CoreML-samples VS Swift-AI

Compare CoreML-samples vs Swift-AI and see what are their differences.


Sample code for Core ML using ResNet50 provided by Apple and a custom model generated by coremltools. (by ytakzk)


The Swift machine learning library. (by Swift-AI)
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CoreML-samples Swift-AI
0 1
39 5,904
- 0.0%
0.0 0.0
over 5 years ago almost 6 years ago
Jupyter Notebook Swift
MIT 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.


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

We haven't tracked posts mentioning CoreML-samples yet.
Tracking mentions began in Dec 2020.


Posts with mentions or reviews of Swift-AI. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-06-25.

What are some alternatives?

When comparing CoreML-samples and Swift-AI you can also consider the following projects:

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

AIToolbox - A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms


SpriteKit+Spring - SpriteKit API reproducing UIView's spring animations with SKAction

PDFGenerator - A simple generator of PDF written in Swift.

BrainCore - The iOS and OS X neural network framework


EVURLCache - a NSURLCache subclass for handling all web requests that use NSURLRequest

Awesome-Mobile-Machine-Learning - A curated list of awesome mobile machine learning resources for iOS, Android, and edge devices.

MLKit - A simple machine learning framework written in Swift 🤖

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