How to Create an Image Labeling Project in Label Studio

Label Studio is an open-source data labeling tool from HumanSignal. It handles images, text, audio, and video, and it stores annotations in a format you can feed straight into training pipelines.

Quick answer: Install with pip install label-studio, run label-studio start, create a project, pick the Image Object Detection template, and import your files. The open-source edition is free and self-hosted.

Labeling is the part of ML that no one enjoys and everyone underestimates. Getting the tool right saves hours per dataset.

How do you install and start Label Studio?

pip install label-studio
label-studio start

It opens on http://localhost:8080. The first run asks you to create an account, which stays local to your machine. There is no cloud requirement in the open-source version.

How do you configure an image labeling project?

Create a project, then pick a template. For object detection the Image Object Detection template gives you a rectangle tool and an image canvas. The labeling config is XML:

<View>
  <Image name="image" value="$image"/>
  <RectangleLabels name="label" toName="image">
    <Label value="person" background="#FFA39E"/>
    <Label value="vehicle" background="#D4380D"/>
  </RectangleLabels>
</View>

Each Label becomes a button in the UI. Keep the list short at first — ten labels is manageable, forty is where annotators start disagreeing.

How do you import data?

Upload files directly, or point at a cloud bucket with pre-signed URLs. For large image sets you want the bucket route; uploading gigabytes through the browser is slow and fragile.

[{"image": "s3://bucket/img001.jpg"}, {"image": "s3://bucket/img002.jpg"}]

Import a JSON list like this and Label Studio pulls each image into the labeling view.

How do you export annotations?

Export as JSON, COCO, YOLO, or CSV. COCO and YOLO are the two that most training frameworks read without conversion, so pick based on your model repo.

Format	Best for
JSON	Custom pipelines
COCO	Detectron2, MMDetection
YOLO	YOLO training repos
CSV	Spreadsheet review

Is Label Studio free?

The open-source version is free under Apache 2.0 and runs on your own hardware. Label Studio Enterprise is a paid product with project management, quality control, and SSO. For a small team the free edition is usually plenty.

How do you keep annotation quality up?

• Write a labeling guide with example images for each class before anyone starts.
• Label 20 items yourself first; you will fix the class definitions once reality hits.
• Use overlap — assign the same image to two annotators and measure agreement.
• Review disagreements rather than majority-voting them into the training set.

What are the limits?

Self-hosting means you own the server, backups, and upgrades. The free edition lacks the built-in quality dashboards of the paid tier, so you track agreement yourself. Very large image sets need a proper database and object storage behind it, not the default SQLite and local files.

Who this suits: teams labeling their own data who want to keep it in-house. Who should skip it: if you would rather pay an outsourced labeling service and never touch the tooling, this adds work you do not want.

Quick Summary

• Label Studio is a free, self-hosted, open-source data labeling tool.
• Install with pip install label-studio and run label-studio start.
• Image Object Detection template uses an XML config with RectangleLabels.
• Export to COCO or YOLO to feed training frameworks directly.
• Budget time for a labeling guide and overlap checks — quality beats speed.