https://github.com/lucianopalmeida/coreml-mnist

A simple CoreML handwritten digits classification demo
https://github.com/lucianopalmeida/coreml-mnist

coreml coreml-mnist digit ios-app prediction swift4 xcode9

Last synced: 6 months ago
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A simple CoreML handwritten digits classification demo

• Host: GitHub
• URL: https://github.com/lucianopalmeida/coreml-mnist
• Owner: LucianoPAlmeida
• Created: 2018-06-28T07:13:17.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2019-01-06T23:42:08.000Z (over 7 years ago)
• Last Synced: 2024-12-26T13:13:33.499Z (over 1 year ago)
• Topics: coreml, coreml-mnist, digit, ios-app, prediction, swift4, xcode9
• Language: Swift
• Homepage:
• Size: 22.6 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# CoreML-MNIST

[![license](https://img.shields.io/github/license/mashape/apistatus.svg)](https://opensource.org/licenses/MIT)

### CoreML is Awesome \o/

This is a simple Demo App to learn the CoreML API.

This app make digits predictions based on a model trained with the MNIST Dataset of handwritten digits. 

It just draw the digit the CoreML will make a prediction.

### The Model

The model was created and trained using [Tensorflow](https://www.tensorflow.org/) and Converted to a CoreML model using 

[tf-coreml](https://github.com/tf-coreml/tf-coreml). 

The network code, the conversion code and the models generated are available on [LucianoPAlmeida/mnist-number-classification](https://github.com/LucianoPAlmeida/mnist-number-classification).

### Credits and Thanks

- [Brian Advent](https://www.youtube.com/channel/UCysEngjfeIYapEER9K8aikw) for the awesome [Core ML Tutorial: Create a Simple Machine Learning App - Image Classification](https://www.youtube.com/watch?v=NNKPbdT9gXU) video.

- [CoreML Tools](https://github.com/apple/coremltools) and [tf-coreml](https://github.com/tf-coreml/tf-coreml) to make possible the conversion of my tensorflow model to a CoreML model.

## Licence 

CoreML-MNIST is released under the [MIT License](https://opensource.org/licenses/MIT).