🛠️Tooling Tuesday🛠️
Good training data is like half of the task when it comes to machine learning.
There are many great tools and services for annotating your data. Today, we'll talk about labelstud.io
1/6
Good training data is like half of the task when it comes to machine learning.
There are many great tools and services for annotating your data. Today, we'll talk about labelstud.io
1/6
2/6 Many kinds of data can be annotated with Label Studio: images, audio, text, time series, and any combination of the above.
You can easily define your own annotation interface with a simple HTML-like markup.
Check out many pre-made templates at labelstud.io/playground/
You can easily define your own annotation interface with a simple HTML-like markup.
Check out many pre-made templates at labelstud.io/playground/
3/6 Label Studio is open-source and is under active development.
It has 4K stars on Github, the latest major release is just a couple of weeks ago, and pull requests are successfuly getting merged :)
You can try it yourself locally with a single Docker command.
It has 4K stars on Github, the latest major release is just a couple of weeks ago, and pull requests are successfuly getting merged :)
You can try it yourself locally with a single Docker command.
4/6 The system is built in a modular way that allows plugging in a pre-labeling or active-learning ML back-end for faster annotation.
5/6 Label Studio also provides a data manager interface for sorting, filtering, and looking through annotated and unannotated data.
6/6 Label Studio is made primarily by @heartexlabs, who offer a managed version of it for enterprise.
That's it for today!
And of course, we'd love to learn what data annotation tools you all use and like :)
That's it for today!
And of course, we'd love to learn what data annotation tools you all use and like :)
• • •
Missing some Tweet in this thread? You can try to
force a refresh
