base on Pretrained models for TensorFlow.js # 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](https://github.com/tensorflow/tfjs/issues) 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> <!-- Images --> <!-- ** MobileNet --> <tr> <td rowspan="12"><b>Images</b></td> <td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./mobilenet"><div style='vertical-align:middle; display:inline;'>MobileNet</div></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> <!-- ** Hand --> <tr> <td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./hand-pose-detection"><div style='vertical-align:middle; display:inline;'>Hand</div></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> <!-- ** Pose --> <tr> <td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./pose-detection"><div style='vertical-align:middle; display:inline;'>Pose</div></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> <!-- ** Coco SSD --> <tr> <td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./coco-ssd"><div style='vertical-align:middle; display:inline;'>Coco SSD</div></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"><div style='vertical-align:middle; display:inline;'>DeepLab v3</div></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> <!-- ** Face Landmark Detection --> <tr> <td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./face-landmarks-detection"><div style='vertical-align:middle; display:inline;'>Face Landmark Detection</div></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> <!-- * Audio --> <!-- ** Speech Commands --> <tr> <td rowspan="2"><b>Audio</b></td> <td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./speech-commands"><div style='vertical-align:middle; display:inline;'>Speech Commands</div></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> <!-- * Text --> <!-- ** Universal Sentence Encoder --> <tr> <td rowspan="4"><b>Text</b></td> <td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./universal-sentence-encoder"><div style='vertical-align:middle; display:inline;'>Universal Sentence Encoder</div></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> <!-- ** Text Toxicity --> <tr> <td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./toxicity"><div style='vertical-align:middle; display:inline;'>Text Toxicity</div></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> <!-- * Depth Estimation --> <!-- ** Portrait Depth --> <tr> <td rowspan="2"><b>Depth Estimation</b></td> <td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./depth-estimation"><div style='vertical-align:middle; display:inline;'>Portrait Depth</div></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> <!-- * General Utilities --> <tr> <td rowspan="2"><b>General Utilities</b></td> <!-- ** KNN Classifier --> <td rowspan="2"><b><a style="white-space:nowrap; display:inline-block;" href="./knn-classifier"><div style='vertical-align:middle; display:inline;'>KNN Classifier</div></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](./mobilenet/package.json) `package.json` and `run_tests.ts` [helper](./mobilenet/run_tests.ts) for reference). To run all of the tests, you can run the following command from the root of this repo: `yarn presubmit` ", Assign "at most 3 tags" to the expected json: {"id":"11173","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"