Client-side indecent content checking

`, ``) and returns an array of most likely predictions and their confidence levels. ```js // Return top 3 guesses (instead of all 5) const predictions = await model.classify(img, 3); ``` **Parameters** - Tensor, Image data, Image element, video element, or canvas element to check - Number of results to return (default all 5) **Returns** - Array of objects that contain `className` and `probability`. Array size is determined by the second parameter in the `classify` function. ### `dispose` a loaded model If you are done with a model instance, call `dispose()` to release held tensors and model resources. ```js const model = await nsfwjs.load(); // ... classify/infer model.dispose(); ``` ## Production Tensorflow.js offers two flags, `enableProdMode` and `enableDebugMode`. If you're going to use NSFWJS in production, be sure to enable prod mode before loading the NSFWJS model. ```js import * as tf from "@tensorflow/tfjs"; import * as nsfwjs from "nsfwjs"; tf.enableProdMode(); //... let model = await nsfwjs.load(`${urlToNSFWJSModel}`); ``` **NOTE:** Consider downloading and hosting the model yourself before moving to production as explained in the [Host your own model](#host-your-own-model) section. This could potentially improve the initial load time of the model. Furthermore, consider [Caching](#caching) the model, if you are using it in the browser. ## Backend selection NSFWJS uses whichever TensorFlow.js backend is active. Setting a backend explicitly is optional. If you import one or more backends and call `await tf.ready()`, TensorFlow.js will pick the best available backend. Use `tf.setBackend(...)` only when you want deterministic behavior across devices. Automatic backend selection: ```js import * as tf from "@tensorflow/tfjs"; import "@tensorflow/tfjs-backend-webgpu"; import "@tensorflow/tfjs-backend-wasm"; import * as nsfwjs from "nsfwjs"; await tf.ready(); const model = await nsfwjs.load(); ``` Pinned backend selection: ```js import * as tf from "@tensorflow/tfjs"; import "@tensorflow/tfjs-backend-webgpu"; await tf.setBackend("webgpu"); await tf.ready(); ``` ### WASM backend (optional) ```bash yarn add @tensorflow/tfjs-backend-wasm ``` ```js import * as tf from "@tensorflow/tfjs"; import { setWasmPaths, } from "@tensorflow/tfjs-backend-wasm"; import "@tensorflow/tfjs-backend-wasm"; // If you aren't using a standard bundler, set the path to the .wasm binaries. setWasmPaths("https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm/dist/"); await tf.setBackend("wasm"); await tf.ready(); ``` ### WebGPU backend (optional) ```bash yarn add @tensorflow/tfjs-backend-webgpu ``` ```js import * as tf from "@tensorflow/tfjs"; import "@tensorflow/tfjs-backend-webgpu"; await tf.setBackend("webgpu"); await tf.ready(); ``` Backend guidance: - `webgpu`: often fastest on supported browsers/hardware. - `webgl`: strong default in modern desktop/mobile browsers. - `wasm`: useful fallback for environments where WebGL is unavailable/restricted. - `cpu`: broad compatibility but typically slower. ### Node.js backend For Node.js workloads, consider using `@tensorflow/tfjs-node` (or `@tensorflow/tfjs-node-gpu` where applicable) for better performance. See the [Node JS App](#node-js-app) section for examples. Always benchmark in your target browser/device set. ## Install NSFWJS is powered by TensorFlow.js as a peer dependency. If your project does not already have TFJS you'll may want to add it. ```bash # peer dependency $ yarn add @tensorflow/tfjs # install NSFWJS $ yarn add nsfwjs ``` For script tags include all the bundles as shown [here](#browserify). 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](https://github.com/gantman/nsfw_model). By default, the models are bundled into this package. But you may want to host the models on your own server to reduce bundle size by loading them as raw binary files or to host your own custom model. If you want to host your own version of [the model files](https://github.com/infinitered/nsfwjs/tree/master/models), you can do so by following the steps below. You can then pass the relative URL to your hosted files in the `load` function along with the `options` if necessary. If you are loading a hosted model URL, prefer `nsfwjs/core` so your app does not bundle built-in model definitions. See [Selective model bundles (tree-shaking)](#selective-model-bundles-tree-shaking). Here is how to install the default model on a website: 1. Download the project by either downloading as zip or cloning `git clone https://github.com/infinitered/nsfwjs.git`. 2. Extract the `models` folder from the root of the project and drop it in the `public` directory of your web application to serve them as static files along side your website. (You can host it anywhere such as on a s3 bucket as long as you can access it via URL). 3. Retrieve the URL and put it into `nsfwjs.load()`. For example: `nsfwjs.load(https://yourwebsite.com/models/mobilenet_v2/model.json)`. ## Run the Examples ### Tensorflow.js in the browser The demo that powers https://nsfwjs.com/ is available in the [`examples/nsfw_demo`](https://github.com/infinitered/nsfwjs/tree/master/examples/nsfw_demo) folder. To run the demo, run `yarn demo` which will copy the latest code into the demo and start the dev server. ### Browserify A browserified version using nothing but promises and script tags is available in the [`minimal_demo`](https://github.com/infinitered/nsfwjs/tree/master/examples/minimal_demo) folder. ```js ``` You should host the `nsfwjs.min.js` file and all the model bundles that you want to use alongside your project, and reference them using the `src` attribute in the script tags. ### React Native The [NSFWJS React Native app](https://github.com/infinitered/nsfwjs-mobile)  Loads a local copy of the model to reduce network load and utilizes TFJS-React-Native. [Blog Post](https://shift.infinite.red/nsfw-js-for-react-native-a37c9ba45fe9) ### Node JS App Using NPM, you can also use the model on the server side. ```bash $ npm install nsfwjs $ npm install @tensorflow/tfjs-node ``` ```javascript 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(pic.data, 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). console.log(predictions); } fn(); ``` Here is another full example of a [multipart/form-data POST using Express](examples/node_demo), supposing you are using JPG format. ```javascript 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, { useTArray: 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] = image.data[i * 4 + c]; return tf.tensor3d(values, [image.height, image.width, numChannels], "int32"); }; app.post("/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 'image=@/full/path/to/picture.jpg' ``` You can also use [`lovell/sharp`](https://github.com/lovell/sharp) for preprocessing tasks and more file formats. ### NSFW Filter [**NSFW Filter**](https://github.com/navendu-pottekkat/nsfw-filter) is an open source web extension for Google Chrome that uses NSFWJS for filtering out NSFW images. Check out the project [here](https://github.com/navendu-pottekkat/nsfw-filter). ## Learn TensorFlow.js Learn how to write your own library like NSFWJS with my O'Reilly book "Learning TensorFlow.js" available on [O'Reilly](https://learning.oreilly.com/library/view/learning-tensorflowjs/9781492090786/) and [Amazon](https://amzn.to/3dR3vpY). [](https://amzn.to/3dR3vpY) ## More! An [FAQ](https://github.com/infinitered/nsfwjs/wiki/FAQ:-NSFW-JS) page is available. More about NSFWJS and TensorFlow.js - https://youtu.be/uzQwmZwy3yw The [model was trained in Keras over several days](https://medium.freecodecamp.org/how-to-set-up-nsfw-content-detection-with-machine-learning-229a9725829c) and 60+ Gigs of data. Be sure to [check out the model code](https://github.com/GantMan/nsfw_model) which was trained on data provided by [Alexander Kim's](https://github.com/alexkimxyz) [nsfw_data_scraper](https://github.com/alexkimxyz/nsfw_data_scraper). ### 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](http://community.infinite.red). ### Need the experts? Hire Infinite Red for your next project If your project's calling for the experts in all things React Native, Infinite Red’s here to help! Our experienced team of software engineers have worked with companies like Microsoft, Zoom, and Mercari to bring even some of the most complex projects to life. Whether it’s running a full project or training a team on React Native, we can help you solve your company’s toughest engineering challenges – and make it a great experience at the same time. Ready to see how we can work together? [Send us a message](mailto:hello@infinite.red) ## Contributors Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):

Gant Laborde 💬 📝 💻 💡 🤔 🚇 👀 ⚠️	Jamon Holmgren 📖 🤔 💻 🖋	Mazen Chami 📖 💻 👀 ⚠️	Jeff Studenski 🎨	Frank von Hoven III 📖 🤔	Sandesh Soni 💻	Sean Nam 📖
Gilbert Emerson 💻	Oleksandr Kozlov 🚇 ⚠️ 💻	Morgan 💻 🤔	Michel Hua 💻 📖	Kevin VanGelder 💻 📖	Jesse Nicholson 🔣 🤔	camhart 📖
Cameron Burkholder 🎨	qwertyforce 📖	Yegor <3 💻 ⚠️	Navendu Pottekkat 📖	Vladislav 💻 📖	Nacht 💻	kateinkim 💻 📖
jan 📖	Rohan Mukherjee 💬 🚇 🚧 💻	Hasitha Wickramasinghe 💻 📖 💡 🤔 🚇 ⚠️

This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!