npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

image-classifier

v1.1.0

Published

Machine Learning Image Classifier for NodeJS

Downloads

56

Readme

Image Classifier NodeJS Package

Machine Learning Image Classifier for NodeJS

Installation

npm install image-classifier

Getting Started

    // CommonJS
    const ImageClassifier = require('image-classifier');

    // Or ES Modules
    import ImageClassifier from "image-classifier/lib/ImageClassifier";

Creating an ImageClassifier

ImageClassifier.create()

Create a new instance of ImageClassifier from scratch

const classifier = await ImageClassifier.create()

ImageClassifier.load(datasetPath: string)

Create a new instance of ImageClassifier from a dataset

const classifier = await ImageClassifier.load('./dataset.json');

ImageClassifier

ImageClassifier.prototype.save(datasetDestination: string)

Save the ImageClassifier's dataset to a json file

await classifier.save('./carset.json');

ImageClassifier.prototype.addExample(label: string, image: string | Buffer)

Add an example image and label it to train the ImageClassifier

// Add Toyota Examples from path
await classifier.addExample('Toyota', './toyota0.png');
await classifier.addExample('Toyota', './toyota1.png');
await classifier.addExample('Toyota', './toyota2.png');

// Add Toyota Example from raw image
const toyotaRawImage = fs.readFileSync('./toyota3.png');
await classifier.addExample('Toyota', toyotaRawImage);
/* ...Add more examples */

// Add Honda Examples
await classifier.addExample('Honda', './honda0.png');
await classifier.addExample('Honda', './honda1.png');
await classifier.addExample('Honda', './honda2.png');

// Add Honda Example from raw image
const hondaRawImage = await fs.promises.readFile('./honda3.png');
await classifier.addExample('Honda', hondaRawImage);
/* ...Add more examples */

ImageClassifier.prototype.dropClassifier(label: string)

Drop all classification for the specified label

classifier.dropClassifier('Honda');

ImageClassifier.prototype.predict(image: string | Buffer)

Predict the label for an image

// Predict by image path
const prediction1 = await classifier.predict('./toyotaTest0.png');

// Predict by raw image
const toyotaTestRawImage = fs.readFileSync('./toyotaTest1.png');
const prediction2 = await classifier.predict('./toyotaTest1.png');
{
    "classIndex": "<Index of Label>",
    "label": "<Label>",
    "confidences": {
        "Toyota": "<Percentile>",
        "Honda": "<Percentile>"
    }
}