ml-classifier-ui
v0.5.1
Published
A package for creating image-based machine learning models
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ML Classifier UI
ML Classifier is a React front end for a machine learning engine for quickly training image classification models in your browser. Models can be saved with a single command, and the resulting models reused to make image classification predictions.
This package is the UI front end for ml-classifier
.
Walkthrough
A walkthrough of the code can be found in the article Image Classification in the Browser with Javascript.
Demo
An interactive demo can be found here.
Screenshot of demo
Getting Started
Installation
ml-classifier-ui
can be installed via yarn
or npm
:
yarn add ml-classifier-ui
or
npm install ml-classifier-ui
Quick Start (Code Sandbox)
You can fork a live running version at codesandbox.io.
Quick Start (Running locally)
Start by instantiating a new MLClassifierUI.
import React from 'react';
import ReactDOM from 'react-dom';
import MLClassifierUI from 'ml-classifier-ui';
ReactDOM.render(<MLClassifierUI />, document.getElementById('root'));
API Documentation
MLClassifierUI
accepts a number of parameters:
- getMLClassifier (
Function
) Optional - A callback that returns an instance of the underlyingml-classifier
object. Call this if you want to programmatically call methods likeaddData
,train
, andpredict
. For more information onml-classifier
's API methods refer to it's documentation. - methodParams (
Object
) Optional - A set of parameters that will be passed in calls toml-classifier
's methods. See below for more information. - uploadFormat (
string
) Optional - A string denoting what type of upload format to accept. Formats can beflat
ornested
. See below note for more information on that. If omitted, all formats are accepted. - imageFormats (
string[]
) Optional - An array of file extensions to accept. By default, all valid images are accepted. Images are transformed via the nativeImage
tag in the browser, so if the browser can display the image, it'll be processed. - showDownload (
boolean
) Optional - A flag denoting whether to show a download button or not. Defaults to true.
MLClassifierUI
also accepts a number of callbacks that are called on the beginnings and ends of ml-classifier
functions. You can view a list of those here.
getMLClassifier
getMLClassifier
returns an instance of ml-classifier
for programmatic access to the underlying methods.
Example
<MLClassifierUI
getMLClassifier={(mlClassifier) => {
mlClassifier.addData(...);
}}
/>
methodParams
methodParams
can be used to pass method-specific parameters to ml-classifier
. The key will be used to determine which method to pass parameters to.
Accepted keys are train
, evaluate
, and save
. Other keys will be ignored.
Example
<MLClassifierUI
methodParams={{
train: {
epochs: 20,
},
evaluate: {
batchSize: 32,
},
save: {
},
}}
/>
uploadFormat
uploadFormat
corresponds to how uploaded images should be organized. There are two options:
nested
Expects images to be organized in folders matching the label. Only the immediate parent folder's name will be used as the label. For example:
- containing-folder/
- dogs/
- IMG-1.jpg
- IMG-2.jpg
- IMG-3.jpg
- cats/
- IMG-1.jpg
- IMG-2.jpg
- IMG-3.jpg
Will product an array of three dogs
labels and three cats
labels.
Nested folders will be searched recursively, but only immediate parent folders' names will be used. If an invalidly nested structure is found an error will be thrown.
flat
(currently in development)
Expects files' names to be the label. Nested folders will be searched recursively (if the browser supports it) to build a flat array of files.
- folder/
- dog-1.jpg
- dog-2.jpg
- dog-3.jpg
- cat-1.jpg
- cat-2.jpg
- cat-3.jpg
Example
<MLClassifierUI
uploadFormat={"nested"}
/>
imageFormats
(currently in development)
imageFormats
denotes the list of acceptable image formats for upload. Any images not matching the list of acceptable formats will be ignored.
Example
<MLClassifierUI
imageFormats={[
'png',
'gif',
]}
/>
Contributing
Contributions are welcome!
You can run the local example with:
yarn watch
ml-classifier-ui
is written in Typescript and React.
Tests
Tests are a work in progress. Currently, the test suite only consists of unit tests. Pull requests for additional tests are welcome!
Run tests with:
yarn test
Author
License
This project is licensed under the MIT License - see the LICENSE file for details