VoTT | Visual Object Tagging Tool : An electron app | Data Labeling library

by microsoft TypeScript Version: v2.2.0 License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | VoTT Summary

VoTT is a TypeScript library typically used in Artificial Intelligence, Data Labeling, React applications. VoTT has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

An open source annotation and labeling tool for image and video assets. VoTT is a React + Redux Web application, written in TypeScript. This project was bootstrapped with Create React App.

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Quality

Security

License

Reuse

Support

VoTT has a medium active ecosystem.

It has 4041 star(s) with 813 fork(s). There are 115 watchers for this library.

It had no major release in the last 12 months.

There are 233 open issues and 241 have been closed. On average issues are closed in 107 days. There are 13 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of VoTT is v2.2.0

Quality

VoTT has 0 bugs and 0 code smells.

Security

VoTT has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

VoTT code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

VoTT is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

VoTT releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 10118 lines of code, 0 functions and 276 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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VoTT Key Features

No Key Features are available at this moment for VoTT.

VoTT Examples and Code Snippets

No Code Snippets are available at this moment for VoTT.

Community Discussions

Trending Discussions on VoTT

Import Labeled Data from vott to Google Cloud AutoML

Image annotation tool that support annotation using existing CNN

QUESTION

Import Labeled Data from vott to Google Cloud AutoML

Asked 2021-Sep-13 at 09:14

I want to go ahead and create a classifier, I and I do not like the Google's Browser Labeling Service. Is there a tool similar to vott or some code, that I can use to go ahead and import my vott labeled data and import it Google AutoML.

The Google Labeling Service looks something like this and is very slow in loading images and inefficient it literally has a white labeling cursor and I have light background in my images

As seen in the Image Here.

On the Other Hand can I import it using vott which is much more better in every way. So is there a way for me to do this using vott to import the labeled csv into Google's Cloud AutoML.

...

ANSWER

Answered 2021-Sep-10 at 09:22

I don't think that it is currently possible to import already labeled data from other apps (like VOTT).

At the moment there are 3 ways to label images in Cloud Vision. It's described in the Annotating imported training images

Provide bounding boxes with labels for your training images via labeled bounding boxes in your .csv import file

In the CSV file you would need to provide GCS url and label/labels

Labeled: gs://my-storage-bucket-vcm/flowers/images/img100.jpg,daisy

Multi-label: gs://my-storage-bucket-vcm/flowers/images/img384.jpg,dandelion,tulip,rose

Assigned to a set: TEST,gs://my-storage-bucket-vcm/flowers/images/img805.jpg,daisy

More details can be found here.

Provide unannotated images in your .csv import file and use the UI to provide image annotations

Not labeled: gs://my-storage-bucket-vcm/flowers/images/img403.jpg

However, later you will need to label it using UI, otherwise it will be ignored.

AutoML Vision ignores items without a category label.

Request manual image annotation with Google's Human Labeling service

This option would include human labelers and would need to provide additional information like dataset, label set and instructions for people.

In the documentation you can also find information that currently API is not supporting any method for labeling.

The AutoML API does not currently include methods for labeling.

However, you can propose Feature Request via IssueTracker to add some additional import methods from different apps or enable the use API.

Source https://stackoverflow.com/questions/69124865

QUESTION

Image annotation tool that support annotation using existing CNN

Asked 2020-Dec-06 at 02:36

I have trained a YoloV4 CNN. It's pretty good already. I want more images as training data but there is no point of manually annotate most of the stuff because CNN can do it for me. I could review and re-correct if there are any issues. Is there a image annotation tool/service that can do that? I'm currently using Supervisely. I also tried CVAT and VoTT Couldn't find such feature.

...

ANSWER

Answered 2020-Dec-06 at 02:36

I created a simple python project to generate supervisely project using darknet. It's available on github.

https://github.com/s1n7ax/partially-annotate

Source https://stackoverflow.com/questions/65111878

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install VoTT

VoTT can be installed as a native application or run from source. VoTT is also available as a stand-alone Web application and can be used in any modern Web browser.
VoTT is available for Windows, Linux and OSX. Download the appropriate platform package/installer from GitHub Releases. v2 releases will be prefixed by 2.x.
VoTT requires NodeJS (>= 10.x, Dubnium) and NPM. IMPORTANT When running locally with npm, both the electron and the browser versions of the application will start. One major difference is that the electron version can access the local file system.

Support

There are many ways to contribute to VoTT -- please review our contribution guidelines. 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.

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