Guide to the most popular image annotation tools that you need to know about in 2024. Compare the features and pricing, and choose the best image annotation tool for your use case. It’s 2024—annotating images is still one of the most time-consuming steps in bringing a computer vision project to market. To help you out, we put together a list of the most popular image labeling tools out there. Whether you are: A computer vision team building unmanned drones with your own in-house annotation tool. A team of data scientists working on an autonomous driving project looking for large-scale labeling services. Or a data operations team working in healthcare looking for the right platform for your radiologists to accurately label CT scans. This guide will help you compare the top AI annotation tools and find the right one for you. We will compare each based on key factors - including image annotation service, support for different data types and use cases, QA/QC capabilities, security and data privacy, integration with the machine learning pipeline, and customer support. But first, let's explore the process of selecting an image annotation tool from the available providers. Choosing the right image annotation tool is a critical decision that can significantly impact the quality and efficiency of the annotation process. To make an informed choice, it's essential to consider several factors and evaluate the suitability of an image annotation tool for specific needs. Evaluating Image Annotation Tools for Computer Vision Projects Selecting the perfect image annotation tool is like choosing the perfect brush for your painting. Different projects require specific annotation needs that dictate how downstream components. When evaluating an annotation tool that fits your project specifications, there are a few key factors you have to consider. In this section, we will explore those key factors and practical considerations to help you navigate the selection process and find the most fitting AI annotation tool for your computer vision applications. Annotation Types: An effective labeling tool should support various annotation types, such as bounding boxes (ideal for object localization), polygons (useful for detailed object outlines), keypoints (for pose estimation), and semantic segmentation (for scene understanding). The tool must be adaptable to different annotation requirements, allowing users to annotate images with precision and specificity based on the task at hand. User Interface (UI) and User Experience (UX): The user interface plays a crucial role in the efficiency and accuracy of the annotation process. A good annotation tool should have an intuitive interface that is easy to navigate, reducing the learning curve for users. Clear instructions, user-friendly controls, and efficient workflows contribute to a smoother annotation experience. Scalability: Consider the tool's ability to scale with the growing volume of data. A tool that efficiently handles large datasets and multiple annotators is crucial for projects with evolving requirements. Automation and AI Integration: Look for image labeling tools that offer automation features, such as automatic annotation tools or features, to accelerate the annotation process. Integration with artificial intelligence (AI) algorithms can further enhance efficiency by automating repetitive tasks, reducing manual effort, and improving annotation accuracy. Collaboration and Workflow Management: Assess the data annotation tool's collaboration features, including version control, user roles, and workflow management. Collaboration tools are essential for teams working on complex annotation projects. Data Security and Privacy: Ensure that the tool adheres to data security and privacy standards like GDPR. Evaluate encryption methods, access controls, and policies regarding the handling of sensitive data. Pricing: Consider various pricing models, such as per-user, per-project, or subscription models. Also factor in scalability costs, and potential additional fees, ensuring transparency in the pricing structure. Once you've identified which factors are most important for you to evaluate image annotating tools, the next step is understanding how to assess their suitability for your specific use case.  Most Popular Image Annotation Tools Let's compare the features offered by the best image annotation companies such as Encord, Scale AI, Label Studio, SuperAnnotate, CVAT, and Amazon SageMaker Ground Truth, and understand how they assist in annotating images. This article discusses the top 17 image annotation tools in 2024 to help you choose the right image annotation software for your use case. Encord Scale CVAT Label Studio Labelbox Playment Appen Dataloop SuperAnnotate V7 Labs Hive COCO Annotator Make Sense VGG Image Annotator LabelMe Amazon SageMaker Ground Truth VOTT Encord Encord is an automated annotation platform for AI-assisted image annotation, video annotation, and dataset management.  Key Features Data Management: Compile your raw data into curated datasets, organize datasets into folders, and send datasets for labeling.  AI-assisted Labeling: Automate 97% of your annotations with 99% accuracy using auto-annotation features powered by Meta's Segment Anything Model or GPT-4’s LLaVA. Collaboration: Integrate human-in-the-loop seamlessly with customized Workflows - create workflows with the no-code drag and drop builder to fit your data ops & ML pipelines. Quality Assurance: Robust annotator management & QA workflows to track annotator performance and increase label quality.  Integrated Data Labeling Services for all Industries: outsource your labeling tasks to an expert workforce of vetted, trained and specialized annotators to help you scale. Video Labeling Tool: provides the same support for video annotation. One of the leading video annotation tools with positive customer reviews, providing automated video annotations without frame rate errors. Robust Security Functionality: label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance. Integrations: Advanced Python SDK and API access (+ easy export into JSON and COCO formats). Best for Commercial teams: Teams translating from an in-house solution or open-source tool that require a scalable annotation workflow with a robust, secure, and collaborative enterprise-grade platform. Complex or unique use case: For teams that require advanced annotation tool and functionality. It includes, complex nested ontologies or rendering native DICOM formats. Pricing Simple per-user pricing – no need to track annotation hours, label consumption or data usage.    Curious? Try it out Scale Scale AI, now Scale, is a data and labeling services platform that supports computer vision use cases but specializes in RLHF, user experience optimization, large language models, and synthetic data. Scale AI's Image Annotation Tool Key Features Customizable Workflows: Offers customizable labeling workflows tailored to specific project requirements and use cases. Data labeling services: Provides high-quality data labeling services for various data types, including images, text, audio, and video. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Best for Teams Looking for a Labeling Tool: Scale is a very popular option for data labeling services. Teams Looking for Annotation Tools for Autonomous Vehicle Vision: Scale is one of the earliest platforms on the market to support 3D Sensor Fusion annotation for RADAR and LiDAR use cases. Teams Looking for Medical Imaging Annotation Tools: Platforms like Scale will usually not support DICOM or NIfTI data types nor allow companies to work with their data annotators on the platform. Pricing On a per-image basis CVAT (Computer Vision Annotation Tool) CVAT is an open source image annotation tool that is a web-based annotation toolkit, built by Intel. For image labeling, CVAT supports four types of annotations: points, polygons, bounding boxes, and polylines, as well as a subset of computer vision tasks: image segmentation, object detection, and image classification. In 2022, CVAT’s data, content, and GitHub repository were migrated over to OpenCV, where CVAT continues to be open-source. Furthermore, CVAT can also be utilized to annotate QR codes within images, facilitating the integration of QR code recognition into computer vision pipelines and applications. CVAT Label Editor Key Features Open-source: Easy and free to get started labeling images. Manual Annotation Tools: Supports a wide range of annotation types including bounding boxes, polygons, polylines, points, and cuboids, catering to diverse annotation needs. Multi-platform Compatibility: Works on various operating systems such as Windows, Linux, and macOS, providing flexibility for users. Export Formats: CVAT offers support for various data formats including JSON, COCO, and XML-based like Pascal VOC, ensuring annotation compatibility with diverse tools and platforms. Best for Students, researchers, and academics testing the waters with image annotation (perhaps with a few images or a small dataset). Not preferable for commercial teams as it lacks scalability, collaborative features, and robust security. Pricing Free 💡 More insights on image labeling with CVAT: For a team looking for free image annotation tools, CVAT is one of the most popular open-source tools in the space, with over 1 million downloads since 2021. Other popular free image annotation alternatives to CVAT are 3D Slicer, Labelimg, VoTT (Visual Object Tagging Tool - developed by Microsoft), VIA (VGG Image Annotator), LabelMe, and Label Studio. If data security is a requirement for your annotation project… Commercial labeling tools will most likely be a better fit — key security features like audit trails, encryption, SSO, and generally-required vendor certifications (like SOC2, HIPAA, FDA, and GDPR) are usually not available in open-source tools. Further reading: Overview of open source annotation tools for computer vision Complete guide to image annotation for computer vision    Label Studio Label Studio is another popular open source data labeling platform. It provides a versatile platform for annotating various data types, including images, text, audio, and video. Label Studio supports collaborative labeling, custom labeling interfaces, and integration with machine learning pipelines for data annotation tasks. Label Studio Image Annotation Tool Key Features Customizable Labeling Interfaces: Flexible configuration for tailored annotation interfaces to specific tasks. Collaboration Tools: Real-time annotation and project sharing capabilities for seamless collaboration among annotators. Extensible: Easily connect to cloud object storage and label data there directly Export Formats: Label Studio supports multiple data formats including JSON, CSV, TSV, and VOC XML like Pascal VOC, facilitating integration and annotation from diverse sources for machine learning tasks. Best for Data scientists, machine learning engineers, and researchers or teams requiring versatile data labeling for images.  Not suitable for teams with limited technical expertise or resources for managing an open source tool Price Free with enterprise plan available Labelbox Labelbox is a US-based data annotation platform founded in 2017. Like most of the other platforms mentioned in this guide, Labelbox offers both an image labeling platform, as well as labeling services. Labelbox Image Editor Key Features Data Management: QA workflows and data annotator performance tracking. Customizable Labeling Interface: 3rd party labeling services through Labelbox Boost. Automation: Integration with AI models for automatic data labeling to accelerate the annotation process. Annotation Type: Support for multiple data types beyond images, especially text. Best for Teams looking for a platform to quickly annotate documents and text. Teams carrying out annotation projects that are use-case specific. As generalist tools, platforms like Labelbox are great at handling a broad variety of data types. If you’re working on a unique use-case-specific annotation project (like scans in DICOM formats or high-resolution images that require pixel-perfect annotations), other commercial AI labeling tools will be a better fit: check out our blog exploring Best DICOM Labeling Tools. Pricing Varies based on the volume of data, percent of the total volume needing to be labeled, number of seats, number of projects, and percent of data used in model training. For larger commercial teams, this pricing may get expensive as your project scales. Playment Playment is a fully-managed data annotation platform. The workforce labeling company was acquired by Telus in 2021 and provides computer vision teams with training data for various use cases, supported by manual labelers and a machine learning platform. Playment Image Annotation Tool Key Features Data Labeling Services: Provides high-quality data labeling services for various data types including images, videos, text, and sensor data. Support: Global workforces of contractors and data labelers. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Audio Labeling Tool: Speech recognition training platform (handles all data types across 500+ languages and dialects). Best for Teams looking for a fully managed solution who do not need visibility into the process. Pricing Enterprise plan Appen Appen is a data labeling services platform founded in 1996, making it one of the first and oldest solutions in the market. The company offers data labeling services for a wide range of industries and in 2019, acquired Figure Eight to build out its software capabilities and help businesses also train and improve their computer vision models. Appen Image Annotation Tool Key Features Data Labeling Services: Support for multiple annotation types (bounding boxes, polygons, and image segmentation). Data Collection: Data sourcing (pre-labeled datasets), data preparation, and real-world model evaluation. Natural Language Processing:  Supports natural language processing tasks such as sentiment analysis, entity recognition, and text classification. Image and Video Analysis: Analyzes images and videos for tasks such as object detection, image classification, and video segmentation. Best for Teams looking for image data sourcing and collection alongside annotation services. Pricing Enterprise plan Dataloop Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data management and annotation projects. The tool offers data labeling capabilities across images, text, audio, and video annotation, helping businesses train and improve their machine learning models. Dataloop Image Annotation Tool Key Features Data Annotation: Features for image annotation tasks, including classification, detection, and semantic segmentation. Video Annotation Tool: Support for video annotations. Collaboration Tool: Features for real-time collaboration among annotators, project sharing, and version control for efficient teamwork. Data Management: Offers data management capabilities including data versioning, tracking, and organization for streamlined workflows. Best for Teams looking for a generalist annotation tool for various data annotation needs. Teams carrying out specific image and video annotation projects that are use-case specific. As generalist tools, platforms like Dataloop are built to support a wide variety of simple use cases, so other commercial platforms are a better fit if you’re trying to label use-case-specific annotation projects (like high-resolution images that require pixel-perfect annotations in satellite imaging or DICOM files for medical teams). Pricing Free trial and an enterprise plan. SuperAnnotate SuperAnnotate provides enterprise solutions for image and video annotation, catering primarily to the needs of the computer vision community. It provides powerful annotation tools and features tailored for machine learning and AI applications, offering efficient labeling solutions to enhance model training and accuracy. SuperAnnotate - Image Annotation Tool Key Features Multi-Data Type Support: Versatile annotation tool for image, video, text, and audio. AI Assistance: Integrates AI-assisted annotation to accelerate the annotation process and improve efficiency. Customization: Provides customizable annotation interfaces and workflows to tailor annotation tasks according to specific project requirements. Integration: Seamlessly integrates with machine learning pipelines and workflows for efficient model training and deployment. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Export Formats: SuperAnnotate supports multiple data formats, including popular ones like JSON, COCO, and Pascal VOC. Best for Larger teams working on various machine learning solutions looking for a versatile annotation tool. Pricing Free for early stage startups and academics for team size up to 3. Enterprise plan V7 Labs V7 is a UK-based data annotation platform founded in 2018. The company enables teams to annotate training data, support the human-in-the-loop processes, and also connect with annotation services. V7 offers annotation of a wide range of data types alongside image annotation tooling, including documents and videos. V7 Labs Image Annotation Tool Key Features Collaboration Capabilities: Project management and automation workflow functionality, with real-time collaboration and tagging. Data Labeling Services: Provides labeling services for images and videos. AI Assistance: Model-assisted annotation of multiple annotation types (segmentation, detection, and more). Best for Students or teams looking for a generalist platform to easily annotate different data types in one place (like documents, images, and short videos). Limited functionalities for use-case specific annotations. Pricing Various options, including academic, business, and pro. Hive Hive was founded in 2013 and provides cloud-based AI solutions for companies wanting to label content across a wide range of data types, including images, video, audio, text, and more. Hive Image Annotation Tool Key Features Image Annotation Tool: Offers annotation tools and workflows for labeling images along with support for unique image annotation use cases (ad targeting, semi-automated logo detection). Ease of Access: Flexible access to model predictions with a single API call. Integration: Seamlessly integrates with machine learning pipelines and workflows for AI model training and deployment. Best for Teams labeling images and other data types for the purpose of content moderation. Pricing Enterprise plan COCO Annotator COCO Annotator is a web-based image annotation tool, crafted by Justin Brooks under the MIT license. Specifically designed to streamline the process of labeling images for object detection, localization, and keypoints detection models, this tool offers a range of features that cater to the diverse needs of machine learning practitioners and researchers.  COCO Annotator - Image Annotation Tool Key Features Image Annotation: Supports annotation of images for object detection, instance segmentation, keypoint detection, and captioning tasks. Export Formats: To facilitate large-scale object detection, the tool exports and stores annotations in the COCO format.  Automations: The tool makes annotating an image easier by incorporating semi-trained models. Additionally, it provides access to advanced selection tools, including the MaskRCNN, Magic Wand and DEXTR. Best For ML Research Teams: COCO Annotator is a good choice for ML researchers, preferable for image annotation for tasks like object detection and keypoints detection. Price Free Make Sense Make Sense AI is a user-friendly and open-source annotation tool, available under the GPLv3 license. Accessible through a web browser without the need for advanced installations, this tool simplifies the annotation process for various image types. Make Sense - Image Annotation Tool Key Features Open Sourced: Make Sense AI stands out as an open-source tool, freely available under the GPLv3 license, fostering collaboration and community engagement for its ongoing development. Accessibility: It ensures web-based accessibility, operating seamlessly in a web browser without complex installations, promoting ease of use across various devices. Export Formats: It facilitates exporting annotations in multiple formats (YOLO, VOC XML like Pascal VOC, VGG JSON, and CSV), ensuring compatibility with diverse machine learning algorithms and seamless integration into various workflows. Best For Small teams seeking an efficient solution to annotate an image. Price Free VGG Image Annotator VGG Image Annotator (VIA) is a versatile open-source tool crafted by the Visual Geometry Group (VGG) for the manual annotation of both image and video data. Released under the permissive BSD-2 clause license, VIA serves the needs of both academic and commercial users, offering a lightweight and accessible solution for annotation tasks. VGG Image Annotator - Image Annotation Tool Key Features Lightweight and User-Friendly: VIA is a lightweight, self-contained annotation tool, utilizing HTML, Javascript, and CSS without external libraries, enabling offline usage in modern web browsers without setup or installation. Offline Capability: The tool is designed to be used offline, providing a full application experience within a single HTML file of size less than 200 KB.  Multi-User Collaboration: Facilitates collaboration among multiple annotators with features such as project sharing, real-time annotation, and version control. Best For VGG Image Annotator (VIA) is ideal for individuals and small teams involved in projects for academic researchers. Price Free LabelMe LabelMe is an open-source web-based tool developed by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) that allows users to label and annotate images for computer vision research. It provides a user-friendly interface for drawing bounding boxes, polygons, and semantic segmentation masks to label objects within images. LabelMe Image Annotation Tool Key Features Web-Based: Accessible through a web-based interface, allowing for annotation tasks to be performed in any modern web browser without requiring software installation. Customizable Interface: Provides a customizable annotation interface with options to adjust settings, colors, and layout preferences to suit specific project requirements. Best for Academic and research purposes Pricing Free Amazon SageMaker Ground Truth Amazon SageMaker Ground Truth is a fully managed data labeling service provided by Amazon Web Services (AWS). It offers a platform for efficiently labeling large datasets to train machine learning models. Ground Truth supports various annotation tasks, including image classification, object detection, semantic segmentation, and more. Amazon SageMaker Ground Truth - Image Annotation Tool Key Features Managed Service: Fully managed by AWS, eliminating the need for infrastructure setup and management. Human-in-the-Loop Labeling: Harnesses the power of human feedback across the ML lifecycle to improve the accuracy and relevancy of models. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Integration with Amazon SageMaker: Seamlessly integrates with Amazon SageMaker for model training and deployment, providing a streamlined end-to-end machine learning workflow. Best for Teams requiring large-scale data labeling. Pricing Varies based on labeling task and type of data. VOTT VOTT or Visual Object Tagging Tool is an open-source tool developed by Microsoft for annotating images and videos to create training datasets for computer vision models. VOTT provides an intuitive interface for drawing bounding boxes around objects of interest and labeling them with corresponding class names. VOTT Image Annotation Tool Key Features Versatile Annotation Tool: Supports a wide range of annotation types including bounding boxes, polygons, polylines, points, and segmentation masks for precise labeling. Video Annotation: Enables annotation of videos frame by frame, with support for object tracking and interpolation to streamline the annotation process. Multi-Platform Compatibility: Works across various operating systems such as Windows, Linux, and macOS, ensuring flexibility for users. Best for Teams requiring lightweight and customizable annotation tool for object detection. Pricing Free Image Annotation Tool: Key Takeaways There you have it! The 17 Best Image Annotation Tools for computer vision in 2024.  For further reading, you might also want to check out a few 2024 honorable mentions, both paid and free annotation tools: Supervisely - commercial data labeling platform praised for its quality control functionality and basic interpolation feature. Labelimg - Labelimg is an open source multi-modal data annotation tool now part of Label Studio. MarkUp - MarkUp image is a free web annotation tool to annotate an image or a PDF.

Building powerful artificial intelligence (AI) models requires a robust data processing workflow. Most importantly, it includes accurate data annotation to build high-quality datasets suitable for training. It’s a crucial step for supervised machine learning, where models learn complex patterns from annotated data and use the knowledge to predict new, unseen samples. However, data annotation is a challenging task. Due to ever-increasing data volume, diverse data modalities, and the scarcity of high-quality domain-specific datasets, it is difficult for organizations and practitioners to streamline their machine learning (ML) development process.  In multimodal learning, annotation involves labeling several data types, such as images, text, and audio. Multimodal data annotation tools enable practitioners to conduct auto-annotation for various objects, from 2D images, videos, Digital Imaging and Communications in Medicine (DICOM) data, and geospatial information to 3D point clouds.  This article discusses the significance of multimodal annotation, different data annotation types, techniques, and challenges, and introduces some popular multimodal annotation tools. It also explains the factors businesses must consider before investing in an annotation tool and concludes with a few key takeaways. Significance of Multimodal Annotation Tools Global data production is increasing. Numerous public and proprietary data sources are curated every day. We need tools to organize and transform this data at scale to make it suitable for downstream AI tasks. This is why data annotation tools are in high demand. In fact, the global data annotation tools market is projected to grow to USD 14 billion by 2035, compared to USD 1 billion in 2022.  Since real-world data is multimodal, practitioners use multimodal annotation tools to curate datasets. Let’s look at a few factors that make these tools so important. Efficient Model Training The primary reason to use a multimodal annotation tool is to automate annotation processes for different modalities since manual data labeling is prone to human error and time-consuming. Such errors diminish training data quality and can lead to poor downstream models with low predictive potential. High-quality Data Curation A robust data curation workflow allows labelers to create, organize, and manage data. With multimodal annotation tools, labelers can quickly label data containing multiple modalities and add relevant categorizations. Data teams can readily feed such datasets into models and build high-quality ML pipelines. Fine-tuning Foundation Models For AI to address specific challenges, general-purpose multimodal foundation models often need to be fine-tuned. This requires carefully curated data that represents the particular business problem. Multimodal annotation tools help labelers label domain-specific data to train AI models for downstream tasks. Flexibility in Model Applications Multimodal annotation enables models to understand information from diverse modalities. This allows practitioners to use AI to the fullest extent in domains such as autonomous driving, medical diagnosis, emotion recognition, etc. However, annotating datasets for such diverse domains is challenging. Automated multimodal annotation tools provide various labeling solutions, such as labeling and bounding box annotation for object detection and frame classification and complex polygon annotations for segmentation tasks. Now that we have set the premise for multimodal annotation, let’s explore the technical aspects of this topic by discussing various data annotation types in the next section. Data Annotation Types and Techniques Before moving on to the list of the top multimodal annotation tools, let’s first consider the types of annotation techniques labelers use to categorize, classify, and annotate different modalities. Image Annotation Practitioners annotate images to help machine learning models learn to recognize different objects or entities they see in an image. This process helps with several AI tasks, such as image classification, image segmentation, and facial recognition.  Common image annotation techniques are: Bounding Box Annotation A bounding box is a rectangular shape drawn around the object of interest we want a model to recognize. For instance, labelers can draw rectangles around vehicles and people to train a model for classifying objects on the road. It is useful in cases where precise segmentation is not required, e.g., human detection in surveillance footage. Bounding Box Annotation Example in Encord Annotate Semantic Segmentation It’s a more granular approach where practitioners label each pixel in an image to classify different regions. They draw a closed boundary around the object. Every pixel within the boundary is assigned a single label. For instance, in the illustration below, semantic segmentation draws boundaries for person, bicycle, and background. Example of Image Segmentation in Encord Annotate Want to learn more about semantic segmentation? Read our detailed guide on Introduction to Semantic Segmentation. 3D Cuboid Annotation Cuboids are similar to bounding boxes, but instead of two-dimensional rectangles, they wrap three-dimensional cuboids around the object of interest. 3D Cuboid Annotation Example Polygon Annotation This image annotation technique involves drawing 2D shapes or polylines around the edges of the objects of interest for more pixel-perfect precision. Small lines trace a series of x-y coordinates along the edges of objects, making this annotation fine-tuned to various shapes. Polygon and Polyline Annotation Examples in Encord Annotate Keypoint Annotation This annotation involves labeling particular anchor points or landmarks on an object at key locations. These anchor points can track changes in an object's shape, size, and position. It is helpful in tasks like human posture detection. Primitive (Keypoint or Skelenton) Annotation in Encord Annotate Text Annotation Text annotation helps practitioners extract relevant information from text snippets by adding tags or assigning labels to documents or different sections or phrases in a document. Prominent text annotation techniques include: Sentiment Annotation Sentiment annotation is used in tasks like user review classification. Text documents are labeled based on the sentiment they represent, such as positive, negative, or neutral. The labels can be more granular depending on the task requirement, e.g., “Angry,” “Disgust,” “Happy,” “Elated,” “Sad,” etc. Text Classification It categorizes documents or longer texts into sub-topics or classes. It suits various domains, like legal, finance, and healthcare, to organize and filter documents or smaller pieces of text.  Entity Annotation It concerns labeling entities within the text, such as names of people, organizations, countries, etc. Natural language processing (NLP) models use labeled entities to learn patterns in text and perform various tasks. Named Entity Recognition Example Parts-of-Speech (POS) Tagging POS tagging involves labeling grammatical aspects, such as nouns, verbs, adjectives, etc., within a sentence. Audio Annotation Like text and image, audio annotation refers to labeling audio clips, verbal speech, etc., for training models to interpret audio data. Below are a few common methods. Speech Transcription Transcription annotation involves converting the entire speech in an audio or video into text and applying different tags and notes to the text. The annotated text helps train ML models to accurately convert speeches to text format. Audio Classification Audio classification involves assigning a single label to an audio clip. For instance, for emotion classification, these labels could be sentiments like happy, sad, angry, etc.; for music classification, these labels can be genres like Jazz, Rock, etc. Sound Segment Labeling This involves labeling different segments of an audio wave according to the task at hand. For instance, these labels could differentiate between different instruments and human vocals in a song or tag noise, silence, and sound segments in a clip. Video Annotation Video annotation is similar to image annotation since videos are a sequence of static frames. It involves labeling actions, tracking objects, identifying locations, etc., across video frames. Below are a few ways to annotate videos. Object Tracking Practitioners label moving objects by tracking their positions in each frame. This is usually done by drawing bounding boxes around the object in each frame. Video Segmentation Practitioners categorize the video into short clips based on what’s in each scene or changing camera angles, marking separate boundaries for background and foreground objects across video frames. Location Video annotators identify and tag the coordinates of objects of interest in video clips for training models that can recognize particular locations. Want to know more about video annotation? Then head to our blog to read The Full Guide to Video Annotation for Computer Vision. Challenges of Multimodal Annotation Several challenges make multimodal annotation a difficult task for organizations and practitioners. Below are a few problems that teams face when labeling multimodal datasets: Data Complexity: As data variety and volume increase, it becomes difficult to segregate different data categories and identify the correct annotation technique. In particular, the emergence of new data types, such as LiDAR and geospatial data, makes identifying the appropriate labeling method challenging. Need for Specialized Skills: Expert intervention is necessary when labeling highly domain-specific data, such as medical images. The expertise required can sometimes be niche, making the process even more intricate. Absence of Universal Tools: Organizations must find different tools to perform various annotation tasks, as no single tool can meet all objectives. Correlations between Modalities: Practitioners must identify relationships between different modalities for correct labeling. For instance, labeling an image may require listening to an audio clip to understand the context. Individually annotating the two modalities can lead to low-quality training data. Compliance and Data Privacy: Strict data regulations mean annotators must be careful when analyzing sensitive data elements, such as faces, names, locations, emotions, etc. Incorrect labeling can introduce serious biases and potentially lead to unexpected data leakages. Cost: Developing an efficient labeling process is costly. It demands investment in robust storage platforms, specialized teams, and advanced tools. These challenges make it difficult for AI teams to curate multimodal datasets and build high-quality AI models. However, there are several multimodal annotation tools available on the market that can make annotation workflows more efficient and productive for AI teams. Let’s explore them in the next section. Top Multimodal Annotation Tools Let’s discuss the top annotation tools businesses can use to streamline their ML workflow. Encord Annotate Encord Annotate is a labeling platform that supports several multimodal annotation techniques for images, videos, Digital Imaging and Communications in Medicine (DICOM), Neuroimaging Informatics Technology Initiative (NIfTI), Electrocardiograms (ECGs), and geospatial data. Benefits and Key Features Supports object detection, keypoint skeleton pose, hanging protocols, instance segmentation, action recognition, frame classifications, polygons, and polyline annotation. Helps experts develop high-quality labeling workflows through human-in-the-loop integration. Allows teams to build sophisticated labeling ontologies – a structured framework for categorizing data by enabling nested classification to create granular datasets with precise ground truth labels. Boosts the annotation workflow through AI-assisted automation. You can train few-shot models and use only a small labeled dataset to annotate the rest of the samples. Provides an integrated platform for managing all training data through an easy-to-use interface. Features performance analytics that let teams assess annotation quality and optimize where necessary. Supports several file formats for different data types. Allows teams to use the Segment Anything Model (SAM) for auto segmentation to annotate domain-specific data instantly. Best For Teams that want to build large-scale computer vision models. Teams looking for expert support can use Encord, especially for labeling complex data. AI developers who work on diverse visual datasets and want a comprehensive annotation tool. Pricing Encord offers a free version for individuals and small teams. It also offers a team version for medium-sized enterprises that wish to scale their AI operations and an enterprise version for large-scale projects. Contact Encord’s sales team to purchase the Team or Enterprise version. Labelbox Labelbox is a multimodal platform that allows practitioners to annotate images, videos, geospatial data, text, audio, and HTML files. LabelBox Interface Benefits and Key Features Offers a single efficiency metric to measure label quality. Allows organizations to develop custom workflows according to data type. Provides data analytics functionality to reduce labeling costs and monitor performance. Has collaboration tools to enhance workflows across different teams. Best For Teams that want to economize on labeling costs. Teams working on visual and natural language processing (NLP) models, which require annotating images and textual data. Pricing LabelBox offers multiple tiers at different prices, including the Free, Starter, Standard, and Enterprise versions. SuperAnnotate SuperAnnotate helps teams speed up multimodal learning projects by providing comprehensive functions for annotating text, audio, images, videos, and point cloud data. SuperAnnotate Dashboard Benefits and Key Features Offers annotation tools for building training data for large language models (LLMs), such as image captions, question answers, instructions, etc. Teams can annotate audio clips to identify speech and sound. Best For Teams struggling with project management due to poor collaboration among teammates. Building efficient multimodal data curation pipelines. Price SuperAnnotate offers a Free, Pro, and Enterprise version. Users must reach out to sales to get a price quote. Computer Vision Annotation Tool (CVAT) CVAT is a multimodal labeling tool primarily for computer vision tasks in healthcare, manufacturing, retail, automotive, etc. CVAT Interface Benefits and Key Features It supports several image annotation techniques, such as 3D cuboids, object detection, semantic segmentation, etc. Features intelligent algorithms for boosting annotation efficiency. It offers integration with the cloud for data storage. Best For Teams that want cloud-based data storage solutions. Teams looking for a specialized image annotation platform. Pricing CVAT, with cloud support, comes in three variants - Free, Solo, and Team. The Solo and Team versions cost USD 33 per month. VGG Image Annotation (VIA) VIA is a web-based manual annotation tool for image, audio, and video data, requiring no initial setup or configuration. VIA Interface Benefits & Key Features Supports basic image annotation methods, including bounding boxes and polygons. Also features audio, face, and video annotation techniques. Offers a list annotation capability that allows experts to label a list of images. Best For Teams that want a low-cost annotation solution for computer vision. Pricing VIA is a free, open-source tool. Basic.ai Basic.ai offers a multimodal data annotation platform for 3D LiDAR point clouds, images, and videos. Basic.ai Data Annotation Dashboard  Benefits and Key Features Offers auto-annotation, segmentation, and tracking. Offers an AI-enabled quality control process to facilitate annotation review. Best For Teams looking to streamline data annotation workflows across industries like automotive, smart city, and robotics. Pricing Basic.ai offers free and team pricing plans. Label Studio Label Studio is an open-source multimodal data annotation tool for audio, text, images, HTML, videos, and time series data. Label Studio Dashboard Benefits and Key Features Teams can perform text classification, audio segmentation, audio transcription, emotion recognition, named entity recognition, video classification, etc. Offers labeling templates for a variety of use cases. Offers imports via different formats like JSON, CSV, TSV, RAR, ZIP archives, S3, and Google Cloud Storage. Best For Large and small teams experimenting with data labeling tools for building fine-tuned AI models. Price Label Studio offers a paid enterprise edition. It also offers a free and open-source community edition. Dataloop Dataloop, the “data engine for AI,” offers a multimodal annotation platform for video, LiDAR, and sensor fusion labeling. Dataloop Platform Interface Benefits and Key Features Offers AI-assisted tools to automate labeling workflows. Offers tools to support internal and external labeling teams. Offers quality and validation tools to streamline annotation issues. Best For Large and small teams working in retail, agriculture, drones, and the medical industries. Price The vendor does not provide pricing. Reportedly, it starts from $85/month for 150 annotation tool hours. Supervisely Supervisely is a multimodal annotation solution with AI-assisted labeling. Supervisely Dashboard Benefits and Key Features Provides precise annotation for images and video. Features annotation tools for point cloud, LiDAR, and DICOM datasets. Uses AI to assist in custom labeling. Best for Teams looking for a labeling solution for domain-specific data. Price Supervisely offers a free community version and a paid enterprise edition. KeyLabs KeyLabs is a multimodal annotation tool with an interactive user interface for labeling graphical and video data. KeyLabs Interface Benefits and Key Features Offers a user-friendly interface for selecting appropriate annotation techniques like segmentation, classification, shape interpolation, etc. Allows users to convert data to JSON. Best For Teams looking for a tool with efficient collaboration and access control features. Price KeyLabs offers paid versions only. It has Startup, Business, Pro, and Enterprise editions. Key Factors to Consider Before Selecting the Best Annotation Tool Choosing a suitable annotation software is daunting as several options exist with different features. Here are the critical factors to prioritize: Data Modality Support: Teams must choose tools that support the data modalities they want to work on with all the suitable annotation techniques. Ease of Use: Tools with an interactive UI help teams learn new features quickly and reduce the chances of error. Security and Compliance: Organizations should opt for solutions that have robust access management and privacy protocols to ensure compliance. Scalability: Teams Organizations planning to expand operations should select scalable solutions with minimal dependency on the vendor. Integration: A tool seamlessly integrating with other systems helps businesses by lowering setup time and costs. Output Format: Tools that readily convert annotated data into an appropriate format are best for teams that don’t want to spend time writing custom code for format conversion. Collaboration and Productivity Features: Businesses with large teams working on a single project should select a tool with robust project management features. Smart Annotation Techniques: Support for intelligent annotation techniques, such as active learning and overlapping labels, can help teams that want to label extensive datasets. Quality Control: Tools should allow AI experts to review annotation samples to avoid errors and maintain a high-quality dataset. Community Support: A platform with sufficient community support, helper guides, documentation, etc., is beneficial as it helps teams resolve issues quickly. Pricing: Beyond affordability, ensure the tool offers a balance of cost and functionality to give the best return on investment. Multimodal Annotation Tools: Key Takeaways Multimodal annotation refers to labeling different data modalities, such as text, video, audio, and image. The need for multimodal annotation tools will increase with rising data volumes and complexity.  Automated multimodal annotation tools boost labeling speed and improve training data quality. Annotating multimodal datasets is challenging due to high costs, hidden correlations between data modalities, and the need for specialized skills. Teams must select tools to mitigate such challenges and speed up the ML model development lifecycle. There are many annotation tools available. Teams must select the one that suits them best according to price and usability.