Labellerr

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Get Started With Labellerr

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Documentation-Labellerr

Labellerr is a simple and powerful platform for labeling data for AI and machine learning. It helps you annotate images, videos, text, PDFs, and audio quickly and accurately. You can use AI-assisted tools, automated workflows, and a smart feedback loop to get high-quality training data fast.

Labellerr Documentation | Labellerr Docs 

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<p class="slate-p " >Labellerr is a simple and powerful platform for labeling data for AI and machine learning. It helps you annotate images, videos, text, PDFs, and audio 
quickly and accurately. You can use AI-assisted tools, automated workflows, and a smart feedback loop to get high-quality training data fast.</p><p class="slate-p " >
This section shows you fast, easy steps to begin using Labellerr. You&apos;ll learn how to sign up, create a project, and use the dashboard 
to manage your labeling work with confidence.</p><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><a href="https://docs.labellerr.com/labellerr/documentation-labellerr/getting-started/get-started-with-labellerr/how-to-get-started-with-labellerr" target="_top" class=card-anchor><div class="card-box without-icon" style="width:60%;"><div ><h3 class="slate-h3 " >How to Get Started with Labellerr?</h3><p class="slate-p " >Learn how to sign up, create a workspace, set up your first domain, and begin your first project in a few simple steps.</p></div></div></a><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><a href="https://docs.labellerr.com/labellerr/documentation-labellerr/getting-started/get-started-with-labellerr/how-to-create-a-new-project" target="_top" class=card-anchor><div class="card-box without-icon" style="width:60%;"><div ><h3 class="slate-h3 " >How to Create a New project?</h3><p class="slate-p " >Covers selecting data type (image, text, audio, video), uploading datasets, and setting up labeling templates and attributes.</p></div></div></a><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><a href="https://docs.labellerr.com/labellerr/documentation-labellerr/getting-started/get-started-with-labellerr/how-to-navigate-dashboard" target="_top" class=card-anchor><div class="card-box without-icon" style="width:60%;"><div ><h3 class="slate-h3 " >How to Navigate Dashboard?</h3><p class="slate-p " >Explains the dashboard layout, status bar, progress indicators, and built‑in analytics to track your labeling work.</p></div></div></a><p class="slate-p " ></p>

Labellerr is a simple and powerful platform for labeling data for AI and machine learning. It helps you annotate images, videos, text, PDFs, and audio 
quickly and accurately. You can use AI-assisted tools, automated workflows, and a smart feedback loop to get high-quality training data fast.


This section shows you fast, easy steps to begin using Labellerr. You'll learn how to sign up, create a project, and use the dashboard 
to manage your labeling work with confidence.

<p class="slate-p " >These quick guides walk you through the core areas of Labellerr—workspace setup, managing datasets, understanding key terms, and using keyboard shortcuts to work faster.</p><p class="slate-p " ></p><div class=card-group-box style="width:100%; min-width:540px;"><a href="https://app.theneo.io/labellerr/documentation-labellerr/frequently-accessed-tutorials/frequently-accessed-tutorials-1/workspace" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Workspace</h3><p class="slate-p " >Manage your workspace settings, invite team members, and configure access roles to organize and control your projects efficiently.</p></div></a><a href="https://app.theneo.io/labellerr/documentation-labellerr/frequently-accessed-tutorials/frequently-accessed-tutorials-1/datasets-1" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Datasets</h3><p class="slate-p " >Learn to create, upload, and manage datasets. Explore sources like local files, cloud storage, and built‑in sample data for quick setup.</p></div></a></div><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><div class=card-group-box style="width:100%; min-width:540px;"><a href="https://app.theneo.io/labellerr/documentation-labellerr/frequently-accessed-tutorials/frequently-accessed-tutorials-1/key-definitions-of-data-annotation" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Key Definitions in Data Annotation</h3><p class="slate-p " >Understand essential terms, objects, classifications, attributes—needed to structure and label your data correctly and consistently.</p></div></a><a href="https://app.theneo.io/labellerr/documentation-labellerr/frequently-accessed-tutorials/frequently-accessed-tutorials-1/labellerr-shortcut-keys-guide" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Labellerr Shortcut Keys Guide</h3><p class="slate-p " >Boost your speed with keyboard shortcuts for annotation tasks—zoom, select, label tools, review toggles, and navigation across files.</p></div></a></div><p class="slate-p " ></p><p class="slate-p " ></p><p class="slate-p " ></p><p class="slate-p " ></p>

These quick guides walk you through the core areas of Labellerr—workspace setup, managing datasets, understanding key terms, and using keyboard shortcuts to work faster.

<div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><h3 class="slate-h3 " >Explore Labellerr Feature Demos
 </h3><p class="slate-p " >See Labellerr’s powerful annotation tools in action, from AI-powered copy &amp; paste to advanced segmentation and grouping feature.</p><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><div class=card-group-box style="width:100%; min-width:540px;"><a href="https://app.theneo.io/labellerr/documentation-labellerr/feature-demos/feature-demos/copy-from-similar-images" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Copy from similar images</h3><p class="slate-p " >Use AI to copy labels from visually similar images and speed up annotation by up to 70 %.</p></div></a><a href="https://app.theneo.io/labellerr/documentation-labellerr/feature-demos/feature-demos/labellerr-grouping-tool-feature" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Labellerr Grouping Tool</h3><p class="slate-p " >Quickly group objects or masks in similar categories to organize complex scenes and boost labeling efficiency.</p></div></a></div><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><div class=card-group-box style="width:100%; min-width:540px;"><a href="https://app.theneo.io/labellerr/documentation-labellerr/feature-demos/feature-demos/image-captioning-using-generative-ai" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Image Captioning Using Generative AI</h3><p class="slate-p " >Generate descriptive captions for images with AI to save time and enrich your annotations with detail.</p></div></a><a href="https://app.theneo.io/labellerr/documentation-labellerr/feature-demos/feature-demos/text-captioning-using-generative-ai" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Text Captioning Using Generative AI</h3><p class="slate-p " >Automatically create text captions or summaries with AI to enhance label context in text and PDF projects.</p></div></a></div><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><div class=card-group-box style="width:100%; min-width:540px;"><a href="https://app.theneo.io/labellerr/documentation-labellerr/feature-demos/feature-demos/segment-anything" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Segment Anything (SAM) Integration</h3><p class="slate-p " >Add description hereUse Meta’s SAM model for instant, pixel-perfect image segmentation right within Labellerr.</p></div></a><a href="https://app.theneo.io/labellerr/documentation-labellerr/feature-demos/feature-demos/auto-label-jobs-by-active-learning" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Auto‑Label Jobs by Active Learning</h3><p class="slate-p " >Leverage active learning to auto-label data, reducing manual effort and improving over time with human feedback.</p></div></a></div><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><div class=card-group-box style="width:100%; min-width:540px;"><a href="https://app.theneo.io/labellerr/documentation-labellerr/feature-demos/feature-demos/image-similarity" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Image Similarity Search</h3><p class="slate-p " >Find and reuse annotations by searching datasets for visually similar images, powered by image similarity AI.</p></div></a><a href="https://app.theneo.io/labellerr/documentation-labellerr/feature-demos/feature-demos/user-settings-guide" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >User Settings Guide</h3><p class="slate-p " >Customize your annotation environment—hotkeys, preferences, notifications—to fit your workflow and speed.</p></div></a></div><p class="slate-p " ></p>

See Labellerr’s powerful annotation tools in action, from AI-powered copy & paste to advanced segmentation and grouping feature.

<h3 class="slate-h3 " >Jump-Start Your Labellerr Workflow</h3><p class="slate-p " >Explore quick-action guides to begin labeling, perform reviews (internal &amp; client), adjust file views, export data, and customize settings.</p><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><div class=card-group-box style="width:100%; min-width:540px;"><a href="https://app.theneo.io/labellerr/documentation-labellerr/actions/actions/start-labelling" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Start Labelling</h3><p class="slate-p " >Click Start Labeling on your dashboard to open tasks and begin annotating files.</p></div></a><a href="https://app.theneo.io/labellerr/documentation-labellerr/actions/actions/review" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Review</h3><p class="slate-p " >Use the Review mode to check and approve annotations by your team or yourself.</p></div></a></div><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><div class=card-group-box style="width:100%; min-width:540px;"><a href="https://app.theneo.io/labellerr/documentation-labellerr/actions/actions/client-review" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Client Review</h3><p class="slate-p " >Open the Client Review section to let clients view, comment, and approve labeled files.</p></div></a><a href="https://app.theneo.io/labellerr/documentation-labellerr/actions/actions/client-review-using-grid-view" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Client Review using Grid View</h3><p class="slate-p " >Switch to Grid View to let clients review many images in a tiled layout—ideal for batch feedback.</p></div></a></div><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><div class=card-group-box style="width:100%; min-width:540px;"><a href="https://app.theneo.io/labellerr/documentation-labellerr/actions/actions/view-on-files-page-1" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Views on Files Page</h3><p class="slate-p " >Tweak how you view your files list, grid, status filters to find tasks and track progress easily.</p></div></a><a href="https://app.theneo.io/labellerr/documentation-labellerr/actions/actions/how-to-create-an-export-in-labellerr" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >How to create an Export on Labellerr</h3><p class="slate-p " >Export data by choosing filters, formats, and setting up scheduled or one-off exports. Only admins can create exports.</p></div></a></div><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><p class="slate-p " ></p>

Explore quick-action guides to begin labeling, perform reviews (internal & client), adjust file views, export data, and customize settings.

<h3 class="slate-h3 " ><div class="slate-div"></div>Explore These Essential Labellerr Features</h3><p class="slate-p " >These guides show how to improve collaboration through file-level remarks and understand the overall annotation workflow with a clear diagram.</p><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><div class=card-group-box style="width:100%; min-width:540px;"><a href="https://app.theneo.io/labellerr/documentation-labellerr/few-additional-features/few-additional-features-1/reviewing-file-level-remarks-by-annotators-reviewers" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Reviewing file‑level remarks by annotators/reviewers</h3><p class="slate-p " >Learn how annotators and reviewers leave comments on specific files, classify remarks (e.g., “Mistake” or “Suggestion”), and filter files based on feedback.</p></div></a><a href="https://app.theneo.io/labellerr/documentation-labellerr/few-additional-features/few-additional-features-1/labellerr-workflow-diagram" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Labellerr Workflow Diagram</h3><p class="slate-p " >A visual flowchart that maps out the entire labeling process—from project setup and data upload to labeling, review, and export.</p></div></a></div><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><p class="slate-p " ></p>

Explore These Essential Labellerr Features

These guides show how to improve collaboration through file-level remarks and understand the overall annotation workflow with a clear diagram.

<p class="slate-p " ></p><div class=card-group-box style="width:100%; min-width:540px;"><a href="https://docs.labellerr.com/labellerr/documentation-labellerr/sdk/sdk-documentation/getting-started-with-sdk" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Getting Started With SDK</h3><p class="slate-p " >Download and start using our sdk.</p></div></a><a href="https://docs.labellerr.com/labellerr/documentation-labellerr/sdk/sdk-documentation/how-to-add-annotation-questions-how-to-create-project-using-sdk" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Creating a New Project</h3><p class="slate-p " >Learn how to create project using our SDK</p></div></a></div><p class="slate-p " ></p><div class=card-group-box style="width:100%; min-width:540px;"><a href="https://docs.labellerr.com/labellerr/documentation-labellerr/sdk/sdk-documentation/how-to-add-annotation-questions" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >How to add annotation questions?</h3><p class="slate-p " >Learn how to add annotation question when creating a project.</p></div></a><a href="https://docs.labellerr.com/labellerr/documentation-labellerr/sdk/sdk-documentation/how-to-upload-preannotation-using-sdk" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Uploading Pre-annotations</h3><p class="slate-p " >Learn how to upload Pre-annotation.</p></div></a></div><p class="slate-p " ></p><div class=card-group-box style="width:100%; min-width:540px;"><a href="https://docs.labellerr.com/labellerr/documentation-labellerr/sdk/sdk-documentation/how-to-create-an-export-using-sdk" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Local Export</h3><p class="slate-p " >Learn how to export, know its status and fetch the download url using sdk.</p></div></a><a href="https://docs.labellerr.com/labellerr/documentation-labellerr/sdk/sdk-documentation/retrieving-projects-and-datasets-using-sdk" target="_top" class="card-box-anchor without-icon"><div ><h3 class="slate-h3 " >Retrieving Datasets and Projects Using SDK</h3><p class="slate-p " >Learn retrieving datasets and projects using SDK for a particular user.</p></div></a></div><p class="slate-p " ></p>

<h2 class="slate-h2 " >Introduction</h2><p class="slate-p " >The <strong class="slate-bold">Labellerr SDK</strong> is a Python library designed to make interaction with the Labellerr platform simple and efficient. With this SDK, you can manage data annotations, projects, and exports seamlessly in your applications.</p><p class="slate-p " >This documentation will guide you through installing the SDK, understanding its core functionalities, and handling common errors.</p><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><h2 class="slate-h2 " >Installation</h2><p class="slate-p " >To install the Labellerr SDK, use the following command:</p><div class="code-block-wrapper"  style="width:100%;min-width:100%" ><div class="code-block-header">Bash</div><pre class="slate-CodeBlockElement slate-code_block"><textarea id="publishedPageCodeblock" style="display:none !important; visibility: hidden; opacity: 0; width: 1px; height: 1px;" type="hidden"> </textarea><code class="language-bash keep-markup drop-tokens"><div class="slate-code_line"><div class="slate-code_line">pip install https://github.com/tensormatics/SDKPython/releases/download/prod/labellerr_sdk-1.0.0.tar.gz</div></div></code></pre></div><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><h2 class="slate-h2 " >Getting Started</h2><h4 class="slate-h4 " >Obtaining <code class="slate-code">api_keys</code> and <code class="slate-code">client_id</code>:</h4><p class="slate-p " ><strong class="slate-bold">To obtain your api_keys and client_id, Pro and Enterprise plan users can contact Labellerr support. If you are on a free plan, you can request them by emailing </strong><code class="slate-code"><strong class="slate-bold">support@tensormatics.com</strong></code><strong class="slate-bold">.</strong></p><p class="slate-p " >Once installed, you can start by importing and initializing the <code class="slate-code">LabellerrClient</code> and <code class="slate-code">LabellerrError</code>. This client will handle all communication with the Labellerr platform.</p><h3 class="slate-h3 " >Example Client initiation:</h3><div class="code-block-wrapper"  style="width:100%;min-width:100%" ><div class="code-block-header">Python</div><pre class="slate-CodeBlockElement slate-code_block"><textarea id="publishedPageCodeblock" style="display:none !important; visibility: hidden; opacity: 0; width: 1px; height: 1px;" type="hidden"> </textarea><code class="language-python keep-markup drop-tokens"><div class="slate-code_line"><div class="slate-code_line">from labellerr.client import LabellerrClient
from labellerr.exceptions import LabellerrError


# Initialize the client with your API credentials
api_key = &quot;your-api-key&quot;
api_secret = &quot;your-api-secret&quot;
client_id = &quot;your-client-id&quot;
project_id = &apos;your-project-id&apos;
# choose scope from SCOPE_LIST=[&apos;project&apos;,&apos;client&apos;,&apos;public&apos;]
scope = &apos;client&apos;
email = &quot;your-email-id&quot;


client = LabellerrClient(api_key, api_secret)</div></div></code></pre></div><p class="slate-p " >Replace <code class="slate-code">&apos;your_api_key&apos;, &apos;your-email-id&apos;, &apos;client_id&apos;, &apos;project_id&apos;</code> and <code class="slate-code">&apos;your_api_secret&apos;</code> with your actual credentials provided by Labellerr.</p><p class="slate-p " ></p>

 is a Python library designed to make interaction with the Labellerr platform simple and efficient. With this SDK, you can manage data annotations, projects, and exports seamlessly in your applications.

This documentation will guide you through installing the SDK, understanding its core functionalities, and handling common errors.

To install the Labellerr SDK, use the following command:

To obtain your api_keys and client_id, Pro and Enterprise plan users can contact Labellerr support. If you are on a free plan, you can request them by emailing 

Once installed, you can start by importing and initializing the 

. This client will handle all communication with the Labellerr platform.

'your_api_key', 'your-email-id', 'client_id', 'project_id'

 with your actual credentials provided by Labellerr.

Getting Started with SDK

<h3 class="slate-h3 " >Creating a New Project</h3><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><p class="slate-p " >A <strong class="slate-bold">project</strong> in Labellerr organizes your datasets and their annotations. Use the following method to create a project:</p><h4 class="slate-h4 " >Limitations:</h4><ul class="slate-ul " ><li class="slate-li " ><div style="position:relative">Maximum of <strong class="slate-bold">2,500 files</strong> per folder.</div></li><li class="slate-li " ><div style="position:relative">Total folder size should not exceed <strong class="slate-bold">2.5 GB</strong>.</div></li></ul><h4 class="slate-h4 " >Acceptable value:</h4><ul class="slate-ul " ><li class="slate-li " ><div style="position:relative"><strong class="slate-bold">option_type</strong></div><ul class="slate-ul " ><li class="slate-li " ><div style="position:relative"><code class="slate-code">&apos;input&apos;</code>, <code class="slate-code">&apos;radio&apos;</code>, <code class="slate-code">&apos;boolean&apos;</code>, <code class="slate-code">&apos;select&apos;</code>, <code class="slate-code">&apos;dropdown&apos;</code>, <code class="slate-code">&apos;stt&apos;</code>, <code class="slate-code">&apos;imc&apos;</code>, <code class="slate-code">&apos;BoundingBox&apos;</code>, <code class="slate-code">&apos;polygon&apos;</code>, <code class="slate-code">&apos;dot&apos;</code>, <code class="slate-code">&apos;audio&apos;</code></div></li></ul></li></ul><h4 class="slate-h4 " >Data Types and Supported Formats</h4><p class="slate-p " >The Labellerr SDK supports various data types and file formats for your annotation projects:</p><h4 class="slate-h4 " ><strong class="slate-bold">Supported Data Types and Extensions</strong></h4><div class="table-container" style="width:100%;min-width:100%"><table class="slate-table" style="min-width:100%"><div class="published-table-header" style="width:100%;min-width:100%;min-height:initial;visibility: hidden;border:initial"><div style="width:100px;">Title</div><div style="width:100px;">Description</div><div style="width:100px;">Title</div></div><colgroup ><col style="width:initial"><col style="width:initial"></colgroup><tbody class="slate-TableElement-tbody"><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Data Type</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Description</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Supported Extensions</p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " ><code class="slate-code">image</code></p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Image files for visual annotation</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " ><code class="slate-code">.jpg</code>, <code class="slate-code">.jpeg</code>, <code class="slate-code">.png</code>, <code class="slate-code">.bmp</code>, <code class="slate-code">.tiff</code></p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " ><code class="slate-code">video</code></p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Video content for temporal annotation</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " ><code class="slate-code">.mp4</code></p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " ><code class="slate-code">audio</code></p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Audio files for sound annotation</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " ><code class="slate-code">.mp3</code>, <code class="slate-code">.wav</code></p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " ><code class="slate-code">document</code></p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Document files for text analysis</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " ><code class="slate-code">.pdf</code></p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " ><code class="slate-code">text</code></p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Plain text files for text annotation</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " ><code class="slate-code">.txt</code></p></td></div></tr></tbody></table></div><p class="slate-p " >When using the SDK methods, specify the data type as one of: <code class="slate-code">&apos;image&apos;</code>, <code class="slate-code">&apos;video&apos;</code>, <code class="slate-code">&apos;audio&apos;</code>, <code class="slate-code">&apos;document&apos;</code>, or <code class="slate-code">&apos;text&apos;</code>.</p><p class="slate-p " >When using the <code class="slate-code">initiate_create_project()</code> function, you <strong class="slate-bold">must provide either</strong>:</p><ul class="slate-ul " ><li class="slate-li " ><div style="position:relative"><code class="slate-code">annotation_guide</code> (as a dictionary), <strong class="slate-bold">or</strong> </div></li><li class="slate-li " ><div style="position:relative"><code class="slate-code">annotation_template_id</code> (as a string) </div></li></ul><div class="slate-div"><p class="slate-p " ><strong class="slate-bold">Important:</strong> At least one of these two fields is required. If both are missing, the project creation will fail.</p></div><h4 class="slate-h4 " >Example Usage:</h4><div class="code-block-wrapper"  style="width:100%;min-width:100%" ><div class="code-block-header">Python</div><pre class="slate-CodeBlockElement slate-code_block"><textarea id="publishedPageCodeblock" style="display:none !important; visibility: hidden; opacity: 0; width: 1px; height: 1px;" type="hidden"> </textarea><code class="language-python keep-markup drop-tokens"><div class="slate-code_line"><div class="slate-code_line">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 = {
    &apos;client_id&apos;: client_id,
    &apos;dataset_name&apos;: &apos;New_dataset&apos;,
    &apos;dataset_description&apos;: &apos;A sample dataset for image classification&apos;,
    &apos;data_type&apos;: &apos;image&apos;,
    &apos;created_by&apos;: email,
    &apos;project_name&apos;: &apos;Image Classification Project&apos;,
    &apos;annotation_template_id&apos;:&quot;existing annotaion_template_id&quot;,</div><div class="slate-code_line">    &apos;rotation_config&apos;: {
        &apos;annotation_rotation_count&apos;: 1,
        &apos;review_rotation_count&apos;: 1,
        &apos;client_review_rotation_count&apos;: 1
    },
    &apos;autolabel&apos;: False,
    &apos;folder_to_upload&apos;: &apos;path/to/your/dataset&apos;
}
try:
    result = client.initiate_create_project(project_payload)
    print(f&quot;Project created successfully. Project ID: {result[&apos;project_id&apos;][&apos;response&apos;][&apos;project_id&apos;]}&quot;)
except LabellerrError as e:
    print(f&quot;Project creation failed: {str(e)}&quot;)</div></div></code></pre></div><p class="slate-p " >You can also add <strong class="slate-bold">classification questions and </strong><a href="https://docs.labellerr.com/labellerr/documentation-labellerr/sdk/sdk-documentation/how-to-add-annotation-questions" target="_self"><strong class="slate-bold">annotation questions</strong></a><strong class="slate-bold"> </strong>while creating the project.</p><div class="code-block-wrapper"  style="width:100%;min-width:100%" ><div class="code-block-header">Python</div><pre class="slate-CodeBlockElement slate-code_block"><textarea id="publishedPageCodeblock" style="display:none !important; visibility: hidden; opacity: 0; width: 1px; height: 1px;" type="hidden"> </textarea><code class="language-python keep-markup drop-tokens"><div class="slate-code_line"><div class="slate-code_line">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 = {
    &apos;client_id&apos;: client_id,
    &apos;dataset_name&apos;: &apos;New_dataset&apos;,
    &apos;dataset_description&apos;: &apos;A sample dataset for image classification&apos;,
    &apos;data_type&apos;: &apos;image&apos;,
    &apos;created_by&apos;: email,
    &apos;project_name&apos;: &apos;Image Classification Project&apos;,
    &apos;annotation_guide&apos;: [
        {
            &quot;question_number&quot;: 1,
            &quot;question&quot;: &quot;What is the main object in the image?&quot;,
            &quot;question_id&quot;: &quot;533bb0c8-fb2b-4394-a8e1-5042a944802f&quot;,
            &quot;option_type&quot;: &quot;dropdown&quot;,
            &quot;required&quot;: True,
            &quot;options&quot;: [
                {&quot;option_name&quot;: &quot;Car&quot;},
                {&quot;option_name&quot;: &quot;Building&quot;},
                {&quot;option_name&quot;: &quot;Person&quot;}
            ]
        }
    ],
    &apos;rotation_config&apos;: {
        &apos;annotation_rotation_count&apos;: 1,
        &apos;review_rotation_count&apos;: 1,
        &apos;client_review_rotation_count&apos;: 1
    },
    &apos;autolabel&apos;: False,
    &apos;folder_to_upload&apos;: &apos;path/to/your/dataset&apos;
}
try:
    result = client.initiate_create_project(project_payload)
    print(f&quot;Project created successfully. Project ID: {result[&apos;project_id&apos;][&apos;response&apos;][&apos;project_id&apos;]}&quot;)
except LabellerrError as e:
  
    print(f&quot;Project creation failed: {str(e)}&quot;)

Upl</div></div></code></pre></div><p class="slate-p " ></p><h1 class="slate-h1 " >Glossary</h1><div class="table-container" style="width:100%;min-width:100%"><table class="slate-table" style="min-width:100%"><div class="published-table-header" style="width:100%;min-width:100%;min-height:initial;visibility: hidden;border:initial"><div style="width:100px;">Title</div><div style="width:100px;">Description</div></div><colgroup ><col style="width:initial"><col style="width:initial"><col style="width:initial"><col style="width:initial"></colgroup><tbody class="slate-TableElement-tbody"><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " ><strong class="slate-bold">Variable/Key</strong></p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " ><strong class="slate-bold">Type</strong></p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " ><strong class="slate-bold">Description</strong></p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " ><strong class="slate-bold">Example Value</strong></p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >client_id</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >String</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Unique identifier for the client or organization creating the project</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >client_123</p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >dataset_name</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >String</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Name of the dataset being created</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >New_dataset</p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >dataset_description</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >String</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Description explaining the dataset&apos;s purpose and contents</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >A sample dataset for image classification</p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >data_type</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >String</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Type of data in the dataset</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >image</p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >created_by</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >String</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Email address of the project creator</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >user@example.com</p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >project_name</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >String</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Display name for the annotation project</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Image Classification Project</p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >annotation_template_id</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >String</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >ID of existing annotation template defining label structure</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >template_456</p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >autolabel</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Boolean</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Enable/disable automatic AI pre-labeling</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >false</p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >folder_to_upload</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >String</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Local file path to dataset folder</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >path/to/your/dataset</p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >annotation_rotation_count</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Integer</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Number of times data item need to be annotated</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >1</p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >review_rotation_count</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Integer</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Number of times annotated item need to be reviewed</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >1</p></td></div></tr><tr class="slate-tr " ><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >client_review_rotation_count</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Integer</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >Number of times client reviewers need to be done.</p></td></div><div class="slate-TableCellElement-content"><td class="slate-td"><p class="slate-p " >1</p></td></div></tr></tbody></table></div><p class="slate-p " ></p>

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

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

Title

Description

When using the SDK methods, specify the data type as one of: 

How to Create Project Using SDK

This guide will help you understand how to write your own annotation questions in JSON format, whether they are for drawing things on images (like bounding boxes or dots) or for selecting/entering answers (like dropdowns or booleans). 

Different datasets support different types of annotations: 

two main types of annotation question that you can create 

A. Object-Based (e.g., draw bounding boxes, polygons, dots, polylines) 

Used when you want users to draw or mark things directly on an image. 

B. Classification (e.g., dropdowns, booleans, radio buttons) 

Used when users answer a question based on the image without drawing. 

Used for selecting or entering answers. Valid annotation types: 

❗ annotation(options) must be one of: dropdown, select, or boolean 

🔍 Required Fields for Classification Questions Fields 

Put all your questions in a list []. You can mix object and classification questions.

✅ Example File with Multiple Questions 

How to add annotation questions?

<h2 class="slate-h2 " >Uploading Pre-annotations</h2><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><p class="slate-p " ><strong class="slate-bold">Pre-annotations </strong>are labels which can be pre-loaded to a project which can speed up manual annotation<strong class="slate-bold">. </strong>Size limit for uploading pre-annotations is <strong class="slate-bold">25 MB. </strong></p><h4 class="slate-h4 " >Example Usage (Synchronous):</h4><div class="code-block-wrapper"  style="width:100%;min-width:100%" ><div class="code-block-header">Plain text</div><pre class="slate-CodeBlockElement slate-code_block"><textarea id="publishedPageCodeblock" style="display:none !important; visibility: hidden; opacity: 0; width: 1px; height: 1px;" type="hidden"> </textarea><code class="language-python keep-markup drop-tokens"><div class="slate-code_line">from labellerr.client import LabellerrClient</div><div class="slate-code_line">from labellerr.exceptions import LabellerrError</div><div class="slate-code_line"></div><div class="slate-code_line"></div><div class="slate-code_line"># Initialize the client with your API credentials</div><div class="slate-code_line">client = LabellerrClient(&apos;your_api_key&apos;, &apos;your_api_secret&apos;)</div><div class="slate-code_line"></div><div class="slate-code_line">project_id = &apos;project_123&apos;</div><div class="slate-code_line">client_id = &apos;12345&apos;</div><div class="slate-code_line">annotation_format = &apos;coco&apos;</div><div class="slate-code_line">annotation_file = &apos;/path/to/annotations.json&apos;</div><div class="slate-code_line"></div><div class="slate-code_line">try:</div><div class="slate-code_line">    # Upload and wait for processing to complete</div><div class="slate-code_line">    result = client.upload_preannotation_by_project_id(project_id, client_id, annotation_format, annotation_file)</div><div class="slate-code_line">  </div><div class="slate-code_line">    # Check the final status</div><div class="slate-code_line">    if result[&apos;response&apos;][&apos;status&apos;] == &apos;completed&apos;:</div><div class="slate-code_line">        print(&quot;Pre-annotations processed successfully&quot;)</div><div class="slate-code_line">        # Access additional metadata if needed</div><div class="slate-code_line">        metadata = result[&apos;response&apos;].get(&apos;metadata&apos;, {})</div><div class="slate-code_line">        print(&quot;metadata&quot;,metadata)</div><div class="slate-code_line">except LabellerrError as e:</div><div class="slate-code_line">    print(f&quot;Pre-annotation upload failed: {str(e)}&quot;)</div></code></pre></div><h4 class="slate-h4 " >Example Usage (Asynchronous):</h4><div class="code-block-wrapper"  style="width:100%;min-width:100%" ><div class="code-block-header">Plain text</div><pre class="slate-CodeBlockElement slate-code_block"><textarea id="publishedPageCodeblock" style="display:none !important; visibility: hidden; opacity: 0; width: 1px; height: 1px;" type="hidden"> </textarea><code class="language-python keep-markup drop-tokens"><div class="slate-code_line">from labellerr.client import LabellerrClient</div><div class="slate-code_line">from labellerr.exceptions import LabellerrError</div><div class="slate-code_line"></div><div class="slate-code_line"></div><div class="slate-code_line"># Initialize the client with your API credentials</div><div class="slate-code_line">client = LabellerrClient(&apos;your_api_key&apos;, &apos;your_api_secret&apos;)</div><div class="slate-code_line"></div><div class="slate-code_line"></div><div class="slate-code_line">project_id = &apos;project_123&apos;</div><div class="slate-code_line">client_id = &apos;12345&apos;</div><div class="slate-code_line">annotation_format = &apos;coco&apos;</div><div class="slate-code_line">annotation_file = &apos;/path/to/annotations.json&apos;</div><div class="slate-code_line"></div><div class="slate-code_line">try:</div><div class="slate-code_line">    # Start the async upload - returns immediately</div><div class="slate-code_line">    future = client.upload_preannotation_by_project_id_async(project_id, client_id, annotation_format, annotation_file)</div><div class="slate-code_line">  </div><div class="slate-code_line">    print(&quot;Upload started, you can do other work here...&quot;)</div><div class="slate-code_line">  </div><div class="slate-code_line">    # When you need the result, wait for completion</div><div class="slate-code_line">    try:</div><div class="slate-code_line">        result = future.result(timeout=300)  # 5 minutes timeout</div><div class="slate-code_line">        if result[&apos;response&apos;][&apos;status&apos;] == &apos;completed&apos;:</div><div class="slate-code_line">            print(&quot;Pre-annotations processed successfully&quot;)</div><div class="slate-code_line">            metadata = result[&apos;response&apos;].get(&apos;metadata&apos;, {})</div><div class="slate-code_line">            print(&quot;metadata&quot;,metadata)</div><div class="slate-code_line">    except TimeoutError:</div><div class="slate-code_line">        print(&quot;Processing took too long&quot;)</div><div class="slate-code_line">    except Exception as e:</div><div class="slate-code_line">        print(f&quot;Error in processing: {str(e)}&quot;)</div><div class="slate-code_line">except LabellerrError as e:</div><div class="slate-code_line">    print(f&quot;Failed to start upload: {str(e)}&quot;)</div></code></pre></div><h4 class="slate-h4 " >Choosing Between Sync and Async</h4><ol class="slate-ol " ><li class="slate-li " ><div style="position:relative"><strong class="slate-bold">Synchronous Method</strong>:</div><ol class="slate-ol " ><li class="slate-li " ><div style="position:relative">Simpler to use - just call and wait for result</div></li><li class="slate-li " ><div style="position:relative">Blocks until processing is complete</div></li><li class="slate-li " ><div style="position:relative">Good for scripts and sequential processing</div></li></ol></li><li class="slate-li " ><div style="position:relative"><strong class="slate-bold">Asynchronous Method</strong>:</div><ol class="slate-ol " ><li class="slate-li " ><div style="position:relative">Returns immediately with a Future object</div></li><li class="slate-li " ><div style="position:relative">Allows you to do other work while processing</div></li><li class="slate-li " ><div style="position:relative">Can set timeouts and handle long-running uploads</div></li><li class="slate-li " ><div style="position:relative">Better for applications that need to stay responsive</div></li></ol></li></ol><p class="slate-p " >Both methods will:</p><ol class="slate-ol " ><li class="slate-li " ><div style="position:relative">Upload your annotation file</div></li><li class="slate-li " ><div style="position:relative">Monitor the processing status</div></li><li class="slate-li " ><div style="position:relative">Return the final result with complete status information</div></li></ol><p class="slate-p " ><strong class="slate-bold">Note</strong>: The processing time depends on the size of your annotation file and the number of annotations. For the sync method, this means waiting time. For the async method, you can do other work during this time.</p><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><p class="slate-p " ></p>

are labels which can be pre-loaded to a project which can speed up manual annotation

Size limit for uploading pre-annotations is 

: The processing time depends on the size of your annotation file and the number of annotations. For the sync method, this means waiting time. For the async method, you can do other work during this time.

How to Upload Preannotation Using SDK

<h3 class="slate-h3 " >Local Export</h3><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><p class="slate-p " >The SDK provides functionality to export project data locally. This feature allows you to export annotations in various formats for further analysis or backup purposes.</p><p class="slate-p " ><strong class="slate-bold">The export created will be available in the exports section of Labellerr dashboard. Here is where you can find the </strong><a href="https://docs.labellerr.com/labellerr/documentation-labellerr/actions/actions/how-to-create-an-export-in-labellerr" target="_self"><strong class="slate-bold">export in our tool</strong></a><strong class="slate-bold">.</strong></p><h4 class="slate-h4 " >Acceptable values:</h4><ul class="slate-ul " ><li class="slate-li " ><div style="position:relative">statuses:</div><ul class="slate-ul " ><li class="slate-li " ><div style="position:relative"><code class="slate-code">&apos;review&apos;</code>, <code class="slate-code">&apos;r_assigned&apos;</code>,<code class="slate-code">&apos;client_review&apos;</code>, <code class="slate-code">&apos;cr_assigned&apos;</code>,<code class="slate-code">&apos;accepted&apos;</code></div></li></ul></li><li class="slate-li " ><div style="position:relative">export_format:</div><ul class="slate-ul " ><li class="slate-li " ><div style="position:relative"><code class="slate-code">&apos;json&apos;</code>, <code class="slate-code">&apos;coco_json&apos;</code>, <code class="slate-code">&apos;csv&apos;</code>, <code class="slate-code">&apos;png&apos;</code></div></li></ul></li></ul><h4 class="slate-h4 " >Example Usage:</h4><div class="code-block-wrapper"  style="width:100%;min-width:100%" ><div class="code-block-header">Plain text</div><pre class="slate-CodeBlockElement slate-code_block"><textarea id="publishedPageCodeblock" style="display:none !important; visibility: hidden; opacity: 0; width: 1px; height: 1px;" type="hidden"> </textarea><code class="language-python keep-markup drop-tokens"><div class="slate-code_line">from labellerr.client import LabellerrClient
from labellerr.exceptions import LabellerrError
# Initialize the client with your API credentials
client = LabellerrClient(api_key, api_secret)
project_id = project_id
client_id = client_id
export_config = {
    &quot;export_name&quot;: &quot;Weekly Export&quot;,
    &quot;export_description&quot;: &quot;Export of all accepted annotations&quot;,
    &quot;export_format&quot;: &quot;coco_json&quot;,
    &quot;statuses&quot;: [&apos;review&apos;, &apos;r_assigned&apos;,&apos;client_review&apos;, &apos;cr_assigned&apos;,&apos;accepted&apos;]
}
try:
    result = client.create_local_export(project_id, client_id, export_config)</div><div class="slate-code_line">    export_id = result[&quot;response&quot;][&apos;report_id&apos;]
    print(f&quot;Local export created successfully. Export ID: {export_id}&quot;)
except LabellerrError as e:
    print(f&quot;Local export creation failed: {str(e)}&quot;)</div></code></pre></div><p class="slate-p " ><strong class="slate-bold">Note</strong>: The export process creates a downloadable file link of your project&apos;s annotations based on the specified status filters. This is useful for backup purposes or when you need to process the annotations offline.</p><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><h3 class="slate-h3 " >Check Export Status</h3><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><p class="slate-p " >This method allows users to check the export status of a previously triggered export task. The status will indicate whether the export is still processing, completed, or failed. Moreover, if the export is successfull then the function also sends the downloadable file link url with it expire time and status .</p><h4 class="slate-h4 " >Example Usage :</h4><div class="code-block-wrapper"  style="width:100%;min-width:100%" ><div class="code-block-header">Python</div><pre class="slate-CodeBlockElement slate-code_block"><textarea id="publishedPageCodeblock" style="display:none !important; visibility: hidden; opacity: 0; width: 1px; height: 1px;" type="hidden"> </textarea><code class="language-python keep-markup drop-tokens"><div class="slate-code_line"><div class="slate-code_line">from labellerr.client import LabellerrClient
from labellerr.exceptions import LabellerrError
# Initialize the client with your API credentials
client = LabellerrClient(api_key, api_secret)

# Example input
project_id = project_id
client_id = client_id
report_ids = report_ids

status = client.check_export_status(
    api_key=&quot;your_api_key&quot;,
    api_secret=&quot;your_api_secret&quot;,
    project_id=project_id,
    report_ids=report_ids, # report_id is your export_id
    client_id=client_id
)

print(status)</div></div></code></pre></div><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><h3 class="slate-h3 " >Fetch Export Download URL</h3><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><p class="slate-p " >This endpoint is used to fetch the <strong class="slate-bold">downloadable link</strong> for a previously created export using the export&apos;s UUID and report ID. The response includes a signed URL from which the export file can be downloaded with the time left to expire.</p><h4 class="slate-h4 " >Example Usage :</h4><div class="code-block-wrapper"  style="width:100%;min-width:100%" ><div class="code-block-header">Python</div><pre class="slate-CodeBlockElement slate-code_block"><textarea id="publishedPageCodeblock" style="display:none !important; visibility: hidden; opacity: 0; width: 1px; height: 1px;" type="hidden"> </textarea><code class="language-python keep-markup drop-tokens"><div class="slate-code_line"><div class="slate-code_line">from labellerr.client import LabellerrClient
from labellerr.exceptions import LabellerrError</div><div class="slate-code_line">import uuid</div><div class="slate-code_line">
# Initialize the client with your API credentials
client = LabellerrClient(api_key, api_secret)</div><div class="slate-code_line"></div><div class="slate-code_line">project_id = project_id
client_id = client_id
report_ids = report_ids   # make sure it is a list</div><div class="slate-code_line">uuid = str(uuid.uuid4())</div><div class="slate-code_line"></div><div class="slate-code_line">status = client.fetch_download_url(</div><div class="slate-code_line">    api_key=&quot;your_api_key&quot;,
    api_secret=&quot;your_api_secret&quot;,
    project_id = project_id,</div><div class="slate-code_line">    uuid = uuid,</div><div class="slate-code_line">    export_id = report_ids[0],</div><div class="slate-code_line">    client_id = client_id</div><div class="slate-code_line">)</div><div class="slate-code_line"></div><div class="slate-code_line">print(status)</div></div></code></pre></div><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><p class="slate-p " ></p>

The SDK provides functionality to export project data locally. This feature allows you to export annotations in various formats for further analysis or backup purposes.

The export created will be available in the exports section of Labellerr dashboard. Here is where you can find the 

: The export process creates a downloadable file link of your project's annotations based on the specified status filters. This is useful for backup purposes or when you need to process the annotations offline.

This method allows users to check the export status of a previously triggered export task. The status will indicate whether the export is still processing, completed, or failed. Moreover, if the export is successfull then the function also sends the downloadable file link url with it expire time and status .

 for a previously created export using the export's UUID and report ID. The response includes a signed URL from which the export file can be downloaded with the time left to expire.

How to Create an Export Using SDK

<h3 class="slate-h3 " >Retrieving All Projects for a Client</h3><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><p class="slate-p " >You can retrieve all projects associated with a specific client ID using the following method:</p><h4 class="slate-h4 " >Example Usage:</h4><div class="code-block-wrapper"  style="width:100%;min-width:100%" ><div class="code-block-header">Plain text</div><pre class="slate-CodeBlockElement slate-code_block"><textarea id="publishedPageCodeblock" style="display:none !important; visibility: hidden; opacity: 0; width: 1px; height: 1px;" type="hidden"> </textarea><code class="language-python keep-markup drop-tokens"><div class="slate-code_line">from labellerr.client import LabellerrClient
from labellerr.exceptions import LabellerrError
# Initialize the client with your API credentials
client = LabellerrClient(api_key, api_secret)
client_id = client_id
try:
    result = client.get_all_project_per_client_id(client_id)
  
    # Check if projects were retrieved successfully
    if result and &apos;response&apos; in result:
        projects = result[&apos;response&apos;]
        print(f&quot;Found {len(projects)} projects:&quot;)
        for project in projects:
            print(f&quot;- Project ID: {project.get(&apos;project_id&apos;)}&quot;)
            print(f&quot;  Name: {project.get(&apos;project_name&apos;)}&quot;)
            print(f&quot;  Type: {project.get(&apos;data_type&apos;)}&quot;)
except LabellerrError as e:
    print(f&quot;Failed to retrieve projects: {str(e)}&quot;)</div></code></pre></div><p class="slate-p " >This method is useful when you need to:</p><ul class="slate-ul " ><li class="slate-li " ><div style="position:relative">List all projects for a client</div></li><li class="slate-li " ><div style="position:relative">Find specific project IDs</div></li><li class="slate-li " ><div style="position:relative">Check project statuses</div></li><li class="slate-li " ><div style="position:relative">Get an overview of client&apos;s work</div></li></ul><p class="slate-p " >The response includes detailed information about each project, including its ID, name, data type, and other relevant metadata.</p><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><h3 class="slate-h3 " >Retrieving All Datasets</h3><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><p class="slate-p " >You can retrieve both linked and unlinked datasets associated with a client using the following method:</p><h4 class="slate-h4 " >Example Usage:</h4><div class="code-block-wrapper"  style="width:100%;min-width:100%" ><div class="code-block-header">Plain text</div><pre class="slate-CodeBlockElement slate-code_block"><textarea id="publishedPageCodeblock" style="display:none !important; visibility: hidden; opacity: 0; width: 1px; height: 1px;" type="hidden"> </textarea><code class="language-python keep-markup drop-tokens"><div class="slate-code_line">from labellerr.client import LabellerrClient
from labellerr.exceptions import LabellerrError
# Initialize the client with your API credentials
client = LabellerrClient(api_key, api_secret)
client_id = client_id
data_type = &apos;image&apos;

try:
    result = client.get_all_dataset(client_id, data_type, project_id, scope)
    # Process linked datasets
    linked_datasets = result[&quot;response&quot;][&quot;datasets&quot;]
    # print(linked_datasets[&quot;datasets&quot;][0][&quot;dataset_id&quot;])
    print(f&quot;Found {len(linked_datasets)} linked datasets:&quot;)
    for dataset in linked_datasets:
        # print(dataset[&quot;datasets&quot;])
        print(f&quot;- Dataset ID: {dataset.get(&apos;dataset_id&apos;)}&quot;)
        print(f&quot;  Name: {dataset.get(&apos;name&apos;)}&quot;)
        print(f&quot;  Description: {dataset.get(&apos;description&apos;)}&quot;)

except LabellerrError as e:
    print(f&quot;Failed to retrieve datasets: {str(e)}&quot;)</div></code></pre></div><p class="slate-p " >This method is useful when you need to:</p><ul class="slate-ul " ><li class="slate-li " ><div style="position:relative">Get an overview of all available datasets</div></li><li class="slate-li " ><div style="position:relative">Find datasets that are linked to projects</div></li><li class="slate-li " ><div style="position:relative">Identify unlinked datasets that can be associated with new projects</div></li><li class="slate-li " ><div style="position:relative">Manage dataset organization and project associations</div></li></ul><p class="slate-p " >The response includes two lists:</p><ul class="slate-ul " ><li class="slate-li " ><div style="position:relative"><code class="slate-code">linked</code>: Datasets that are already associated with projects</div></li><li class="slate-li " ><div style="position:relative"><code class="slate-code">unlinked</code>: Datasets that are not yet associated with any project</div></li></ul><p class="slate-p " >Each dataset object contains detailed information including its ID, name, description, and other metadata.</p><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><h2 class="slate-h2 " >Error Handling</h2><div class="slate-hr"><hr contenteditable="false" class="slate-HrElement-hr"/></div><p class="slate-p " >The Labellerr SDK uses a custom exception class, <code class="slate-code">LabellerrError</code>, to indicate issues during API interactions. Always wrap your function calls in <code class="slate-code">try-except</code> blocks to gracefully handle errors.</p><h4 class="slate-h4 " >Example:</h4><div class="code-block-wrapper"  style="width:100%;min-width:100%" ><div class="code-block-header">Plain text</div><pre class="slate-CodeBlockElement slate-code_block"><textarea id="publishedPageCodeblock" style="display:none !important; visibility: hidden; opacity: 0; width: 1px; height: 1px;" type="hidden"> </textarea><code class="language-python keep-markup drop-tokens"><div class="slate-code_line">from labellerr.exceptions import LabellerrError</div><div class="slate-code_line"></div><div class="slate-code_line">try:</div><div class="slate-code_line">    # Example function call</div><div class="slate-code_line">    result = client.initiate_create_project(payload)</div><div class="slate-code_line">except LabellerrError as e:</div><div class="slate-code_line">    print(f&quot;An error occurred: {str(e)}&quot;)</div></code></pre></div><p class="slate-p " ></p>

You can retrieve all projects associated with a specific client ID using the following method:

The response includes detailed information about each project, including its ID, name, data type, and other relevant metadata.

You can retrieve both linked and unlinked datasets associated with a client using the following method:

Each dataset object contains detailed information including its ID, name, description, and other metadata.

The Labellerr SDK uses a custom exception class, 

, to indicate issues during API interactions. Always wrap your function calls in 

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