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:
You can create a new project by navigating to the 'Get Started With Labellerr' section under 'Getting Started'. Select the appropriate project type (image, video, or audio) based on your data needs.
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 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 Labeller?' in the documentation for step-by-step instructions on setting up access and importing data from your S3 bucket.
Look under 'How To Create an Export in Labellerr' within the Actions section of the product documentation.
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.
This feature allows you to copy annotations from one image to another if they are visually similar. It's useful for quickly labeling multiple images with common objects or structures without redundant manual drawing.
File level remarks allow annotators to add comments directly on images, audio files, or text documents. These annotations provide context or feedback about the item being labeled without altering it.
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.
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.
You can use the SDK feature 'Upload Preannotation' or follow the manual instructions detailed in the 'Importing users from one project to another in the same workspace' guide if you're moving between projects, but note that this specifically is about user imports.
The Grouping Tool allows you to organize similar images or files efficiently. You can copy annotations from one image to another, saving time during annotation tasks by leveraging existing work on similar media items.
Use the SAM mode in the Magic Editor section of Labeller. This feature enables interactive and efficient image segmentation using advanced AI tools like SAM 2, which can be accessed via 'Magic Editor' documentation.
'Search by remarks' allows users to quickly find specific labeled data based on comments or annotations made during the labeling process. This improves efficiency by reducing redundant work and making it easier for all stakeholders (client, annotators, reviewers) to locate relevant examples faster.
Labellerr improves labeling efficiency by reducing iterations needed for quality approval from 5 down to just one and by enabling collaboration through better tools (search by remarks) that minimize doubt clearing sessions. This allows teams to complete projects faster with less overhead.
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.
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.
Auto Label Jobs by Active Learning is a feature that helps you label data automatically. It's described in the guide under 'Auto Label Jobs by Active Learning'.
Image recognition refers to software and tools that use machine learning and computer vision techniques to identify objects, patterns, text, or actions within digital images.
Automated labeling leverages machine learning models (like SAM) that analyze images once and then apply the learned patterns across multiple similar images, drastically reducing manual work for large datasets compared to traditional pixel-by-pixel annotation methods.
Labellerr's data labeling solution focuses on three key aspects: Throughput, Efficiency, and Quality. These components help in improving speed, resource efficiency, and accuracy respectively.
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.
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.
Our Video Annotation Platform provides advanced tools specifically designed for dynamic content. Features include timeline-based labeling, object tracking across frames, and specialized interfaces for temporal data handling. These capabilities enable efficient labeling of complex video datasets used in security, autonomous driving, sports analysis, and surveillance applications.
Yes, Labellerr provides a Video Annotation Platform designed specifically for dynamic video labeling. It helps enhance security systems by enabling precise labeling tasks needed to train models for applications like facial recognition or activity monitoring.
Labellerr offers an intuitive interface, training resources through its 'Learn' section including tutorials and documentation, making it easier for users even without deep technical knowledge to navigate and use effectively.
We provide comprehensive customer support including a Product Demo to show you the platform firsthand, Interactive Demo options, detailed Documentation resources, Pricing information transparently displayed, and dedicated contact channels through our global offices (San Francisco & India).