Best Image Annotation Tools for Computer Vision [Updated 2024]
If you're looking for an image annotation tool, you have plenty of choices. The market is saturated, making it challenging to find the best tool for your needs. To help you out, we did much of the research for you to streamline your buying process. In this article, you will find a detailed overview of the most popular data annotation tools, including: Encord, Amazon SageMaker Ground Truth, Scale Rapid, Supervisely, CVAT, Labelbox, Playment, Appen, Dataloop, SuperAnnotate, V7, Hive, Label Studio, COCO Annotator, Make Sense, VGG Image Annotator, and, LabelMe Best Image Annotation Tools The sections below give an overview of the key features and user reviews for the above mentioned tools. The summary table below compares all the tools based on supported data types, annotation types, ease of use, and automation. Encord Encord is an end-to-end data development platform with an advanced image annotation tool for complex computer vision and multimodal use cases. The platform offers state-of-the-art model-assisted labeling and customizable workflows to accelerate image annotation projects and build production-ready models. Key Features AI-assisted labeling: Automate 97% of your image annotations with 99% accuracy by leveraging SOTA automated labeling capabilities such as Meta AI’s Segment Anything Model (SAM). Full suite of tools: Encord supports a range of labeling options, such as bounding boxes, rotatable boxes, polygons, polylines, key points, and classifications to support your model requirements. Accelerate with models-in-the-loop: Bring your own model to the Encord platform or leverage one of our Agents to pre-label datasets. Scalability: Encord lets you scale AI projects by supporting extensive datasets of up to 500,000 images. Build balanced datasets: Filter and slice datasets in a consolidated visual explorer and export for labeling in one click. Encord supports deep search, filtering, and metadata analysis. Complex ontologies: Build nested relationship structures in your data schema to improve the quality of your model output. Bulk classification: Leverage natural language or similarity search to select large datasets and label en masse, queue for review to accelerate labeling operations. Build reliable quality control workflows: Build robust workflows with multi-step review stages and consensus benchmarking for quality assurance. Find and fix label errors: Automatically surface labeling errors to shift your attention to the labels impacting model performance. Collaboration: Control user roles with permissions, manage task assignments and infinitely scale your MLOps workflows. Enterprise-grade security as standard: Encord Annotate complies with the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2), and Health Insurance Portability and Accountability Act (HIPAA) standards while using advanced encryption protocols to ensure data privacy. Integrations: Encord allows you to retain total control of your data. Securely connect your native cloud storage buckets and programmatically control workflows. Advanced Python SDK and API access with easy export into JSON and COCO formats. Integrated Data Labeling Services: Outsource your labeling tasks to an expert workforce of vetted, trained, and specialized annotators. Encord G2 Review Summary Encord has a rating of 4.8/5 based on 60 reviews. Users prefer Encord’s powerful ontology feature, which lets them define rich taxonomy for all data sizes. In addition, the platform’s collaborative features and granular annotation tools help users improve annotation quality. Learn how Encord Annotate helps you create rich ontologies for an efficient labeling process. Curious? Try it out Amazon SageMaker Ground Truth Amazon SageMaker Ground Truth is a human-in-the-loop data labeling platform that offers features to label large datasets. It provides a self-serve and a managed service option to help you streamline your annotation workflow for multiple CV tasks. Amazon SageMaker Ground Truth Key Features Data Generation: The platform offers tools to fine-tune pre-trained models on a few data points to generate synthetic data samples for more diverse training. Model Evaluation: Sagemaker Ground Truth lets you evaluate foundation models based on multiple metrics such as accuracy, relevancy, toxicity, and bias through human feedback. Labeling Templates: It features over thirty labeling templates for multiple CV and NLP tasks, including image classification, object detection, text classification, and named entity recognition (NER). Interactive Dashboards: The tool offers intuitive dashboards and user-friendly interfaces to monitor labeling progress across multiple projects. G2 Review Summary Amazon SageMaker Ground Truth has a rating of 4.1/5 based on 19 reviews. Users like its ease of use and advanced annotation capabilities. However, they feel it is expensive, and tracking labeling performance is challenging. Scale Rapid Scale Rapid is a data and labeling services platform that supports computer vision use cases. It specializes in reinforcement learning with human feedback (RLHF), user experience optimization, large language models (LLMs), and synthetic data. Scale Rapid Key Features Supported Data Types: Scale lets you annotate text, images, video, audio, and point-cloud data. 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. G2 Review Summary Scale Rapid has a rating of 4.4/5 based on 11 reviews. Users say it is easy to learn and does not require complex installation procedures. However, they feel the user interface is clunky and the tool’s pricing is complex. Find out about the top tools that help you perform reinforcement learning with human feedback (RLHF). Supervisely Supervisely is an end-to-end computer vision platform that offers multiple annotation tools for labeling images and videos. It features AI-based labeling that lets users automate labeling workflow through advanced ML models. Supervisely Key Features Versatile Annotation Tool: It supports multiple annotation types, including bounding boxes, polygons, polylines, points, and segmentation masks for precise labeling. Supported Data Types: Supervisely lets you label images, videos, point cloud, and medical image data. Smart Labeling Tools: Feature a class-agnostic smart tool based on customizable neural networks for capturing any object type, depending on your use case. Collaboration: The platform lets you collaborate with team members and assign relevant user roles to track issues and labeling performance. G2 Review Summary Supervisely has a rating of 4.7/5 based on ten reviews. Users like the tool’s integration with multiple apps within the Supervisely ecosystem, giving a smooth user experience. However, the number of options can be overwhelming, and the platform has latency issues. CVAT (Computer Vision Annotation Tool) CVAT is an open-source web-based image annotation tool by Intel. In 2022, CVAT’s data, content, and GitHub repository became a part of OpenCV, where CVAT continues to be open-source. Furthermore, CVAT can also help annotate QR codes within images, facilitating the integration of QR code recognition into computer vision pipelines and applications. CVAT Key Features Manual Annotation Tools: The tool supports various annotation types, including bounding boxes, polygons, polylines, points, and cuboids, catering to diverse annotation needs. Multi-platform Compatibility: Works on multiple operating systems such as Windows, Linux, and macOS, providing flexibility for users. Export Formats: CVAT supports numerous data formats, including JSON, COCO, and Pascal VOC, ensuring annotation compatibility with diverse tools and platforms. Automated Labeling: CVAT supports multiple algorithms, including the Segment Anything Model (SAM), YOLOv3, and Deep Extreme Cut (DEXTR). G2 Review Summary CVAT has a rating of 4.5/5 based on two reviews. Users like that the tool is free to use and requires no configuration and installation process because it is web-based. However, its slow performance and backend server failure are the most significant concerns. Labelbox Labelbox is a US-based data annotation platform founded in 2017 that provides a unified framework for curating and labeling datasets with collaboration and model evaluation tools. Besides a stand-alone image labeling platform, the tool offers managed annotation services with data labeling experts. Labelbox Key Features Data Management: Labelbox offers QA workflows and data annotator performance tracking. Customizable Labeling Interface: It features a user-friendly interface, providing easy-to-navigate editors for specific needs. Automation: Allows integration with AI models for automatic data labeling to accelerate the annotation process. Annotation Capabilities: It supports annotation for multiple data types beyond images, including text, video, audio, geospatial and medical images. G2 Review Summary LabelBox has a rating of 4.7/5 based on 33 reviews. Users find the tool’s data management features helpful. However, they feel that it does not perform well with high-resolution images. Playment Playment is an Indian-based end-to-end data annotation platform founded in 2015 and now operating under Telus’ ownership. It offers managed annotation services by employing computer vision teams to annotate training data for multiple use cases. Playment Key Features Data Labeling Services: Provides high-quality data labeling services for various data types, including images, videos, text, and sensor data. Support: Global workforce of contractors and data labelers. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Audio Labeling Tool: The tool features a speech recognition training platform that can handle over five hundred languages and dialects. G2 Review Summary Playment has a rating of 4.7/5 based on 11 reviews. Users find Playment’s annotation performance fast and accurate. However, they find the tool expensive and that it needs more improvement in automated labeling features. Appen Appen is a data labeling services platform founded in 1996, making it one of the first and oldest solutions in the market, offering data labeling services for various industries. In 2019, it acquired Figure Eight to expand its software capabilities and help businesses train and improve their computer vision models. Appen 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: Support for natural language processing (NLP) 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. G2 Review Summary Appen has a rating of 4.2/5 based on 28 reviews. Users like that the tool is web-based and does not require specific installation procedures. However, the platform’s server crashes frequently, and the support team is slow to respond. Dataloop 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 Key Features Data Annotation: Supports multiple image annotation tasks, including classification, detection, and semantic segmentation. Collaboration Tool: It features tools for real-time collaboration among annotators, project sharing, and version control, allowing for efficient teamwork. Data Management: Offers data management capabilities, including data versioning, tracking, and organization for streamlined workflows. Model Management: Dataloop offers tools to manage different model versions and download SOTA models from the Model Marketplace. G2 Review Summary Dataloop has a rating of 4.4/5 based on 90 reviews. The tool’s plus points include its ease of use and annotation efficiency. However, users find it challenging to learn and face frequent performance issues. SuperAnnotate SuperAnnotate is an end-to-end AI platform that offers tools for data curation and automatic annotation with MLOps functionalities. It also lets you fine-tune LLMs using annotated data and RLHF. SuperAnnotate Key Features Multi-Data Type Support: Versatile annotation features for labeling videos, text, audio, and image data. AI Assistance: Integrates AI-assisted annotation to accelerate the labeling process and improve efficiency. Customization: Provides customizable annotation interfaces and workflows to tailor annotation tasks to specific project requirements. Export Formats: SuperAnnotate supports multiple data formats, including popular ones like JSON, COCO, and Pascal VOC. G2 Review Summary SuperAnnotate has a rating of 4.9/5 based on 137 reviews. Users find the tool’s feature set comprehensive and the interface intuitive. However, there have been complaints regarding its custom workflow setup and high price. V7 Labs V7 is a UK-based data annotation platform founded in 2018. The company enables teams to annotate image and video data using automated pipelines and custom workflows. The platform also offers model and data management tools to help users build high-quality training data for scalable AI projects. V7 Key Features Collaboration Capabilities: Project management and automation workflow functionality, with real-time collaboration and tagging. Data Management: The tool offers data management features, including functionalities to filter and sort data. It also helps organize and manage data classes at team and dataset levels. Auto-Annotate: Features auto-annotation that lets you use deep learning models to create pixel-perfect polygon masks. Auto-Track: V7 offers an auto-track feature for object tracking and instance segmentation in long videos. G2 Review Summary V7 has a rating of 4.8/5 based on 52 reviews. Users find its automation and collaboration features significantly helpful. However, they feel it lacks file manipulation options, and its sorting and filtering features do not perform well with large files. Hive Hive is a content-moderation platform that offers deep learning models to highlight harmful and explicit content in images, videos, text, and audio. It also features search and generative APIs to visualize similarities between images and videos and generate images based on textual prompts. Hive AI Key Features Ease of use: Hive offers an intuitive interface with multiple in-built image and text classification models. Embeddings: The platform lets you quickly create text embeddings to build retrieval augmented generation (RAG)-based LLMs. Search: Hive offers versatile web search functionality. You can use image prompts to retrieve relevant links to similar images. Generative Artificial Intelligence (Gen AI): Hive features APIs to generate text, images, and videos based on textual prompts. G2 Review Summary Hive has a rating of 4.6/5 based on 528 reviews. Users find its project management and collaboration features helpful. However, the interface is challenging to navigate and has a few glitches, which makes it complex to operate. Label Studio Label Studio is a popular open-source data labeling platform for annotating various data types, including images, text, audio, and video. It supports collaborative labeling, custom labeling interfaces, and integration with machine learning (ML) pipelines for data annotation tasks. Label Studio Key Features Customizable Labeling Interfaces: Label Studio lets you label data through flexible configurations that allow you to tailor annotation interfaces to specific tasks. Collaboration Tools: Real-time annotation and project-sharing capabilities for seamless collaboration among annotators. 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. ML Pipelines: Label Studio lets you connect the model development pipeline with the data labeling project. The method allows you to use ML models to predict labels, evaluate model performance, and perform human-in-the-loop labeling. G2 Review Summary G2 review not available. COCO Annotator COCO Annotator is a web-based labeling tool by Justin Brooks that is under the MIT license. The tool helps streamline the process of annotating images for object recognition, localization, and key point detection models. It also offers a range of features that cater to the diverse needs of machine learning practitioners, data scientists, and researchers. COCO Annotator Key Features Image Annotation: Supports annotation of images for object detection, instance segmentation, keypoint detection, and captioning tasks. Export Formats: The tool exports and stores annotations in the COCO format to facilitate large-scale object detection. Automation: The tool makes annotating an image easier by incorporating semi-trained models. It also provides access to advanced selection tools, including the Mask Region-based Convolutional Neural Network (MaskRCNN), Magic Wand, and Deep Extreme Cut (DEXTR) frameworks. Metadata Management: Users can create custom metadata for each instance or object. G2 Review Summary G2 review not available. Make Sense Make Sense AI is a user-friendly open-source annotation tool available under the GPLv3 license. It is accessible through a web browser and does not require advanced installations. The tool simplifies the annotation process for multiple image types. Make Sense 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, VGG JSON, and CSV), ensuring compatibility with diverse machine learning algorithms. Supported Annotation Types: The tool supports rectangles, lines, points, and polygons. G2 Review Summary G2 review not available. VGG Image Annotator VGG Image Annotator (VIA) is a versatile open-source tool by the Visual Geometry Group (VGG) for manually annotating image and video data. Released under the permissive BSD-2 clause license, VIA serves the needs of academic and commercial users, offering a lightweight and accessible solution for annotation tasks. VIA Key Features Lightweight and User-Friendly: VIA is a lightweight, self-contained annotation tool that uses HTML, Javascript, and CSS without external libraries. Offline Capability: The tool works offline, providing a full application experience within a single HTML file of less than 200 KB. Audio and Video Annotation: In addition to images, the tool lets users define temporal segments in audio and video data with textual descriptions. Supported Annotation Types: The tool allows you to draw rectangles, circles, ellipses, polygons, points, and polylines. G2 Review Summary G2 review not available. LabelMe LabelMe is an open-source web-based tool 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 Key Features Web-Based: Accessible through a web-based interface, allowing you to perform annotation tasks in any modern web browser without requiring software installation. Supported Data Types: The tool supports image and video annotation. Supported Annotation Types: LabelMe lets you draw polygons, rectangles, circles, lines, and points. Export Format: It lets you export annotation in VOC and COCO format for semantic and instance segmentation. G2 Review Summary G2 review not available. Key Takeaways: Image Annotation Tools in 2024 As data volume and variety increase, businesses must invest in a suitable and reliable annotation tool to build high-quality datasets for training models. Below are a few key points regarding the top image annotation tools and tips for selecting an appropriate solution. Best Annotation Tools in 2024: Encord, Amazon Sagemaker Ground Truth, and Scale Rapid are the top annotation tools in 2024. Ease-of-use: Most G2 reviews highlight issues with the user interface. You should ensure that you select a tool that offers intuitive navigation and labeling features. Automation: Select a platform that offers state-of-the-art automation features, including pre-trained models and smart labeling tools. Open-source vs. Paid Platforms: While open-source tools offer a cost-effective solution, they have limited functionality. Paid tools provide a rich feature set with robust customer support to help you annotate multiple data types. So, streamline your CV operations with the annotation tool that best suits your needs.