Pre-trained TensorFlow.js models

This repository hosts a set of pre-trained models that have been ported to
TensorFlow.js.

The models are hosted on NPM and unpkg so they can be used in any project out of the box. They can be used directly or used in a transfer learning
setting with TensorFlow.js.

To find out about APIs for models, look at the README in each of the respective
directories. In general, we try to hide tensors so the API can be used by
non-machine learning experts.

For those interested in contributing a model, please file a GitHub issue on tfjs to gauge
interest. We are trying to add models that complement the existing set of models
and can be used as building blocks in other apps.

Models

<table style="max-width:100%;table-layout:auto;">
<tr style="text-align:center;">
<th>Type</th>
<th>Model</th>
<th>Demo</th>
<th>Details</th>
<th>Install</th>
</tr>

<tr>
<td rowspan="12"><b>Images</b></td>
<td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./mobilenet">

</a></b></td>
<td><a href="https://storage.googleapis.com/tfjs-models/demos/mobilenet/index.html">live</a></td>
<td rowspan="2">Classify images with labels from the <a href="http://www.image-net.org/">ImageNet database</a>.</td>
<td rowspan="2"><code>npm i @tensorflow-models/mobilenet</code></td>
</tr>
<tr>
<td><a href="./mobilenet/demo/index.html">source</a></td>
</tr>

<tr>
<td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./hand-pose-detection">

</a></b></td>
<td><a href="https://storage.googleapis.com/tfjs-models/demos/hand-pose-detection/index.html?model=mediapipe_hands">live</a></td>
<td rowspan="2">Real-time hand pose detection in the browser using TensorFlow.js.</td>
<td rowspan="2"><code>npm i @tensorflow-models/hand-pose-detection</code></td>
</tr>
<tr>
<td><a href="./hand-pose-detection/demos/live_video/index.html">source</a></td>
</tr>

<tr>
<td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./pose-detection">

</a></b></td>
<td><a href="https://storage.googleapis.com/tfjs-models/demos/pose-detection/index.html?model=movenet">live</a></td>
<td rowspan="2">An API for real-time human pose detection in the browser.</td>
<td rowspan="2"><code>npm i @tensorflow-models/pose-detection</code></td>
</tr>
<tr>
<td><a href="./pose-detection/demos/live_video/index.html">source</a></td>
</tr>

<tr>
<td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./coco-ssd">

</a></b></td>
<td><a href=""></a></td>
<td rowspan="2">Object detection model that aims to localize and identify multiple objects in a single image. Based on the <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/README.md">TensorFlow object detection API</a>.</td>
<td rowspan="2"><code>npm i @tensorflow-models/coco-ssd</code></td>
</tr>
<tr>
<td><a href="./coco-ssd/demo">source</a></td>
</tr>
<td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./deeplab">

</a></b></td>
<td><a href=""></a></td>
<td rowspan="2">Semantic segmentation</td>
<td rowspan="2"><code>npm i @tensorflow-models/deeplab</code></td>
</tr>
<tr>
<td><a href="./deeplab/demo">source</a></td>
</tr>

<tr>
<td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./face-landmarks-detection">

</a></b></td>
<td><a href="https://storage.googleapis.com/tfjs-models/demos/face-landmarks-detection/index.html?model=mediapipe_face_mesh">live</a></td>
<td rowspan="2">Real-time 3D facial landmarks detection to infer the approximate surface geometry of a human face</td>
<td rowspan="2"><code>npm i @tensorflow-models/face-landmarks-detection</code></td>
</tr>
<tr>
<td><a href="./face-landmarks-detection/demos">source</a></td>
</tr>

<tr>
<td rowspan="2"><b>Audio</b></td>
<td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./speech-commands">

</a></b></td>
<td><a href="https://storage.googleapis.com/tfjs-speech-model-test/2019-01-03a/dist/index.html">live</a></td>
<td rowspan="2">Classify 1 second audio snippets from the <a href="https://www.tensorflow.org/tutorials/audio/simple_audio">speech commands dataset</a>.</td>
<td rowspan="2"><code>npm i @tensorflow-models/speech-commands</code></td>
</tr>
<tr>
<td><a href="./speech-commands/demo/index.html">source</a></td>
</tr>

<tr>
<td rowspan="4"><b>Text</b></td>
<td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./universal-sentence-encoder">

</a></b></td>
<td><a href=""></a></td>
<td rowspan="2">Encode text into a 512-dimensional embedding to be used as inputs to natural language processing tasks such as sentiment classification and textual similarity.</td>
<td rowspan="2"><code>npm i @tensorflow-models/universal-sentence-encoder</code></td>
</tr>
<tr>
<td><a href="./universal-sentence-encoder/demo">source</a></td>
</tr>

<tr>
<td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./toxicity">

</a></b></td>
<td><a href="https://storage.googleapis.com/tfjs-models/demos/toxicity/index.html">live</a></td>
<td rowspan="2">Score the perceived impact a comment might have on a conversation, from "Very toxic" to "Very healthy".</td>
<td rowspan="2"><code>npm i @tensorflow-models/toxicity</code></td>
</tr>
<tr>
<td><a href="./toxicity/demo/index.html">source</a></td>
</tr>

<tr>
<td rowspan="2"><b>Depth Estimation</b></td>
<td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./depth-estimation">

</a></b></td>
<td><a href="https://storage.googleapis.com/tfjs-models/demos/3dphoto/index.html">live</a></td>
<td rowspan="2">Estimate per-pixel depth (the distance to the camera center) for a single portrait image, which can be further used for creative applications such as <a href="https://blog.tensorflow.org/2022/05/portrait-depth-api-turning-single-image.html?linkId=8063793">3D photo</a> and <a href="https://storage.googleapis.com/tfjs-models/demos/relighting/index.html">relighting</a>.</td>
<td rowspan="2"><code>npm i @tensorflow-models/depth-estimation</code></td>
</tr>
<tr>
<td><a href="./depth-estimation/demos/3d_photo/index.html">source</a></td>
</tr>

<tr>
<td rowspan="2"><b>General Utilities</b></td>

<td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./knn-classifier">

</a></b></td>
<td><a href=""></a></td>
<td rowspan="2">This package provides a utility for creating a classifier using the K-Nearest Neighbors algorithm. Can be used for transfer learning.</td>
<td rowspan="2"><code>npm i @tensorflow-models/knn-classifier</code></td>
</tr>
<tr>
<td><a href="./knn-classifier/demo">source</a></td>
</tr>
</table>

Development

You can run the unit tests for any of the models by running the following
inside a directory:

yarn test

New models should have a test NPM script (see this package.json and run_tests.ts helper for reference).

To run all of the tests, you can run the following command from the root of this
repo:

yarn presubmit