Search Results for "phone"

...Provides valid example numbers for all countries/regions, with the option of specifying which type of example phone number is needed. Quickly guesses whether a number is a possible phone number by using only the length information, much faster than a full validation. To include the Java code in your application, either integrate with Maven (see wiki) or download the latest jars from the Maven repository.

Faker C++ is a C++ library inspired by the popular Faker.js, aimed at providing developers with a robust tool for generating fake (but realistic) data. Whether you're building test suites, populating databases, or creating demos, Faker C++ has got you covered.

...It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs. It is cross-platform and supports most commonly used CNN networks, including Classical CNN (VGG AlexNet GoogleNet Inception), Face Detection (MTCNN RetinaFace), Segmentation (FCN PSPNet UNet YOLACT), and more. ncnn is currently being used in a number of Tencent applications, namely: QQ, Qzone, WeChat, and Pitu.

...QxEntityEditor is multi-platform (available for Windows, Linux, and Mac OS X) and generates native code for all environments, desktop (Windows, Linux, Mac OS X), embedded and mobile (Android, iOS, Windows Phone, Raspberry Pi, etc.). A user manual (documentation) for QxEntityEditor application is available on QxOrm website.

...At the same time, it refers to the high performance and good scalability characteristics of the industry's mainstream open source frameworks, and expands the support for X86 and NV GPUs. On the mobile phone, TNN has been used by many applications such as mobile QQ, weishi, and Pitu. As a basic acceleration framework for Tencent Cloud AI, TNN has provided acceleration support for the implementation of many businesses. Everyone is welcome to participate in the collaborative construction to promote the further improvement of the TNN inference framework.