Create Projects - Labellerr Documentation

Overview

A project in Labellerr combines datasets with annotation guidelines to create a structured workflow for your team. This guide covers everything you need to create projects programmatically using the SDK.

Project Creation Requirements

To create a project, you need three components:

Dataset(s) - One or more datasets containing your files
Annotation Template - Questions and labels defining your annotation schema
Project Configuration - Settings for rotations, AI features, and workflow rules

All components use validated schemas to catch errors before making API calls.

Supported Data Types

image - .jpg, .jpeg, .png, .bmp, .tiff
video - .mp4
audio - .mp3, .wav
document - .pdf
text - .txt

Quick Start

Here’s the minimal code to create a project:

from labellerr.client import LabellerrClient
from labellerr.core.datasets import create_dataset_from_local
from labellerr.core.annotation_templates import create_template
from labellerr.core.projects import create_project
from labellerr.core.schemas import *
import uuid

# Initialize client
client = LabellerrClient(
    api_key='your_api_key',
    api_secret='your_api_secret',
    client_id='your_client_id'
)

# 1. Create dataset
dataset = create_dataset_from_local(
    client=client,
    dataset_config=DatasetConfig(dataset_name="My Dataset", data_type="image"),
    folder_to_upload="path/to/images"
)

# Wait for dataset to be ready
dataset.status()  # Polls until dataset processing is complete

# 2. Create annotation template
template = create_template(
    client=client,
    params=CreateTemplateParams(
        template_name="My Template",
        data_type=DatasetDataType.image,
        questions=[
            AnnotationQuestion(
                question_number=1,
                question="Object",
                question_id=str(uuid.uuid4()),
                question_type=QuestionType.bounding_box,
                required=True,
                color="#FF0000"
            )
        ]
    )
)

# 3. Create project
project = create_project(
    client=client,
    params=CreateProjectParams(
        project_name="My Project",
        data_type=DatasetDataType.image,
        rotations=RotationConfig(
            annotation_rotation_count=1,
            review_rotation_count=1,
            client_review_rotation_count=1
        )
    ),
    datasets=[dataset],
    annotation_template=template
)

print(f"✓ Project created: {project.project_id}")

Step-by-Step Guide

Step 1: Prepare Your Dataset

You can either create a new dataset or use an existing one.

Create New Dataset
Use Existing Dataset

from labellerr.core.datasets import create_dataset_from_local
from labellerr.core.schemas import DatasetConfig

# From local folder
dataset = create_dataset_from_local(
    client=client,
    dataset_config=DatasetConfig(
        dataset_name="Training Images",
        data_type="image"
    ),
    folder_to_upload="path/to/images"
)

# Wait for dataset processing to complete
dataset.status()

Step 2: Create Annotation Template

Define the questions your annotators will answer.

Simple Template
Multiple Questions
Use Existing Template

from labellerr.core.annotation_templates import create_template
from labellerr.core.schemas import *
import uuid

# Single bounding box question
template = create_template(
    client=client,
    params=CreateTemplateParams(
        template_name="Object Detection",
        data_type=DatasetDataType.image,
        questions=[
            AnnotationQuestion(
                question_number=1,
                question="Detect Vehicle",
                question_id=str(uuid.uuid4()),
                question_type=QuestionType.bounding_box,
                required=True,
                color="#00FF00"
            )
        ]
    )
)

Step 3: Configure & Create Project

Now combine your dataset and template to create the project.

from labellerr.core.projects import create_project
from labellerr.core.schemas import CreateProjectParams, RotationConfig, DatasetDataType

project = create_project(
    client=client,
    params=CreateProjectParams(
        project_name="Vehicle Detection Project",
        data_type=DatasetDataType.image,
        rotations=RotationConfig(
            annotation_rotation_count=1,  # Each file annotated once
            review_rotation_count=1,      # Reviewed once
            client_review_rotation_count=1  # Client review once
        ),
        use_ai=False,  # Optional: Enable AI assistance
        created_by="[email protected]"  # Optional: Creator email
    ),
    datasets=[dataset],
    annotation_template=template
)

# Access project details
print(f"Project ID: {project.project_id}")
print(f"Data Type: {project.data_type}")
print(f"Template: {project.annotation_template_id}")
print(f"Datasets: {project.attached_datasets}")

Common Scenarios

Using Multiple Datasets

Attach multiple datasets to a single project for larger annotation workflows:

Homogeneous Data RequirementAll datasets attached to a single project must be of the same data type (e.g., all image, all video, etc.). You cannot mix different data types in the same project.

from labellerr.core.datasets import LabellerrDataset

# Get multiple existing datasets
dataset1 = LabellerrDataset(client=client, dataset_id="dataset_id_1")
dataset2 = LabellerrDataset(client=client, dataset_id="dataset_id_2")
dataset3 = LabellerrDataset(client=client, dataset_id="dataset_id_3")

# Ensure all datasets are ready
for dataset in [dataset1, dataset2, dataset3]:
    dataset.status()

# Create project with all datasets
project = create_project(
    client=client,
    params=CreateProjectParams(
        project_name="Large Scale Annotation",
        data_type=DatasetDataType.image,
        rotations=RotationConfig(1, 1, 1)
    ),
    datasets=[dataset1, dataset2, dataset3],  # List of datasets
    annotation_template=template
)

Multiple Annotation Rotations

For high-quality annotations, require multiple annotators per file:

project = create_project(
    client=client,
    params=CreateProjectParams(
        project_name="High Quality Annotations",
        data_type=DatasetDataType.image,
        rotations=RotationConfig(
            annotation_rotation_count=3,  # 3 annotators per file
            review_rotation_count=2,      # 2 reviewers per file
            client_review_rotation_count=1
        )
    ),
    datasets=[dataset],
    annotation_template=template
)

Video Project with Keyframes

For video annotation projects:

from labellerr.core.schemas import DatasetDataType

# Create video dataset
video_dataset = create_dataset_from_local(
    client=client,
    dataset_config=DatasetConfig(
        dataset_name="Traffic Videos",
        data_type="video"
    ),
    folder_to_upload="path/to/videos"
)

# Wait for dataset processing to complete
video_dataset.status()

# Create video template
video_template = create_template(
    client=client,
    params=CreateTemplateParams(
        template_name="Vehicle Tracking",
        data_type=DatasetDataType.video,
        questions=[
            AnnotationQuestion(
                question_number=1,
                question="Track Vehicle",
                question_id=str(uuid.uuid4()),
                question_type=QuestionType.bounding_box,
                required=True,
                color="#FF0000"
            )
        ]
    )
)

# Create video project
video_project = create_project(
    client=client,
    params=CreateProjectParams(
        project_name="Vehicle Tracking Project",
        data_type=DatasetDataType.video,
        rotations=RotationConfig(1, 1, 1)
    ),
    datasets=[video_dataset],
    annotation_template=video_template
)

Schema Reference

CreateProjectParams

Project Configuration Parameters

Parameter	Type	Required	Description	Example
`project_name`	str	Yes	Display name for your project	”Vehicle Detection”
`data_type`	DatasetDataType	Yes	Type of data being annotated	DatasetDataType.image
`rotations`	RotationConfig	Yes	Annotation and review cycle settings	RotationConfig(1, 1, 1)
`use_ai`	bool	No	Enable AI-assisted annotation	True / False (default)
`created_by`	str	No	Email of project creator	”[email protected]”

The data_type must match the data type of all attached datasets.

RotationConfig

Rotation Configuration

Defines how many times each file goes through annotation and review cycles.Constructor:

RotationConfig(
    annotation_rotation_count: int,
    review_rotation_count: int,
    client_review_rotation_count: int
)

Parameter	Type	Min Value	Description
`annotation_rotation_count`	int	1	Number of annotators per file
`review_rotation_count`	int	1	Number of reviewers per file
`client_review_rotation_count`	int	1	Number of client reviewers

Examples:

# Standard workflow: 1 annotator, 1 reviewer
RotationConfig(1, 1, 1)

# High quality: 3 annotators, 2 reviewers
RotationConfig(3, 2, 1)

# Review-focused: 1 annotator, 3 reviewers
RotationConfig(1, 3, 1)

Higher rotation counts improve annotation quality but increase project time and cost. Balance based on your accuracy requirements.

Free User Rotation LimitationsFor users on the Free Plan, rotation configuration is limited to the basic workflow:Available Configuration:

RotationConfig(1, 1, 1) - Only 1:1:1 rotation is available

This means:

✅ 1 annotator per file
✅ 1 reviewer per file
✅ 1 client reviewer per file

Upgrade for Advanced Rotations:

RotationConfig(3, 2, 1) - Requires paid plan
RotationConfig(2, 2, 1) - Requires paid plan
Any configuration with values > 1 - Requires paid plan

To unlock higher rotation counts and improve annotation quality through multiple annotators/reviewers, please upgrade your plan.

QuestionType Enum

Available Question Types

Choose the appropriate question type for your annotation needs.Object-Based Questions (Drawing):

Type	Value	Use Case	Requires Color
`QuestionType.bounding_box`	”BoundingBox”	Rectangular object detection	✅ Yes
`QuestionType.polygon`	”polygon”	Irregular shape segmentation	✅ Yes
`QuestionType.polyline`	”polyline”	Line/path annotation	✅ Yes
`QuestionType.dot`	”dot”	Point marking (landmarks, keypoints)	✅ Yes

Classification Questions (Selection):

Type	Value	Use Case	Requires Options
`QuestionType.dropdown`	”dropdown”	Single selection from many options	✅ Yes
`QuestionType.radio`	”radio”	Single selection with visible options	✅ Yes
`QuestionType.select`	”select”	Multiple selections allowed	✅ Yes
`QuestionType.boolean`	”boolean”	Yes/No or True/False questions	✅ Yes (2 options)
`QuestionType.input`	”input”	Free text entry	❌ No

Example Usage:

# Object detection (requires color)
AnnotationQuestion(
    question_type=QuestionType.bounding_box,
    color="#FF0000",  # Required for object types
    ...
)

# Classification (requires options)
AnnotationQuestion(
    question_type=QuestionType.dropdown,
    options=[  # Required for classification types
        Option(option_name="Option 1"),
        Option(option_name="Option 2")
    ],
    ...
)

# Text input (no options needed)
AnnotationQuestion(
    question_type=QuestionType.input,
    # No color or options required
    ...
)

Complete End-to-End Example

Here’s a production-ready example with error handling:

from labellerr.client import LabellerrClient
from labellerr.core.datasets import create_dataset_from_local
from labellerr.core.annotation_templates import create_template
from labellerr.core.projects import create_project
from labellerr.core.schemas import *
from labellerr.core.exceptions import LabellerrError
import uuid

def create_annotation_project():
    """Create a complete annotation project from scratch."""
    
    # Initialize client
    client = LabellerrClient(
        api_key='your_api_key',
        api_secret='your_api_secret',
        client_id='your_client_id'
    )
    
    try:
        # Step 1: Create dataset
        print("📦 Creating dataset...")
        dataset = create_dataset_from_local(
            client=client,
            dataset_config=DatasetConfig(
                dataset_name="Production Images Q1 2024",
                dataset_description="Customer submitted images for annotation",
                data_type="image"
            ),
            folder_to_upload="data/images"
        )
        print(f"   ✓ Dataset created: {dataset.dataset_id}")
        print(f"   Files count: {dataset.files_count}")
        
        # Wait for dataset processing to complete
        print("\n⏳ Waiting for dataset to be ready...")
        dataset.status()
        print("   ✓ Dataset is ready!")
        
        # Step 2: Create annotation template
        print("\n📋 Creating annotation template...")
        questions = [
            AnnotationQuestion(
                question_number=1,
                question="Vehicle Detection",
                question_id=str(uuid.uuid4()),
                question_type=QuestionType.bounding_box,
                required=True,
                color="#FF5733"
            ),
            AnnotationQuestion(
                question_number=2,
                question="Vehicle Category",
                question_id=str(uuid.uuid4()),
                question_type=QuestionType.dropdown,
                required=True,
                options=[
                    Option(option_name="Sedan"),
                    Option(option_name="SUV"),
                    Option(option_name="Truck"),
                    Option(option_name="Van"),
                    Option(option_name="Motorcycle")
                ]
            ),
            AnnotationQuestion(
                question_number=3,
                question="Image Clear?",
                question_id=str(uuid.uuid4()),
                question_type=QuestionType.radio,
                required=True,
                options=[
                    Option(option_name="Clear"),
                    Option(option_name="Blurry"),
                    Option(option_name="Dark")
                ]
            )
        ]
        
        template = create_template(
            client=client,
            params=CreateTemplateParams(
                template_name="Vehicle Detection Template Q1",
                data_type=DatasetDataType.image,
                questions=questions
            )
        )
        print(f"   ✓ Template created: {template.annotation_template_id}")
        
        # Step 3: Create project
        print("\n🚀 Creating project...")
        project = create_project(
            client=client,
            params=CreateProjectParams(
                project_name="Vehicle Detection - Q1 2024",
                data_type=DatasetDataType.image,
                rotations=RotationConfig(
                    annotation_rotation_count=2,  # Double annotation for quality
                    review_rotation_count=1,
                    client_review_rotation_count=1
                ),
                use_ai=True,  # Enable AI assistance
                created_by="[email protected]"
            ),
            datasets=[dataset],
            annotation_template=template
        )
        
        print(f"\n{'='*60}")
        print(f"✅ PROJECT CREATED SUCCESSFULLY")
        print(f"{'='*60}")
        print(f"Project ID:       {project.project_id}")
        print(f"Project Name:     Vehicle Detection - Q1 2024")
        print(f"Data Type:        {project.data_type}")
        print(f"Template ID:      {project.annotation_template_id}")
        print(f"Datasets:         {len(project.attached_datasets)}")
        print(f"{'='*60}")
        
        return project
        
    except LabellerrError as e:
        print(f"\n❌ Error creating project: {str(e)}")
        raise
    except Exception as e:
        print(f"\n❌ Unexpected error: {str(e)}")
        raise

# Run the function
if __name__ == "__main__":
    project = create_annotation_project()

Troubleshooting

Dataset has no files error

Error: Dataset {dataset_id} has no filesCause: The dataset was created but files haven’t finished uploading or processing.Solution:

Always call dataset.status() before creating a project to ensure dataset is ready
This method polls until dataset processing is complete

dataset = create_dataset_from_local(...)
dataset.status()  # Wait for completion
project = create_project(...)  # Now safe to create

Verify folder path is correct and contains files
Check dataset.files_count to confirm files were uploaded

Data type mismatch

Error: Data type conflicts between project, dataset, and templateSolution:

Ensure all components use the same data type:

# All must match
dataset_config = DatasetConfig(data_type="image")
template_params = CreateTemplateParams(data_type=DatasetDataType.image)
project_params = CreateProjectParams(data_type=DatasetDataType.image)

Template creation fails

Cause: Missing required fields in questions (color for objects, options for classifications)Solution:

Object types require color field
Classification types require options list
Use required=True for mandatory questions

Authentication errors

Solution:

Verify API key and secret are correct
Check key hasn’t expired
Ensure you have permissions to create projects

Create Dataset

Learn how to create and manage datasets

Annotation Questions

Detailed guide on crafting annotation questions

Upload Pre-annotations

Import existing annotations into your project

Export Annotations

Export completed annotations in various formats

Video Operations

Special operations for video projects

Getting Started

SDK installation and initialization guide

Need Help?

For technical support or questions about project creation, contact us at [email protected]

SDK Documentation

​Overview

Project Creation Requirements

Supported Data Types

​Quick Start

​Step-by-Step Guide

​Step 1: Prepare Your Dataset

​Step 2: Create Annotation Template

​Step 3: Configure & Create Project

​Common Scenarios

​Using Multiple Datasets

​Multiple Annotation Rotations

​Video Project with Keyframes

​Schema Reference

​CreateProjectParams

Project Configuration Parameters

​RotationConfig

Rotation Configuration

​QuestionType Enum

Available Question Types

​Complete End-to-End Example

​Troubleshooting

​Related Documentation

Create Dataset

Annotation Questions

Upload Pre-annotations

Export Annotations

Video Operations

Getting Started

​Need Help?

Overview

Quick Start

Step-by-Step Guide

Step 1: Prepare Your Dataset

Step 2: Create Annotation Template

Step 3: Configure & Create Project

Common Scenarios

Using Multiple Datasets

Multiple Annotation Rotations

Video Project with Keyframes

Schema Reference

CreateProjectParams

RotationConfig

QuestionType Enum

Complete End-to-End Example

Troubleshooting

Related Documentation

Need Help?