🏷️ Label Studio is an open-source data labeling tool to label multiple data types such as audio, text, images, videos, and time series. You can export multiple model formats using a simple user interface. It is often used for data preparation to get more accurate machine learning models.
Here’s an example from the GitHub repository:
🖊️ Try It Yourself: You can try out the labeling interface in the Playground.
Install Label Studio
There are multiple ways to install it. For me, the easiest way is to use
# Requires Python >=3.7 <=3.9 pip install label-studio # Start the server at http://localhost:8080 label-studio
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If you want to install it with Docker (locally), I recommend you check out the docs. The TLDR is to run the following commands:
docker pull heartexlabs/label-studio:latest docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest
You’ll find all generated assets (database storage and uploaded files, for example) in the
Here’s how the user interface looks like:
I recommend you check out the Playground to learn if this applies to your machine-learning labeling pipeline:
There are many more data types that can be labeled using this platform, I particularly liked the Polygon segmentation:
Overall, a very helpful tool. Let’s finish with the ecosystem of the Label Studio tool:
|Server, distributed as a |
|Encode labels in the format of your favorite machine learning library|
|Transformers library connected and configured for use with Label Studio|
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