javacpp-presets VS opus-java

Compare javacpp-presets vs opus-java and see what are their differences.

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javacpp-presets opus-java
9 1
2,580 41
0.7% -
8.0 3.6
about 17 hours ago over 2 years ago
Java Java
GNU General Public License v3.0 or later Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

javacpp-presets

Posts with mentions or reviews of javacpp-presets. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-27.

opus-java

Posts with mentions or reviews of opus-java. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-01-12.

What are some alternatives?

When comparing javacpp-presets and opus-java you can also consider the following projects:

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

discord-webhooks - Provides easy to use bindings for the Discord Webhook API

JavaCV - Java interface to OpenCV, FFmpeg, and more

Hibernate - Hibernate's core Object/Relational Mapping functionality

youtubedl-android - youtube-dl for android

libGDX - Desktop/Android/HTML5/iOS Java game development framework

Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java

OggJavaPlayer - Play Ogg in Java

api-swgoh-help - Java client wrapper for the API at https://api.swgoh.help

rotp-public - Public repository for Remnants of the Precursors

instancio - A library that creates fully populated objects for your unit tests.

Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.