---
description: Learn how to deploy pre-trained models in a TensorFlow.js web applications to perform face detection.
keywords: tensorflow.js, machine learning, ml, mediapipe, blazeface, face detection
title: Face detection with TensorFlow.js
summary: |
  This guide explains how to run TensorFlow.js in Docker containers.
tags: [ai]
languages: [js]
aliases:
  - /guides/use-case/tensorflowjs/
params:
  time: 20 minutes
---

This guide introduces the seamless integration of TensorFlow.js with Docker to
perform face detection. In this guide, you'll explore how to:

- Run a containerized TensorFlow.js application using Docker.
- Implement face detection in a web application with TensorFlow.js.
- Construct a Dockerfile for a TensorFlow.js web application.
- Use Docker Compose for real-time application development and updates.
- Share your Docker image on Docker Hub to facilitate deployment and extend
  reach.

> **Acknowledgment**
>
> Docker would like to thank [Harsh Manvar](https://github.com/harsh4870) for
> his contribution to this guide.

## Prerequisites

- You have installed the latest version of
  [Docker Desktop](/get-started/get-docker.md).
- You have a [Git client](https://git-scm.com/downloads). The examples in this
  guide use a command-line based Git client, but you can use any client.

## What is TensorFlow.js?

[TensorFlow.js](https://www.tensorflow.org/js) is an open-source JavaScript
library for machine learning that enables you to train and deploy ML models in
the browser or on Node.js. It supports creating new models from scratch or using
pre-trained models, facilitating a wide range of ML applications directly in web
environments. TensorFlow.js offers efficient computation, making sophisticated
ML tasks accessible to web developers without deep ML expertise.

## Why Use TensorFlow.js and Docker together?

- Environment consistency and simplified deployment: Docker packages
  TensorFlow.js applications and their dependencies into containers, ensuring
  consistent runs across all environments and simplifying deployment.
- Efficient development and easy scaling: Docker enhances development efficiency
  with features like hot reloading and facilitates easy scaling of -
  TensorFlow.js applications using orchestration tools like Kubernetes.
- Isolation and enhanced security: Docker isolates TensorFlow.js applications in
  secure environments, minimizing conflicts and security vulnerabilities while
  running applications with limited permissions.

## Get and run the sample application

In a terminal, clone the sample application using the following command.

```console
$ git clone https://github.com/harsh4870/TensorJS-Face-Detection
```

After cloning the application, you'll notice the application has a `Dockerfile`.
This Dockerfile lets you build and run the application locally with nothing more
than Docker.

Before you run the application as a container, you must build it into an image.
Run the following command inside the `TensorJS-Face-Detection` directory to
build an image named `face-detection-tensorjs`.

```console
$ docker build -t face-detection-tensorjs .
```

The command builds the application into an image. Depending on your network
connection, it can take several minutes to download the necessary components the
first time you run the command.

To run the image as a container, run the following command in a terminal.

```console
$ docker run -p 80:80 face-detection-tensorjs
```

The command runs the container and maps port 80 in the container to port 80 on
your system.

Once the application is running, open a web browser and access the application
at [http://localhost:80](http://localhost:80). You may need to grant access to
your webcam for the application.

In the web application, you can change the backend to use one of the following:

- WASM
- WebGL
- CPU

To stop the application, press `ctrl`+`c` in the terminal.

## About the application

The sample application performs real-time face detection using