Introduction

NsfwSpy.js is a nudity/pornography image classifier built for Node.js and browsers, based on our parent .NET project, to aid in moderating user-generated content for various different application types, written in TypeScript. The machine learning model has been trained against the MobileNetV2 neural net architecture with 537,000 images (186GB), from 4 different categories:

Performance

NsfwSpy.js isn't perfect, but the accuracy should be good enough to detect approximately 96% of Nsfw images, those being images that are classed as pornography, sexy or hentai.

Quick Start

Want to see how NsfwSpy.js performs? Try it now on our test site.

This project is available as two seperate packages, one for browsers and one for Node.js.

Browser

npm install @nsfwspy/browser

Import NsfwSpy at the top of your JavaScript or TypeScript file:

import { NsfwSpy } from '@nsfwspy/browser';

Classify an Image element

const img = document.getElementById("img");
const nsfwSpy = new NsfwSpy();
await nsfwSpy.load();
const result = await nsfwSpy.classifyImage(img);

Node.js

npm install @nsfwspy/node

Import NsfwSpy at the top of your JavaScript or TypeScript file:

JavaScript

const { NsfwSpy } = require('@nsfwspy/node');

TypeScript

import { NsfwSpy } from '@nsfwspy/node';

Classify an Image File

const filePath = "C:\\Users\\username\\Documents\\flower.jpg";
const nsfwSpy = new NsfwSpy();
await nsfwSpy.load();
const result = await nsfwSpy.classifyImageFile(filePath);

Classify an Image from a Byte Array

const imageBuffer = await fs.readFileSync(filePath);
const nsfwSpy = new NsfwSpy();
await nsfwSpy.load();
const result = await nsfwSpy.classifyImageFromByteArray(imageBuffer);

Contact Us

Interested to get involved in the project? Whether you fancy adding features, providing images to train NsfwSpy with or something else, feel free to contact us via email at nsfwspy@outlook.com or find us on Twitter at @nsfw_spy.

Notes

Using NsfwSpy? Let us know! We're keen to hear how the technology is being used and improving the safety of applications.

Got a feature request or found something not quite right? Report it here on GitHub and we'll try to help as best as possible.