# Labeling

- [Labeling Toolboxes](/labeling/labeling-toolbox.md)
- [Images](/labeling/labeling-toolbox/images.md): The image labeling toolbox allows you to annotate one image at a time, such as .jpg, .png, .tiff, and many more formats you can import to Supervisely.
- [Videos 2.0](/labeling/labeling-toolbox/videos.md): Label hours-long videos without cutting them into images. In your browser, with multi-track timeline, built-in object tracking and segments tagging tools.
- [Videos 3.0](/labeling/labeling-toolbox/videos-3.0.md)
- [3D Point Cloud and Episodes](/labeling/labeling-toolbox/3d-point-cloud-episodes-2.md): This article about the new labeling interface for 3D Point Clouds in Supervisely that introduces a significantly enhanced workflow, offering extended functionality and improved usability.
- [Performance Upgrade for Dense Clouds](/labeling/labeling-toolbox/3d-point-cloud-episodes-2/3d-point-cloud-optimizations.md): This article describes performance improvements in the 3D Point Cloud labeling tool, including the migration to a WebGPU-based rendering pipeline and optimizations for the Pen and Select tools.
- [3D Point Cloud and Episodes (legacy)](/labeling/labeling-toolbox/3d-point-cloud-episodes-2/3d-point-clouds-episodes-1.md): 3D Point Cloud Episodes refer to sequences of point clouds that capture dynamic scenes over time, allowing for the analysis of object movements and interactions within those scenes.
- [DICOM](/labeling/labeling-toolbox/dicom.md): Label volumetric medical scans from CT, MRI, and PET in 2D or 3D using professional viewers, advanced editing tools, and AI-powered enhancements.
- [Multiview images](/labeling/labeling-toolbox/multi-view-images.md): Create image groups inside your dataset by assigning a grouping tag. View and label grouped images together, compare annotation results, or couple dependent imagery such as .nrrd studies.
- [Overlay](/labeling/labeling-toolbox/overlay.md): Inspect base images together with linked overlay layers, adjust opacity, and compare microscopy layers in one simple labeling workspace.
- [Multiview videos](/labeling/labeling-toolbox/multi-view-videos.md): Annotate multiple synchronized videos from different camera angles as a unified scene with shared objects and synchronized playback.
- [Fisheye](/labeling/labeling-toolbox/fisheye.md)
- [Labeling Tools](/labeling/labeling-tools.md)
- [Navigation & Selection Tools](/labeling/labeling-tools/navigation-and-selection-tools.md)
- [Point Tool](/labeling/labeling-tools/point-tool.md)
- [Bounding Box (Rectangle) Tool](/labeling/labeling-tools/bounding-box-rectangle-tool.md): In this guide, you'll learn how to use bounding box tool in the Supervisely image labeling toolbox and explore additional features that can help streamline your annotation experience.
- [Oriented Bounding Box Tool](/labeling/labeling-tools/oriented-bounding-box-tool.md): In this guide, you'll learn how to use oriented-bounding-box tool for video.
- [Polyline Tool](/labeling/labeling-tools/polyline-tool.md)
- [Polygon Tool](/labeling/labeling-tools/polygon-tool.md): Learn how to use the Polygon Tool for precise object annotation in semantic and instance segmentation tasks with Supervisely.
- [Brush Tool](/labeling/labeling-tools/brush-tool.md): Learn how to annotate objects of any complexity by creating freeform outlines using the Brush annotation tool.
- [Mask Pen Tool](/labeling/labeling-tools/mask-pen-tool.md): Learn how to use the Mask Pen annotation tool, a powerful combination of polygonal contours and freeform drawing, to create precise and flexible segmentation masks.
- [Smart Tool](/labeling/labeling-tools/smart-tool.md)
- [Graph (Keypoints) Tool](/labeling/labeling-tools/graph-keypoints-tool.md)
- [Frame-based tagging](/labeling/labeling-tools/frame-based-tags.md): Frame-based tagging is a crucial task in video annotation, allowing tags to be assigned to specific frame ranges or multiple intervals.
- [Labeling Jobs](/labeling/jobs.md)
- [Labeling Queues](/labeling/jobs/labeling-queues.md): Discover the power of collaborative annotation with labeling queues. Learn how to harness teamwork for efficient and accurate data annotation.
- [Labeling Consensus](/labeling/jobs/labeling-consensus.md)
- [Labeling Statistics](/labeling/jobs/labeling-statistics.md)
- [Labeling Quality Control](/labeling/jobs/labeling-quality-control.md): This article provides an overview of the Labeling Quality Control process in image projects using Supervisely's dedicated functionality.
- [Labeling Performance](/labeling/labeling-performance.md): Discover how to track annotation progress, team performance, and review stats across projects using flexible filters and visual dashboards.
- [Labeling with AI](/labeling/overview.md)
- [Live Training while Annotating](/labeling/overview/live-training.md): Live Training by Supervisely is a real-time AI annotation framework. Continuously fine-tune models on the fly to accelerate custom dataset labeling.
- [Smart Tool for Segmentation](/labeling/overview/smart-tool-labeling.md): Disrupt accustomed approaches and boost both labeling performance and quality with the help of interactive smart tools.
- [Images](/labeling/overview/images.md): In this guide, you'll learn about AI-driven tools which you can use in Supervisely to optimize your image labeling pipelines.
- [Videos](/labeling/overview/videos.md): In this guide, you'll learn about AI-driven tools which you can use in Supervisely to optimize your video labeling pipelines.
- [3D Point Clouds](/labeling/overview/3d-point-clouds.md): In this guide, you'll learn about AI-driven tools which you can use in Supervisely to optimize your 3D point clouds labeling pipelines.
- [DICOM](/labeling/overview/dicom.md): In this guide, you'll learn about AI-driven tools which you can use in Supervisely to optimize your DICOM labeling pipelines.