Best Image Annotation Tools

Compare the Top Image Annotation Tools as of June 2025

What are Image Annotation Tools?

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 currently available using the table below. This list is updated regularly.

  • 1
    Supervisely

    Supervisely

    Supervisely

    The leading platform for entire computer vision lifecycle. Iterate from image annotation to accurate neural networks 10x faster. With our best-in-class data labeling tools transform your images / videos / 3d point cloud into high-quality training data. Train your models, track experiments, visualize and continuously improve model predictions, build custom solution within the single environment. Our self-hosted solution guaranties data privacy, powerful customization capabilities, and easy integration into your technology stack. A turnkey solution for Computer Vision: multi-format data annotation & management, quality control at scale and neural networks training in end-to-end platform. Inspired by professional video editing software, created by data scientists for data scientists — the most powerful video labeling tool for machine learning and more.
  • 2
    Automaton AI

    Automaton AI

    Automaton AI

    With Automaton AI’s ADVIT, create, manage and develop high-quality training data and DNN models all in one place. Optimize the data automatically and prepare it for each phase of the computer vision pipeline. Automate the data labeling processes and streamline data pipelines in-house. Manage the structured and unstructured video/image/text datasets in runtime and perform automatic functions that refine your data in preparation for each step of the deep learning pipeline. Upon accurate data labeling and QA, you can train your own model. DNN training needs hyperparameter tuning like batch size, learning, rate, etc. Optimize and transfer learning on trained models to increase accuracy. Post-training, take the model to production. ADVIT also does model versioning. Model development and accuracy parameters can be tracked in run-time. Increase the model accuracy with a pre-trained DNN model for auto-labeling.
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