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What is Zastra?

We are set to enhance the platform by adding annotation capabilities specifically designed for segmentation tasks. The Zastra repository will feature cutting-edge algorithms that streamline segmentation processes, thereby improving active learning across various datasets. To support this, we will implement complete end-to-end machine learning operations, which will include dataset and experiment version control, as well as templated pipelines that facilitate model deployment in standard cloud environments and on edge devices. By embedding advancements in Bayesian deep learning within the active learning framework, we aspire to significantly enhance overall performance. Additionally, we will improve the precision of annotations by utilizing specialized architectures like Bayesian CNNs, which will ensure superior outcomes. Our dedicated team has committed considerable time and resources to create this innovative solution tailored to your requirements. While we are consistently adding new features and improvements to the platform, we invite you to participate in a trial run soon! Zastra offers a variety of essential functionalities, such as active learning for object classification, detection, localization, and segmentation across multiple formats, including images, videos, audio, text, and point cloud data. This versatility not only makes Zastra a powerful tool for addressing a wide range of data challenges, but it also positions us well to adapt to future needs in data processing and machine learning advancements.

What is Hive Data?

Create training datasets for computer vision models through our all-encompassing management solution, as we recognize that the effectiveness of data labeling is vital for developing successful deep learning applications. Our goal is to position ourselves as the leading data labeling platform within the industry, allowing enterprises to harness the full capabilities of AI technology. To facilitate better organization, categorize your media assets into clear segments. Use one or several bounding boxes to highlight specific areas of interest, thereby improving detection precision. Apply bounding boxes with greater accuracy for more thorough annotations and provide exact measurements of width, depth, and height for a variety of objects. Ensure that every pixel in an image is classified for detailed analysis, and identify individual points to capture particular details within the visuals. Annotate straight lines to aid in geometric evaluations and assess critical characteristics such as yaw, pitch, and roll for relevant items. Monitor timestamps in both video and audio materials for effective synchronization. Furthermore, include annotations of freeform lines in images to represent intricate shapes and designs, thus enriching the quality of your data labeling initiatives. By prioritizing these strategies, you'll enhance the overall effectiveness and usability of your annotated datasets.

Media

Media

Integrations Supported

Apache Atlas
Hive AutoML
Quickwork
Style Intelligence
Taxi for Email
Toad Intelligence Central
Wyn Enterprise
bipp

Integrations Supported

Apache Atlas
Hive AutoML
Quickwork
Style Intelligence
Taxi for Email
Toad Intelligence Central
Wyn Enterprise
bipp

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$25 per 1,000 annotations
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

RoundSqr

Date Founded

2018

Company Location

India

Company Website

www.roundsqr.com/zastra/

Company Facts

Organization Name

Hive

Date Founded

2013

Company Location

United States

Company Website

thehive.ai/hive-data

Categories and Features

Data Labeling

Human-in-the-loop
Labeling Automation
Labeling Quality
Performance Tracking
Polygon, Rectangle, Line, Point
SDK
Supports Audio Files
Task Management
Team Collaboration
Training Data Management

Categories and Features

Computer Vision

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

Data Labeling

Human-in-the-loop
Labeling Automation
Labeling Quality
Performance Tracking
Polygon, Rectangle, Line, Point
SDK
Supports Audio Files
Task Management
Team Collaboration
Training Data Management

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