Awesome-Mobile-Machine-Learning vs SwiftCoreMLTools

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Awesome-Mobile-Machine-Learning		SwiftCoreMLTools
	Project
1	Mentions	-
1,231	Stars	158
-	Growth	-
4.2	Activity	0.0
almost 3 years ago	Latest Commit	almost 4 years ago
	Language	Swift
MIT License	License	MIT License

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.

Awesome-Mobile-Machine-Learning

Posts with mentions or reviews of Awesome-Mobile-Machine-Learning. We have used some of these posts to build our list of alternatives and similar projects.

Mobile ML general
1 project | /r/createml | 12 Jan 2021

SwiftCoreMLTools

Posts with mentions or reviews of SwiftCoreMLTools. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning SwiftCoreMLTools yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing Awesome-Mobile-Machine-Learning and SwiftCoreMLTools you can also consider the following projects:

Swift-AI - The Swift machine learning library.

TensorSwift - A lightweight library to calculate tensors in Swift, which has similar APIs to TensorFlow's

CoreML-Models - Largest list of models for Core ML (for iOS 11+)

Serrano - A Swift deep learning library with Accelerate and Metal support.

CoreML-samples - Sample code for Core ML using ResNet50 provided by Apple and a custom model generated by coremltools.

spokestack-ios - Spokestack: give your iOS app a voice interface!

Caffe2

Revolver - A framework for building fast genetic algorithms in Swift.

Bender - Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.

Swift-Brain - Artificial intelligence/machine learning data structures and Swift algorithms for future iOS development. bayes theorem, neural networks, and more AI.

DL4S - Accelerated tensor operations and dynamic neural networks based on reverse mode automatic differentiation for every device that can run Swift - from watchOS to Linux

Tensorflow-iOS