Multi-view images
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.
Last updated
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.
Last updated
Supervisely's Multi-View Image Labeling Toolbox allows simultaneous annotation of multiple images on a single screen, significantly streamlining the data annotation process. This guide covers key features, tools, and steps for annotating multi-view images, enabling you to build custom training datasets quickly and effectively.
Check out our comprehensive guide on uploading and annotating multi-view images for custom training datassets simultaneously using multi-view mode.
Synchronized Views: Annotate objects on multiple images simultaneously, ideal for multi-spectral images.
Flexible Grouping: Group images by various criteria (e.g., camera angles, object classes) to suit your needs.
Manual Tools: Use bounding boxes, polygons, masks, brushes, polylines, and keypoints for precise annotations.
AI-Assisted Segmentation: Speed up labeling with the Smart Tool, leveraging pre-trained models.
Python SDK: Automate annotation tasks and integrate them into your workflow.
Organize images into groups by creating a project directory. Example:
📂 dataset_name
┣ 📂 group_name_1
┃ ┣ 🏞️ image_1.png
┃ ┣ 🏞️ image_2.png
┃ ┗ 🏞️ image_3.png
┗ 📂 group_name_2
┣ 🏞️ image_4.png
┣ 🏞️ image_5.png
┣ 🏞️ image_6.png
Use the Import Wizard or the "Import Images Groups" app to upload and group multi-view images. Enable multi-view mode in the Labeling Tool for synchronized annotation.
Annotate multiple images on a single screen using manual tools or the AI-assisted Smart Tool for faster and more accurate labeling. Customize annotations with tags and other attributes.
Export annotations in various formats (e.g., Supervisely JSON, COCO, YOLO) for further processing or model training.
Use Supervisely's team features to manage labeling jobs, monitor progress, and ensure quality control during large-scale annotation projects.
Convert existing projects to a multi-view setup using the "Group Images for Multi-View Labeling" app. This enables synchronized annotation for already uploaded images.
Automate your multi-view annotation processes with the Supervisely Python SDK. This allows for efficient batch processing, custom integrations, and more streamlined workflows.
In this 4-minute video guide, you will learn how to import multi-view images and label them in Supervisely Multi-View Image Annotation Tool. The video includes the following steps:
Structuring and importing multi-view images into Supervisely
Annotating multi-view images using manual tools
Speeding up the annotation process with AI-assisted Supervisely Smart Tool
Exploring the synchronized views and labeling in Multi-View mode
Exporting multi-view images with annotations
