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NSFW detection on the client-side via TensorFlow.js

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Client-side indecent content checking

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A simple JavaScript library to help you quickly identify unseemly images; all in the client's browser. NSFWJS isn't perfect, but it's pretty accurate (~90% with small and ~93% with midsized model)... and it's getting more accurate all the time.

Why would this be useful? Check out the announcement blog post.

demo example

Table of Contents

The library categorizes image probabilities in the following 5 classes:

  • Drawing
    - safe for work drawings (including anime)
  • Hentai
    - hentai and pornographic drawings
  • Neutral
    - safe for work neutral images
  • Porn
    - pornographic images, sexual acts
  • Sexy
    - sexually explicit images, not pornography

The demo is a continuous deployment source - Give it a go:

QUICK: How to use the module


import * as nsfwjs from 'nsfwjs'

const img = document.getElementById('img')

// Load model from my S3. // See the section hosting the model files on your site. const model = await nsfwjs.load()

// Classify the image const predictions = await model.classify(img) console.log('Predictions: ', predictions)


import * as nsfwjs from 'nsfwjs'

const img = document.getElementById('img')

// Load model from my S3. // See the section hosting the model files on your site. nsfwjs.load() .then(function (model) { // Classify the image return model.classify(img) }) .then(function (predictions) { console.log('Predictions: ', predictions) })

Library API

the model

Before you can classify any image, you'll need to load the model. You should use the optional first parameter and load the model from your website, as explained in the install directions.

Model example - 224x224

const model = nsfwjs.load('/path/to/model/directory/')

If you're using a model that needs an image of dimension other than 224x224, you can pass the size in the options parameter.

Model example - 299x299

const model = nsfwjs.load('/path/to/different/model/', { size: 299 })

If you're using a graph model, you cannot use the infer method, and you'll need to tell model load that you're dealing with a graph model in options.

Model example - Graph

const model = nsfwjs.load('/path/to/different/model/', { type: 'graph' })

If you're using in the browser and you'd like to subsequently load from indexed db or local storage you can save the underlying model using the appropriate scheme and load from there.

const initialLoad = await nsfwjs.load('/path/to/different/model')
const model = await nsfwjs.load('indexeddb://model')


  • optional URL to the
  • optional object with size property that your model expects.


  • Ready to use NSFWJS model object

an image

This function can take any browser-based image elements (

) and returns an array of most likely predictions and their confidence levels.
// Return top 3 guesses (instead of all 5)
const predictions = await model.classify(img, 3)


  • Tensor, Image data, Image element, video element, or canvas element to check
  • Number of results to return (default all 5)


  • Array of objects that contain
    . Array size is determined by the second parameter in the


Gif Example

This function can take a browser-based image element (

) that is a GIF, and returns an array of prediction arrays. It breaks a GIF into its frames and runs
on each with a given configuration. This can take a while, as GIFs are frequently hundreds of frames.
// Returns all predictions of each GIF frame
const framePredictions = await model.classifyGif(img)

If you're looking to update the user on status (e.g. progress bar) or change the number of top results per frame, then you can utilize the configuration parameter.

Example of passing a configuration:

// returns top 1 prediction of each GIF frame, and logs the status to console
const myConfig = {
  topk: 1,
  fps: 1,
  onFrame: ({ index, totalFrames, predictions, image }) => {
    console.log({ index, totalFrames, predictions })
    // document.body.appendChild(image)
    // require('fs').writeFileSync(`./file.jpeg`, require('jpeg-js').encode(image).data)
const framePredictions = await classifyGif(img, myConfig)


  • Image element to check
  • Configuration object with the following possible key/values:
    • topk
      - Number of results to return per frame (default all 5)
    • fps
      - Frames per seconds, frames picks proportionally from the middle (default all frames)
    • onFrame
      - Function callback on each frame - Param is an object with the following key/values:
    • index
      - the current GIF frame that was classified (starting at 0)
    • totalFrames
      - the complete number of frames for this GIF (for progress calculations)
    • predictions
      - an array of length
      , returning top results from classify
    • image
      - an image of specific frame


  • Array of the same order as number of frames in GIF. Each index corresponding to that frame, an returns array of objects that contain
    ; sorted by probability and limited by topk config parameter.


Tensorflow.js offers two flags,

. If you're going to use NSFWJS in production, be sure to enable prod mode before loading the NSFWJS model.
import * as tf from '@tensorflow/tfjs'
import * as nsfwjs from 'nsfwjs'
let model = await nsfwjs.load(`${urlToNSFWJSModel}`)


NSFWJS is powered by TensorFlow.js as a peer dependency. If your project does not already have TFJS you'll need to add it.

# peer dependency
$ yarn add @tensorflow/tfjs
# install NSFWJS
$ yarn add nsfwjs

For script tags add . Then simply access the nsfwjs global variable. This requires that you've already imported TensorFlow.js as well.

Host your own model

The magic that powers NSFWJS is the NSFW detection model. By default, this node module is pulling from my S3, but I make no guarantees that I'll keep that download link available forever. It's best for the longevity of your project that you download and host your own version of the model files. You can then pass the relative URL to your hosted files in the

function. If you can come up with a way to bundle the model into the NPM package, I'd love to see a PR to this repo!

Run the Examples

Tensorflow.js in the browser

The demo that powers is available in the

example folder.

To run the demo, run

yarn prep
which will copy the latest code into the demo. After that's done, you can
into the demo folder and run with
yarn start


A browserified version using nothing but promises and script tags is available in the


Please do not use the script tags hosted in this demo as a CDN. This can and should be hosted in your project along side the model files.

React Native

The NSFWJS React Native app React Native Demo

Loads a local copy of the model to reduce network load and utilizes TFJS-React-Native. Blog Post

Node JS App

Using NPM, you can also use the model on the server side.

$ npm install nsfwjs
$ npm install @tensorflow/tfjs-node
const axios = require('axios') //you can use any http client
const tf = require('@tensorflow/tfjs-node')
const nsfw = require('nsfwjs')
async function fn() {
  const pic = await axios.get(`link-to-picture`, {
    responseType: 'arraybuffer',
  const model = await nsfw.load() // To load a local model, nsfw.load('file://./path/to/model/')
  // Image must be in tf.tensor3d format
  // you can convert image to tf.tensor3d with tf.node.decodeImage(Uint8Array,channels)
  const image = await tf.node.decodeImage(,3)
  const predictions = await model.classify(image)
  image.dispose() // Tensor memory must be managed explicitly (it is not sufficient to let a tf.Tensor go out of scope for its memory to be released).

Here is another full example of a multipart/form-data POST using Express, supposing you are using JPG format.

const express = require('express')
const multer = require('multer')
const jpeg = require('jpeg-js')

const tf = require('@tensorflow/tfjs-node') const nsfw = require('nsfwjs')

const app = express() const upload = multer()

let _model

const convert = async (img) => { // Decoded image in UInt8 Byte array const image = await jpeg.decode(img, true)

const numChannels = 3 const numPixels = image.width * image.height const values = new Int32Array(numPixels * numChannels)

for (let i = 0; i < numPixels; i++) for (let c = 0; c < numChannels; ++c) values[i * numChannels + c] =[i * 4 + c]

return tf.tensor3d(values, [image.height, image.width, numChannels], 'int32') }'/nsfw', upload.single('image'), async (req, res) => { if (!req.file) res.status(400).send('Missing image multipart/form-data') else { const image = await convert(req.file.buffer) const predictions = await _model.classify(image) image.dispose() res.json(predictions) } })

const load_model = async () => { _model = await nsfw.load() }

// Keep the model in memory, make sure it's loaded only once load_model().then(() => app.listen(8080))

// curl --request POST localhost:8080/nsfw --header 'Content-Type: multipart/form-data' --data-binary '[email protected]/full/path/to/picture.jpg'

You can also use

for preprocessing tasks and more file formats.

NSFW Filter

NSFW Filter is a web extension that uses NSFWJS for filtering out NSFW images from your browser.

It is currently available for Chrome and Firefox and is completely open-source.

Check out the project here.

Learn TensorFlow.js

Learn how to write your own library like NSFWJS with my O'Reilly book "Learning TensorFlow.js" available on O'Reilly and Amazon.

Learning TensorFlow.js JavaScript Book Red


An FAQ page is available.

More about NSFWJS and TensorFlow.js -

The model was trained in Keras over several days and 60+ Gigs of data. Be sure to check out the model code which was trained on data provided by Alexander Kim's nsfwdatascraper.

Open Source

NSFWJS, as open source, is free to use and always will be :heart:. It's MIT licensed, and we'll always do our best to help and quickly answer issues. If you'd like to get a hold of us, join our community slack.


Infinite Red offers premium training and support. Email us at [email protected] to get in touch.


Thanks goes to these wonderful people (emoji key):

Gant Laborde

๐Ÿ’ฌ ๐Ÿ“ ๐Ÿ’ป ๐Ÿ’ก ๐Ÿค” ๐Ÿš‡ ๐Ÿ‘€ โš ๏ธ

Jamon Holmgren

๐Ÿ“– ๐Ÿค” ๐Ÿ’ป ๐Ÿ–‹

Jeff Studenski


Frank von Hoven III

๐Ÿ“– ๐Ÿค”

Sandesh Soni


Sean Nam


Gilbert Emerson


Oleksandr Kozlov

๐Ÿš‡ โš ๏ธ ๐Ÿ’ป


๐Ÿ’ป ๐Ÿค”

Michel Hua

๐Ÿ’ป ๐Ÿ“–

Kevin VanGelder

๐Ÿ’ป ๐Ÿ“–

Jesse Nicholson

๐Ÿ”ฃ ๐Ÿค”



Cameron Burkholder




Yegor <3

๐Ÿ’ป โš ๏ธ

Navendu Pottekkat



๐Ÿ’ป ๐Ÿ“–




๐Ÿ’ป ๐Ÿ“–



Rohan Mukherjee

๐Ÿ’ฌ ๐Ÿš‡ ๐Ÿšง ๐Ÿ’ป

This project follows the all-contributors specification. Contributions of any kind welcome!

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