Java Options

Preparing a Dataset with Label Studio

This guide explains how to create an annotated dataset for YOLO training using Label Studio.

Install Label Studio using pip:

pip install label-studio

Verify the installation:

python -m label_studio.server --help

Or refer to the official installation guide: https://labelstud.io/guide/install

Enable Local File Storage

For large projects it is not recommended to upload images directly through the Label Studio interface. Instead, configure a local directory that contains the images to annotate.

To enable local file access, configure the following environment variables before starting Label Studio:

LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true
LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/path/to/images

Starting Label Studio

Label Studio can be started using one of the following methods.

Default Startup

If local file storage is not required, Label Studio can be started with the default configuration:

label-studio start

or

python -m label_studio.server start

By default, the application is available at:

http://localhost:8080

Startup with Local File Storage

When working with large datasets, it is recommended to configure Local Storage so that images are accessed directly from the filesystem.

Windows example:

set LABEL_STUDIO_PORT=8081
set LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true
set LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=C:\Users\username\Documents\label-studio

python -m label_studio.server start

After startup, Label Studio will be available at:

http://localhost:8081

The directory specified by LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT can then be configured as Local Storage within a Label Studio project.

Port 8081 is used to avoid conflicts with the default LogicalDOC installation, which typically runs on port 8080.

Create a Project

1. Login to Label Studio
2. Click Create Project
3. Enter a project name
4. Configure the labeling interface
5. Save the project

Import Images

1. Open the project
2. Click Import
3. Select Local Storage

Configure Local Storage

1. Open the project
2. Navigate to Settings > Cloud Storage
3. Click Add Source Storage
4. Select Local Files
5. Configure the path specified by LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT
6. Click Sync Storage

Local Storage button

Local Storage Selection

Local Storage configuration showing a synchronized directory of document images

After synchronization, Label Studio automatically creates one task for each imported document image.

When importing images, choose Files as the import method.

Annotate Documents

1. Open a task
2. Select a label
3. Draw a bounding box around the target area
4. Save the annotation

Example labels:

• Invoice Number
• Date
• Seller Name
• Buyer Name
• Total Amount

Example annotation

Export the Dataset

1. Open the project
2. Click Export
3. Select the desired format

Supported formats include:

• YOLO
• COCO
• Pascal VOC
• CSV

For YOLO training, export the dataset in YOLO format.

Dataset Formats

COCO

COCO is a JSON-based dataset format commonly used for object detection datasets.

More information: https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-coco-overview.html

YOLO

YOLO datasets contain images and annotation files organized according to a predefined directory structure.

More information: https://docs.cvat.ai/docs/dataset_management/formats/format-yolo/

YOLOv8 OBB

YOLOv8 OBB (Oriented Bounding Boxes) extends the standard YOLO format by supporting rotated bounding boxes using eight normalized coordinates.