization options, and ensures smooth integration with your current technology infrastructure. This all-encompassing solution for computer vision covers multi-format data annotation and management, extensive quality control, and neural network training within a single platform. Designed by data scientists for their colleagues, our advanced video labeling tool is inspired by professional video editing applications and is specifically crafted for machine learning uses and beyond. Additionally, with our platform, you can optimize your workflow and markedly enhance the productivity of your computer vision initiatives, ultimately leading to more innovative solutions in your projects.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Has API

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Tictag

Company Website

www.tictag.io

Company Facts

Organization Name

Supervisely

Date Founded

2017

Company Location

United States

Company Website

supervise.ly/

Categories and Features

Data Annotation

Not specified

Data Labeling

Not specified

Image Annotation

Not specified

Video Annotation

Not specified

Categories and Features

Computer Vision

Blob Detection & Analysis
Building Tools
Image Processing
Multiple Image Type Support
Reporting / Analytics Integration
Smart Camera Integration

Data Annotation

Not specified

Data Labeling

Labeling Automation
Labeling Quality
Supports Audio Files
Task Management
Team Collaboration

Image Annotation

Not specified

Image Recognition

Not specified

Machine Learning

Not specified

Neural Network

Not specified

Video Annotation

Not specified

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