Compare the Top Image Annotation Tools for Linux as of June 2025

What are Image Annotation Tools for Linux?

Image annotation tools are used to automatically process and label digital images using advanced techniques in machine learning, AI, and computer vision. These tools can accurately recognize important features in images, such as objects, characters, or facial expressions. This data can then be used for various purposes such as automatic image tagging and sorting. Image annotation is becoming an increasingly popular tool for organizing large databases of images and videos. Compare and read user reviews of the best Image Annotation tools for Linux currently available using the table below. This list is updated regularly.

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    APISCRAPY

    APISCRAPY

    AIMLEAP

    APISCRAPY is an AI-driven web scraping and automation platform converting any web data into ready-to-use data API. Other Data Solutions from AIMLEAP: AI-Labeler: AI-augmented annotation & labeling tool AI-Data-Hub: On-demand data for building AI products & services PRICE-SCRAPY: AI-enabled real-time pricing tool API-KART: AI-driven data API solution hub  About AIMLEAP AIMLEAP is an ISO 9001:2015 and ISO/IEC 27001:2013 certified global technology consulting and service provider offering AI-augmented Data Solutions, Data Engineering, Automation, IT and Digital Marketing services. AIMLEAP is certified as ‘The Great Place to Work®’. Since 2012, we have successfully delivered projects in IT & digital transformation, automation-driven data solutions, and digital marketing for 750+ fast-growing companies globally. Locations: USA | Canada | India| Australia
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    Starting Price: $25 per website
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    SuperAnnotate

    SuperAnnotate

    SuperAnnotate

    SuperAnnotate is the world's leading platform for building the highest quality training datasets for computer vision and NLP. With advanced tooling and QA, ML and automation features, data curation, robust SDK, offline access, and integrated annotation services, we enable machine learning teams to build incredibly accurate datasets and successful ML pipelines 3-5x faster. By bringing our annotation tool and professional annotators together we've built a unified annotation environment, optimized to provide integrated software and services experience that leads to higher quality data and more efficient data pipelines.
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    Prodigy

    Prodigy

    Explosion

    Radically efficient machine teaching. An annotation tool powered by active learning. Prodigy is a scriptable annotation tool so efficient that data scientists can do the annotation themselves, enabling a new level of rapid iteration. Today’s transfer learning technologies mean you can train production-quality models with very few examples. With Prodigy you can take full advantage of modern machine learning by adopting a more agile approach to data collection. You'll move faster, be more independent and ship far more successful projects. Prodigy brings together state-of-the-art insights from machine learning and user experience. With its continuous active learning system, you're only asked to annotate examples the model does not already know the answer to. The web application is powerful, extensible and follows modern UX principles. The secret is very simple: it's designed to help you focus on one decision at a time and keep you clicking – like Tinder for data.
    Starting Price: $490 one-time fee
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    Diffgram Data Labeling
    Your AI Data Platform Quality Training Data for Enterprise Data Labeling Software for Machine Learning Free on your Kubernetes Cluster Up to 3 Users. TRUSTED BY 5,000 HAPPY USERS WORLDWIDE Images, Video, Text Spatial Tools Quadratic Curves, Cuboids, Segmentation, Box, Polygons, Lines, Keypoints, Classification Tags, and More Use the exact spatial tool you need. All tools are easy to use, fully editable, and powerful ways to represent your data. All tools are available in Video. Attribute Tools More Meaning. More degrees of freedom through: Radio buttons. Multiple select. Date pickers. Sliders. Conditional logic. Directional Vectors. And more! You can capture complex knowledge and encode it into your AI. Streaming Data Automation Up to 10x Faster then manual labeling
    Starting Price: Free
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    LinkedAI

    LinkedAI

    LinkedAi

    We label your data with the higher quality standards to fulfill the needs of the most complex AI projects, using our proprietary labeling platform. Now you can get back to creating the products your customers love. We provide an end-to-end solution for image annotation with fast labeling tools, synthetic data generation, data management, automation features and annotation services on-demand with integrated tooling to accelerate and finish computer vision projects. When every pixel matters, you need accurate, AI-powered intuitive image annotation tools to support your specific use case, including instances, attributes and much more. Our in-house highly trained data labelers are able to deal with any data challenge. As your data labeling needs grow over time, you can count on us to scale the workforce necessary to meet your goals, and in contrast to crowdsourcing platforms your data quality will not suffer.
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    CVAT

    CVAT

    CVAT

    Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale. CVAT’s blazing-fast, intuitive user interface, was designed by working closely with real-world teams solving real-world problems. From medical to retail to autonomous vehicles, world’s most ambitious AI teams use CVAT as a part of their AI workflow every day. No matter what your input data or expected results are, CVAT is ready. It works great with images, videos, and even 3D. Bounding boxes, polygons, points, skeletons, cuboids, trajectories, and more. Annotate more efficiently with automated interactive algorithms like intelligent scissors, histogram equalization, and more. Gain actionable insights with metrics such as annotator working hours, objects per hour, and more.
    Starting Price: $33 per month
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    Colabeler

    Colabeler

    Colabeler

    Image classification, bounding box, polygon, curve, 3D localization Video trace, text classification, text entity labeling. Support custom task plugin, you can create your own label tool. Export PascalVoc XML (The same format used by ImageNet) and CoreNLP file. Supports Windows/Mac/CentOS/Ubuntu.
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    Sixgill Sense
    Every step of the machine learning and computer vision workflow is made simple and fast within one no-code platform. Sense allows anyone to build and deploy AI IoT solutions to any cloud, the edge or on-premise. Learn how Sense provides simplicity, consistency and transparency to AI/ML teams with enough power and depth for ML engineers yet easy enough to use for subject matter experts. Sense Data Annotation optimizes the success of your machine learning models with the fastest, easiest way to label video and image data for high-quality training dataset creation. The Sense platform offers one-touch labeling integration for continuous machine learning at the edge for simplified management of all your AI solutions.
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    Sama

    Sama

    Sama

    We offer the highest quality SLA (>95%), even on the most complex workflows. Our team assists with anything from implementing a robust quality rubric to raising edge cases. As an ethical AI company, we have provided economic opportunities for over 52,000 people from underserved and marginalized communities. ML Assisted annotation created up to 3-4x efficiency improvement for a single class annotation. We quickly adapt to ramp-ups, focus shifts, and edge cases. ISO certified delivery centers, biometric authentication, and user authentication with 2FA ensure a secure work environment. Seamlessly re-prioritize tasks, provide quality feedback, and monitor models in production. We support data of all types. Get more with less. We combine machine learning and humans in the loop to filter data and select images relevant to your use case. Receive sample results based on your initial guidelines. We work with you to identify edge cases and recommend annotation best practices.
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