NPM package for TensorFlow.js models exported from Custom Vision Service

Перейти к файлу

dependabot[bot] d7b6f6fed2 Bump braces from 3.0.2 to 3.0.3 Bumps [braces](https://github.com/micromatch/braces) from 3.0.2 to 3.0.3. - [Changelog](https://github.com/micromatch/braces/blob/master/CHANGELOG.md) - [Commits](https://github.com/micromatch/braces/compare/3.0.2...3.0.3) --- updated-dependencies: - dependency-name: braces dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com>		2024-06-17 08:54:50 -07:00
lib	Update dependencies	2021-12-19 01:44:22 -08:00
src	Fix a memory leak issue.	2021-01-14 11:52:05 -08:00
test	Support a new metadata: Image.BitmapPixelFormat	2021-01-13 19:27:27 -08:00
.gitignore	Initial commit.	2019-10-30 18:17:03 -07:00
CODE_OF_CONDUCT.md	Initial CODE_OF_CONDUCT.md commit	2019-10-29 11:15:41 -07:00
LICENSE	Initial LICENSE commit	2019-10-29 11:15:42 -07:00
README.md	Update README	2021-12-19 01:55:20 -08:00
SECURITY.md	Initial SECURITY.md commit	2019-10-29 11:15:44 -07:00
package-lock.json	Bump braces from 3.0.2 to 3.0.3	2024-06-17 08:54:50 -07:00
package.json	Bump flat and mocha	2023-01-10 13:07:26 -08:00
rollup.config.js	Initial commit.	2019-10-30 18:17:03 -07:00

README.md

customvision-tfjs

NPM package for TensorFlow.js models exported from Custom Vision Service

This package is for web browsers. If you are looking for a library to run on Node.js environment, please use customvision-tfjs-node.

Install

npm install @microsoft/customvision-tfjs

Or, if you would like to use CDN,

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@3.12.0/dist/tf.min.js"></script>
<script src="https://unpkg.com/@microsoft/customvision-tfjs@1.3.0"></script>

Usage

<img id="image" src="test_image.jpg" />

Classification

import * as cvstfjs from '@microsoft/customvision-tfjs';

let model = new cvstfjs.ClassificationModel();
await model.loadModelAsync('model.json');
const image = document.getElementById('image');
const result = await model.executeAsync(image);

The result is a 1D-array of probabilities.

Object Detection

import * as cvstfjs from '@microsoft/customvision-tfjs';

let model = new cvstfjs.ObjectDetectionModel();
await model.loadModelAsync('model.json');
const image = document.getElementById('image');
const result = await model.executeAsync(image);

The result has 3 arrays.


[
	[[0.1, 0.3, 0.4, 0.3], [0.2, 0.4, 0.8, 0.9]], // bounding boxes (x1, y1, x2, y2)
	[0.2, 0.3], // probabilities
	[1, 4] // class ids
]

References

sample scripts

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.