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= Preparing a Dataset with Label Studio =
<span style="font-size: 150%;">Welcome to the '''LogicalDOC'''<sup>™</sup> Wiki</span>
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This guide explains how to create an annotated dataset for YOLO training using Label Studio .
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*[http://docs.logicaldoc.com User Guides]
*[http://issues.logicaldoc.com Reporting bugs and feature requests]
*[http://forums.logicaldoc.com Community Forums]
*[[Translate LogicalDOC|Translate LogicalDOC]]
*[[CMIS|CMIS Interface]]
*[[WebDAV|WebDAV Interface]]
*[[LogicalDOC Joomla Explorer|LogicalDOC Joomla explorer]]
*[[LogicalDOC WordPress Explorer|LogicalDOC WordPress explorer]]
*[[Connect_Webex_Audio|Connect Webex Audio]]
*[[Release Notes|Release Notes]]
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| width="33%" valign="top" class="logicaldoc_hubbox" | <div class="logicaldoc_hubtitle">'''System Administrators'''</div> <div class="logicaldoc_boxcontents">
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*[[Quick Install]]
*[[Installation using MySQL database|Installation using MySQL]]
*[[Installation using Oracle|Installation using Oracle]]
*[[Docker|LogicalDOC Docker container]]
*[[Virtual Appliance|Virtual Appliance]]
*[[MySQL_Timezone|MySQL timezone issue]]
*[[SSL_Configuration_using_Certificate|SSL Configuration using Certificate]]
*[[SSL_Configuration_using_Java_Keystore|SSL Configuration using Java Keystore]]
*[[SwitchToJava11|Switch from Java8 to Java11 on Windows]]
<!-- *[[Switch_To_Java_11_on_Synology_and_QNAP|Switch from Java8 to Java11 on Synology and QNAP]] -->
*[[Switch_to_Java17_on_Windows|Switch from Java11 to Java17 on Windows]]
*[[Switch_to_Java17_on_Linux|Switch from Java11 to Java17 on Linux]]
*[[Switch_to_Java21_on_Windows|Switch from Java17 to Java21 on Windows]]
*[[Switch_to_Java21_on_Linux|Switch from Java17 to Java21 on Linux]]
*[[Tuning_Memory_Usage_On_Windows|Tuning memory usage on Windows]]
*[[Changing_Java_Virtual_Machine_On_Windows|Changing the Java Virtual Machine on Windows]]
*[[Java Options|Java Options]]
*[[Server Migration|How to migrate to a new server]]
*[[Docker Migration|How to migrate to a new docker container]]
*[[How to configure an Apache load balancer]]
*[[IndexConverter|How to prepare the Index for the next generation LogicalDOC 8.5]]
*[[Upgrade Tesseract OCR|Upgrade Tesseract OCR]]
*[[Upgrade Docker container|Recommended Update Procedure for Docker container]]
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=== Install Label Studio ===
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*[[Web Services|Web Services API]]
*[[RESTful Guide with curl|RESTful Guide with curl]]
*[[Building from sources|Building from the Sources]]
*[[Developer Guide|Developer Guide]]
*[[Java_Options|Java Options]]
*[[Plug-in System|Plug-in System]]
*[[Blueprints|Blueprints]]
*[http://logicaldoc.sourceforge.net/site/ Maven2 generated Site]
*[[BPM: Workflowsystem|BPM Workflow System]]
*[[Architecture|Architecture]]
*[[Benchmark LogicalDOC|Benchmark]]
*[[External Authentication|External Authentication]]
*[[Store filenames with emoticons|Store filenames with emoticons]]
*[[Label Studio Guide]]
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<br> '''LogicalDOC''' is a Web-based, open source document management software that is very simple to use and suitable for organizations of any size and type.<br>
Refer to the official installation guide:
https://labelstud .io/guide/install
LogicalDOC uses the best-of-breed Java technologies such as Spring, Hibernate and AJAX and can run on any system, from Windows to Linux or MAC OS X.
=== Enable Local File Storage ===
The features included in the community edition — including workflow light, version control and the full-text search engine - help manage the document lifecycle, encourage cooperation, allow to quickly find the document you need without wasting time.
For large projects it is not recommended to upload images directly through the Label Studio interface .
The application is implemented as a plugin system that allows you to easily add new features through the ability to engage the various extension points predisposed. Moreover, the presence of Web services ensures that LogicalDOC can be easily integrated with other systems.
To enable local file access , configure the following environment variables:
<br> LogicalDOC Community Edition is licensed under the [http://www.gnu.org/licenses/lgpl-3.0.html GNU LGPL version 3.0]
<pre >
LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true
LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT= /path /to /images
< /pre>
<br> LogicalDOC is available in several flavors:
Launch Label Studio :
*'''[https://www.logicaldoc.com/download-logicaldoc-community LogicalDOC Community Edition]:''' A free download, with access to the source code under the LGPLv3 open source license
<pre>
*'''[https://www.logicaldoc.com/try LogicalDOC Enterprise Edition]:''' A full-featured and commercially supported edition of LogicalDOC
label -studio start
< /pre>
<br>
=== Create a Project ===
<div style="text-align: center;">
[https://www.logicaldoc.com/product/overview '''About LogicalDOC'''] | [https://www.logicaldoc.com/try '''Download Software'''] | [https://www.logicaldoc.com/support/professional-support '''Professional Support'''] | [http://docs.logicaldoc.com '''Documentation'''] <br>
# Login to Label Studio
</div>
# Click '''Create Project'''
# Enter a project name
# Configure the labeling interface
# Save the project
=== Import Images == =
# Open the project
# Click '''Import'''
# Select '''Local Storage'''
When importing images, choose '''Files ''' as the import method.
Unlike CVAT, Label Studio creates one task for each imported document image.
[[File:LabelStudio-import-method.png|thumb|600px|center|Selecting the Files import method ]]
=== Annotate Documents ===
# Open a task
# Select a label
# Draw a bounding box around the target area
# Save the annotation
Example labels:
* Invoice Number
* Date
* Seller Name
* Buyer Name
* Total Amount
[ [File :LabelStudio-annotation-example .png|thumb|600px|center|Example annotation]]
=== Export the Dataset ===
# Open the project
# Click '''Export '''
# 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.
Preparing a Dataset with Label Studio This guide explains how to create an annotated dataset for YOLO training using Label Studio.
Install Label Studio 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.
To enable local file access, configure the following environment variables:
LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true
LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/path/to/images
Launch Label Studio:
label-studio start
Create a Project Login to Label Studio
Click Create Project
Enter a project name
Configure the labeling interface
Save the project Import Images Open the project
Click Import
Select Local Storage When importing images, choose Files as the import method.
Unlike CVAT, Label Studio creates one task for each imported document image.
File:LabelStudio-import-method.png Selecting the Files import method Annotate Documents Open a task
Select a label
Draw a bounding box around the target area
Save the annotation Example labels:
Invoice Number
Date
Seller Name
Buyer Name
Total Amount File:LabelStudio-annotation-example.png Example annotation Export the Dataset Open the project
Click Export
Select the desired format Supported formats include:
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.
