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What is LiteRT?

LiteRT, which was formerly called TensorFlow Lite, is a sophisticated runtime created by Google that delivers enhanced performance for artificial intelligence on various devices. This innovative platform allows developers to effortlessly deploy machine learning models across numerous devices and microcontrollers. It supports models from leading frameworks such as TensorFlow, PyTorch, and JAX, converting them into the FlatBuffers format (.tflite) to ensure optimal inference efficiency. Among its key features are low latency, enhanced privacy through local data processing, compact model and binary sizes, and effective power management strategies. Additionally, LiteRT offers SDKs in a variety of programming languages, including Java/Kotlin, Swift, Objective-C, C++, and Python, facilitating easier integration into diverse applications. To boost performance on compatible devices, the runtime employs hardware acceleration through delegates like GPU and iOS Core ML. The anticipated LiteRT Next, currently in its alpha phase, is set to introduce a new suite of APIs aimed at simplifying on-device hardware acceleration, pushing the limits of mobile AI even further. With these forthcoming enhancements, developers can look forward to improved integration and significant performance gains in their applications, thereby revolutionizing how AI is implemented on mobile platforms.

What is Captur?

Captur is a cutting-edge computer-vision SDK designed to enable real-time photo validation in mobile apps. By leveraging on-device models, it evaluates images as they are captured, notifying users of potential problems such as blurriness, inadequate lighting, or facial presence, and ensuring compliance with specific criteria prior to upload. This versatile SDK works seamlessly on both iOS and Android systems and can be integrated into projects that utilize Swift, Kotlin, React Native, and Flutter frameworks. Developers can define image requirements using simple business language, allowing Captur to aid users in submitting high-quality evidence for various tasks, including proof of delivery, monitoring micromobility parking, vehicle inspections, and ensuring helmet compliance. The image analysis process occurs offline on the user's device, while the management of policies and model updates can be conducted independently. Moreover, applications can review or modify the results produced by the model, providing flexibility. By focusing on empowering product and operations teams, Captur enhances the development of mobile applications reliant on trustworthy user-generated images, which significantly improves the quality and reliability of submissions. This comprehensive approach not only simplifies the validation process but also enriches the user experience by minimizing the likelihood of erroneous submissions, ultimately fostering greater user satisfaction and trust in the application.

Media

Media

No images available

Integrations Supported

C++
Google AI Edge Gallery
JAX
Java
Kotlin
Objective-C
PyTorch
Python
Swift
TensorFlow

Integrations Supported

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Version

Pricing Information

Pricing is consumption-based and is sized to expected volume and use case. New customers can start with a paid pilot.

Supported Platforms

Android
iPhone
iPad
Linux

Supported Platforms

Android
iPhone
iPad

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

ai.google.dev/edge/litert

Company Facts

Organization Name

Captur

Company Location

United Kingdom

Company Website

captur.ai/

Categories and Features

Categories and Features

Computer Vision

Not specified

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