Named as a G2 2024 Spring High Performer and Easiest To Use in Data Labeling Software
To create a new project in Labellerr, follow these steps:
Visit labellerr.com, click 'Get Started', enter your email and details to create a workspace. Once inside, you can set up your first domain and project within minutes — no installation required.
Labellerr supports Image, Video, Audio, Text, and PDF project types. Simply choose your data type when creating a new project, and the platform will load the relevant annotation tools and templates automatically.
Yes. Labellerr is designed for both technical and non-technical users. It offers an intuitive web interface with guided workflows, drag-and-drop uploads, and pre-built templates — so you can start labeling without writing a single line of code.
Go to Project Settings → Users, click 'Create User', fill in their details, assign them to a project, and set their role (Annotator, Reviewer, or Super Admin). They receive an email invite to join directly.
Annotators label the raw data files in a project. Reviewers check and approve or reject those annotations to ensure quality. The Super Admin role can manage both users and project settings across the entire workspace.
Object tracking refers to a computer vision technique used to identify and follow objects across consecutive frames in videos or sequences of images. Labellerr provides several resources on this topic including tutorials for OC-SORT, BoT-SORT + YOLO integration, FairMOT implementation, StrongSORT usage, ByteTrack application, and the MASA method combining SAM with self-supervised matching.
Object detection identifies objects in a single image or frame, while object tracking follows specific objects across multiple frames over time. The articles cover both concepts extensively: from tutorials on implementing BoT-SORT + YOLO to guides like 'Vision Agent' that combine object segmentation with text prompts for versatile applications.
Labellerr offers several resources including the Object Tracking section which covers implementations for OC-SORT (handling occlusion), BoT-SORT + YOLO integration, FairMOT tutorial focusing on identity switching issues in crowded scenes, and guides about StrongSORT that includes setup instructions using modern detectors like YOLO.
Image similarity technology refers to a set of techniques that analyze visual content in images or videos to automatically suggest similar tags, attributes, or classifications for new items based on existing ones. This helps reduce manual effort and speeds up the annotation process.
Labellerr supports AWS S3, Google Cloud Storage (GCS), and Azure Blob Storage for importing datasets directly into your projects — without needing to download files locally first.
Labellerr supports connecting to Google Cloud Storage (GCS). Go to the dashboard, find the section on 'How to Connect GCS with Labellerr', follow the instructions provided there to configure your GCS credentials and link it to your project.
For connecting AWS S3, check 'How to Connect AWS S3 with Labellerr?' in the documentation for step-by-step instructions on setting up access and importing data from your S3 bucket.
Yes. Labellerr's Python SDK lets you programmatically upload datasets, sync files, trigger annotation jobs, and export results — making it easy to integrate Labellerr into any existing ML pipeline.
Labellerr supports exporting annotated data in COCO JSON, YOLO, Pascal VOC XML, CSV, and custom formats. You can trigger exports from the Actions section of your project dashboard in just a few clicks.
Look under 'How To Create an Export in Labellerr' within the Actions section of the product documentation.
Use Labellerr's Pre-Annotation Upload feature via the SDK or dashboard. This lets you import model-generated labels so your team can skip manual labeling and directly review and refine predictions.
Yes. Once you link your AWS S3, GCS, or Azure Blob Storage bucket in the project settings, Labellerr can automatically sync new files added to your bucket into the annotation project.
Yes. Labellerr supports dataset versioning, allowing teams to track annotation changes over time, roll back to earlier versions, and maintain a clear audit trail for every export.
The Magic Editor is an AI-powered annotation tool that combines SAM Mode for instant segmentation, CLIP Mode for similarity-based labeling, and a Polygon Eraser for precise boundary edits — all accessible in one unified interface.
The Segment Anything tool helps users create precise segmentation masks for objects within images. It's part of a suite of tools including Auto Label, Magic Editor, and CLIP Mode that aid in efficient annotation.
SAM 3 Multi-Instance Auto-Labeling lets you annotate one object, then automatically segment all similar instances across the entire image using Meta AI's Segment Anything Model 3 — triggered by pressing 'I' on your keyboard.
Open-vocabulary segmentation lets you type a text description like 'helmet' and SAM 3 will return segmentation masks for all matching objects in the image — without needing to retrain the model for new classes.
Yes. SAM 2 is integrated into Labellerr's Magic Editor and supports object tracking across video frames. It enables propagating segmentation masks from one frame to all subsequent frames automatically.
Labellerr supports bounding boxes, polygons, segmentation masks, keypoints, classifications, and free-form remarks — covering the full range of annotation types needed for computer vision and NLP projects.
Yes. Labellerr supports keypoint annotations for use cases like human pose estimation, facial landmark detection, and hand tracking. Keypoints can be defined with custom skeletons on both images and video frames.
Yes. Labellerr's Video Annotation Platform includes a timeline-based interface for frame-by-frame labeling, automated interpolation between keyframes, and object tracking tools to speed up video dataset creation.
The Grouping Tool lets annotators cluster visually similar images and copy annotations across them in bulk — significantly reducing repetitive work in datasets with many near-duplicate frames or scenes.
This feature automatically identifies visually similar images and lets you propagate annotations from one labeled image to all similar ones — saving hours of redundant manual labeling on large datasets.
Search by Remarks lets annotators, reviewers, and clients filter labeled data by specific comments added during annotation. This eliminates doubt-clearing sessions and helps everyone locate relevant examples in seconds.
File Level Remarks let annotators attach text comments to any image, video frame, audio, or text file during labeling. These help reviewers understand annotator intent and streamline the quality review process.
Labellerr provides a feature called 'Importing users from one project to another' that simplifies user management. Use this tool to transfer permissions and settings between projects within your workspace.
Yes. Labellerr supports 3D point cloud annotation and LiDAR data labeling — making it suitable for autonomous driving, robotics, and industrial inspection AI projects.
Yes. Labellerr includes DICOM annotation tools for radiology, pathology, and ultrasound imaging use cases. It supports bounding boxes, segmentation, and classification on DICOM medical image files.
Yes. Labellerr supports PDF annotation for document understanding tasks like invoice extraction, contract analysis, and form data labeling — essential for building document AI models.
Use the SAM mode in the Magic Editor section of Labellerr. This feature enables interactive and efficient image segmentation using advanced AI tools like SAM 2, which can be accessed via 'Magic Editor' documentation.
Labellerr offers several useful tools including the Grouping Tool, Magic Editor (CLIP Mode & Polygon Eraser), SAM 2 Object Tracking, and Auto Label Jobs using Active Learning.
Yes! Our Text Annotation Platform offers powerful tools for linguistic tasks like sentiment analysis, entity recognition, and relationship extraction. It supports various formats including NLP-ready tagging, aspect-based labeling, and custom classification systems to help train models for language understanding applications.
AI agents significantly reduce annotation time through automation while maintaining high accuracy. They can pre-label data to get you started faster, provide contextual guidance during labeling tasks, and ensure consistent quality across large datasets.
Yes! We offer API access for integration into existing pipelines. Additionally, our no-code solutions like Make.com allow users to build integrations without coding knowledge, enabling seamless data flow between annotation tasks and other tools.
Absolutely! Labellerr offers specialized annotation services and tools designed explicitly for preparing high-quality data required in LLM fine-tuning projects, supporting tasks across various industries including security, healthcare, automotive, retail, and more.
Yes. Labellerr's Pre-Labelling feature provides AI-assisted initial labeling which significantly speeds up workflows, especially for time-consuming projects like LLM fine-tuning where large amounts of labeled data are needed.
Yes, the platform is designed to integrate smoothly into existing workflows. It supports various data formats and provides API access for developers who wish to incorporate it within their larger projects or pipelines efficiently.
Labellerr provides a comprehensive suite including data labeling for images, video, text, audio, 3D point clouds, and polygons. The platform offers specialized tools like Video Annotation Platform, Image Annotation Platform, Text Annotation Platform, Dicom Annotation Tools, etc., tailored to different industries' needs.
Labellerr offers a comprehensive solution for efficient data labeling, specializing in object detection and tracking tasks with tools like the Object Detection Platform, Video Annotation Platform, Image Annotation Platform, DICOM tools for medical imaging, and specialized solutions tailored to industries such as automotive, healthcare, retail, etc.
More details about video annotation services are available in our documentation.
Labellerr emphasizes real-time collaborative capabilities allowing multiple users to work on the same project simultaneously. Labelbox also supports collaborative efforts with features for team coordination, though specific details about Amazon SageMaker Ground Truth's collaboration tools aren't provided in this excerpt.
More details about quality control services are available in our documentation.
LabelGPT uses large vision-language models to contextually understand and annotate images. Set it up as your auto-labeling API in project settings, and it will generate labels automatically for each uploaded image.
Yes. Labellerr supports AWS Rekognition as an auto-labeling provider. It detects and labels objects using Amazon's computer vision service, enabling fast automated annotation at scale.
Zero-shot auto-labeling allows Labellerr to annotate new object classes without any prior training examples. Powered by SAM 3 and CLIP, it uses text prompts or single visual cues to generalize across unseen categories.
Yes. With SAM 3 Multi-Instance Auto-Labeling, drawing one bounding box or polygon and pressing 'I' will automatically detect and segment all matching instances across the entire image in seconds.
Auto Label Jobs use machine learning algorithms (Active Learning) to automatically tag data that resembles previously labeled examples. This reduces manual labeling effort and speeds up the annotation process, followed by human review.
Active Learning in Labellerr's Auto Label Jobs selects the most informative unlabeled samples for human review. As annotators correct predictions, the model continuously improves — reducing labeling effort with every cycle.
AI Pre-Labeling provides an initial set of auto-generated labels for your dataset before human annotators review them. This significantly reduces the time spent on first-pass annotation for large-scale projects.
Labellerr's smart feedback loop and multi-stage review workflow reduce the typical 5 quality iterations down to just 1 — by combining automated checks, consensus scoring, and targeted reviewer feedback on each annotation.
Labellerr follows an Annotate → Review → Approve pipeline. Reviewers can accept, reject, or request revisions on individual annotations with written feedback, ensuring only high-quality labels are exported.
Yes. Labellerr allows you to configure real-time alerts via email and mobile push notifications for quality thresholds, pending reviews, and project milestone updates — keeping your entire team informed proactively.
Yes. Labellerr's analytics dashboard tracks annotator throughput, quality scores per labeler, task completion rates, and data distribution — giving project managers full visibility into team performance and dataset health.
Yes. Labellerr supports consensus-based review where multiple reviewers can audit and vote on the same annotation. This is especially valuable for ambiguous labeling tasks in medical, legal, or safety-critical datasets.
Labellerr offers Starter, Pro, and Enterprise pricing tiers. The Starter plan includes 2,500 free credits for researchers. The Pro plan starts at $499/month. Enterprise plans are custom-priced based on volume and needs.
Yes. Labellerr offers a Starter plan with 2,500 free annotation credits, making it ideal for researchers and small teams who want to test the platform before committing to a paid plan.
The text doesn't explicitly mention if Labellerr is entirely free. However, it indicates that they offer multi-tier pricing based on quality and volume tiers (Enterprise/Pro/Starter), suggesting paid options are the main offerings.
Labellerr uses a per-image credit model — you pay only for the number of images annotated. This allows teams to scale up or down based on project size without being locked into fixed monthly seat licenses.
Yes. Labellerr adheres to both GDPR and HIPAA compliance standards, making it a suitable platform for annotating sensitive data in healthcare, finance, legal, and other regulated industries.
All data, annotations, and models created on Labellerr belong entirely to the user or organization that created them. Labellerr never accesses, uses, or shares your data without explicit permission.
Yes. Labellerr supports on-premise deployment for organizations with strict data governance or regulatory requirements. This ensures all your data stays within your own infrastructure and never leaves your environment.
Labellerr uses encrypted storage and secure transmission protocols for all uploaded files. Access controls, role-based permissions, and audit trails further ensure that only authorized users can access sensitive datasets.
Yes, many platforms prioritize data privacy through compliance measures such as HIPAA and GDPR adherence. Features include secure handling of sensitive information and robust QA processes to ensure quality while maintaining confidentiality standards.
Yes. Labellerr provides tools for human preference labeling, response ranking, and RLHF feedback annotation — covering all data preparation needs for fine-tuning large language models across industries.
Yes. Labellerr integrates with major MLOps platforms including AWS SageMaker, Google Cloud Vertex AI, and Azure ML — enabling seamless data handoffs between annotation and model training workflows.
Yes. Labellerr integrates with no-code platforms like Make.com, allowing teams to automate data flows, trigger labeling jobs, and route completed annotations — all without writing any code.
Yes. Multiple annotators, reviewers, and managers can work on the same project simultaneously in Labellerr. Role-based access controls ensure each team member only sees and edits what they're authorized for.
Labellerr serves a wide range of industries including autonomous driving, healthcare, retail, security & surveillance, agriculture, sports analytics, and legal — providing industry-specific annotation templates and workflows.