# Labeling - [Labeling Toolboxes](https://docs.supervisely.com/labeling/labeling-toolbox.md) - [Images](https://docs.supervisely.com/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](https://docs.supervisely.com/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](https://docs.supervisely.com/labeling/labeling-toolbox/videos-3.0.md) - [3D Point Cloud and Episodes](https://docs.supervisely.com/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](https://docs.supervisely.com/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)](https://docs.supervisely.com/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](https://docs.supervisely.com/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](https://docs.supervisely.com/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](https://docs.supervisely.com/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](https://docs.supervisely.com/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](https://docs.supervisely.com/labeling/labeling-toolbox/fisheye.md) - [Video tracking](https://docs.supervisely.com/labeling/labeling-toolbox/video-tracking.md) - [3D Point Clouds](https://docs.supervisely.com/labeling/labeling-toolbox/3d-point-clouds.md): Label comprehensive 3D scenes from LiDAR or RADAR sensors with additional photo and video context, AI object tracking and point cloud segmentation. - [Sensor-fusion](https://docs.supervisely.com/labeling/labeling-toolbox/sensor-fusion.md) - [Labeling Tools](https://docs.supervisely.com/labeling/labeling-tools.md) - [Navigation & Selection Tools](https://docs.supervisely.com/labeling/labeling-tools/navigation-and-selection-tools.md) - [Point Tool](https://docs.supervisely.com/labeling/labeling-tools/point-tool.md) - [Bounding Box (Rectangle) Tool](https://docs.supervisely.com/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](https://docs.supervisely.com/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](https://docs.supervisely.com/labeling/labeling-tools/polyline-tool.md) - [Polygon Tool](https://docs.supervisely.com/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](https://docs.supervisely.com/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](https://docs.supervisely.com/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](https://docs.supervisely.com/labeling/labeling-tools/smart-tool.md) - [Graph (Keypoints) Tool](https://docs.supervisely.com/labeling/labeling-tools/graph-keypoints-tool.md) - [Frame-based tagging](https://docs.supervisely.com/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](https://docs.supervisely.com/labeling/jobs.md) - [Labeling Queues](https://docs.supervisely.com/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](https://docs.supervisely.com/labeling/jobs/labeling-consensus.md) - [Labeling Statistics](https://docs.supervisely.com/labeling/jobs/labeling-statistics.md) - [Labeling Quality Control](https://docs.supervisely.com/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](https://docs.supervisely.com/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](https://docs.supervisely.com/labeling/overview.md) - [Live Training while Annotating](https://docs.supervisely.com/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. - [Introducing Live Training](https://docs.supervisely.com/labeling/overview/live-training/technical-report.md): Introducing Live Training: Model Training during Annotation. A novel annotation approach enables models to quickly adapt to domain-specific data and effectively assist in labeling. - [Smart Tool for Segmentation](https://docs.supervisely.com/labeling/overview/smart-tool-labeling.md): Disrupt accustomed approaches and boost both labeling performance and quality with the help of interactive smart tools. - [Images](https://docs.supervisely.com/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](https://docs.supervisely.com/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](https://docs.supervisely.com/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](https://docs.supervisely.com/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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.supervisely.com/labeling.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.