How to Create Project Using SDK - Labellerr Documentation

Creating a New Project

Project Overview

A project in Labellerr organizes your datasets and their annotations. Use the following method to create a project:

Limitations:

Maximum of 2,500 files per folder.
Total folder size should not exceed 2.5 GB.

Acceptable value:

option_type
- 'input', 'radio', 'boolean', 'select', 'dropdown', 'stt', 'imc', 'BoundingBox', 'polygon', 'dot', 'audio'

Data Types and Supported Formats

The Labellerr SDK supports various data types and file formats for your annotation projects:

Supported Data Types and Extensions

Data Type	Description	Supported Extensions
image	Image files for visual annotation	.jpg, .jpeg, .png, .bmp, .tiff
video	Video content for temporal annotation	.mp4
audio	Audio files for sound annotation	.mp3, .wav
document	Document files for text analysis	.pdf
text	Plain text files for text annotation	.txt

When using the SDK methods, specify the data type as one of: 'image', 'video', 'audio', 'document', or 'text'.When using the initiate_create_project() function, you must provide either:

annotation_guide (as a dictionary), or
annotation_template_id (as a string)

Important: At least one of these two fields is required. If both are missing, the project creation will fail.

Example: Creating a Project with an Existing Template

Project Creation with Template

from labellerr.client import LabellerrClient
from labellerr.exceptions import LabellerrError

# Initialize the client with your API credentials
client = LabellerrClient(api_key, api_secret)
project_payload = {
    'client_id': client_id,
    'dataset_name': 'New_dataset',
    'dataset_description': 'A sample dataset for image classification',
    'data_type': 'image',
    'created_by': email,
    'project_name': 'Image Classification Project',
    'annotation_template_id':"existing annotaion_template_id",
    'rotation_config': {
        'annotation_rotation_count': 1,
        'review_rotation_count': 1,
        'client_review_rotation_count': 1
    },
    'autolabel': False,
    'folder_to_upload': 'path/to/your/dataset'
}
try:
    result = client.initiate_create_project(project_payload)
    print(f"Project created successfully. Project ID: {result['project_id']['response']['project_id']}")
except LabellerrError as e:
    print(f"Project creation failed: {str(e)}")

Example: Creating a Project with Inline Annotation Questions

You can also add classification questions and annotation questions while creating the project.

Project Creation with Inline Annotation Guide

from labellerr.client import LabellerrClient
from labellerr.exceptions import LabellerrError

# Initialize the client with your API credentials
client = LabellerrClient(api_key, api_secret)
project_payload = {
    'client_id': client_id,
    'dataset_name': 'New_dataset',
    'dataset_description': 'A sample dataset for image classification',
    'data_type': 'image',
    'created_by': email,
    'project_name': 'Image Classification Project',
    'annotation_guide': [
        {
            "question_number": 1,
            "question": "What is the main object in the image?",
            "question_id": "533bb0c8-fb2b-4394-a8e1-5042a944802f",
            "option_type": "dropdown",
            "required": True,
            "options": [
                {"option_name": "Car"},
                {"option_name": "Building"},
                {"option_name": "Person"}
            ]
        }
    ],
    'rotation_config': {
        'annotation_rotation_count': 1,
        'review_rotation_count': 1,
        'client_review_rotation_count': 1
    },
    'autolabel': False,
    'folder_to_upload': 'path/to/your/dataset'
}
try:
    result = client.initiate_create_project(project_payload)
    print(f"Project created successfully. Project ID: {result['project_id']['response']['project_id']}")
except LabellerrError as e:
    print(f"Project creation failed: {str(e)}")

Glossary

Project Payload Fields

Variable/Key	Type	Description	Example Value
client_id	String	Unique identifier for the client or organization creating the project	client_123
dataset_name	String	Name of the dataset being created	New_dataset
dataset_description	String	Description explaining the dataset’s purpose and contents	A sample dataset for image classification
data_type	String	Type of data in the dataset	image
created_by	String	Email address of the project creator	user@example.com
project_name	String	Display name for the annotation project	Image Classification Project
annotation_template_id	String	ID of existing annotation template defining label structure	template_456
autolabel	Boolean	Enable/disable automatic AI pre-labeling	false
folder_to_upload	String	Local file path to dataset folder	path/to/your/dataset
annotation_rotation_count	Integer	Number of times data item need to be annotated	1
review_rotation_count	Integer	Number of times annotated item need to be reviewed	1
client_review_rotation_count	Integer	Number of times client reviewers need to be done.	1

SDK Documentation

​Creating a New Project