Supervisely
AboutAPI ReferenceSDK Reference
  • 🤖What's Supervisely
  • 🚀Ecosystem of Supervisely Apps
  • 💡FAQ
  • 📌Getting started
    • How to import
    • How to annotate
    • How to invite team members
    • How to connect agents
    • How to train models
  • 🔁Import and Export
    • Import
      • Overview
      • Import using Web UI
      • Supported annotation formats
        • Images
          • 🤖Supervisely JSON
          • 🤖Supervisely Blob
          • COCO
          • Yolo
          • Pascal VOC
          • Cityscapes
          • Images with PNG masks
          • Links from CSV, TXT and TSV
          • PDF files to images
          • Multiview images
          • Multispectral images
          • Medical 2D images
          • LabelMe
          • LabelStudio
          • Fisheye
          • High Color Depth
        • Videos
          • Supervisely
        • Pointclouds
          • Supervisely
          • .PCD, .PLY, .LAS, .LAZ pointclouds
          • Lyft
          • nuScenes
          • KITTI 3D
        • Pointcloud Episodes
          • Supervisely
          • .PCD, .PLY, .LAS, .LAZ pointclouds
          • Lyft
          • nuScenes
          • KITTI 360
        • Volumes
          • Supervisely
          • .NRRD, .DCM volumes
          • NIfTI
      • Import sample dataset
      • Import into an existing dataset
      • Import using Team Files
      • Import from Cloud
      • Import using API & SDK
      • Import using agent
    • Migrations
      • Roboflow to Supervisely
      • Labelbox to Supervisely
      • V7 to Supervisely
      • CVAT to Supervisely
    • Export
  • 📂Data Organization
    • Core concepts
    • MLOps Workflow
    • Projects
      • Datasets
      • Definitions
      • Collections
    • Team Files
    • Disk usage & Cleanup
    • Quality Assurance & Statistics
      • Practical applications of statistics
    • Operations with Data
      • Data Filtration
        • How to use advanced filters
      • Pipelines
      • Augmentations
      • Splitting data
      • Converting data
        • Convert to COCO
        • Convert to YOLO
        • Convert to Pascal VOC
    • Data Commander
      • Clone Project Meta
  • 📝Labeling
    • Labeling Toolboxes
      • Images
      • Videos 2.0
      • Videos 3.0
      • 3D Point Clouds
      • DICOM
      • Multiview images
      • Fisheye
    • Labeling Tools
      • Navigation & Selection Tools
      • Point Tool
      • Bounding Box (Rectangle) Tool
      • Polyline Tool
      • Polygon Tool
      • Brush Tool
      • Mask Pen Tool
      • Smart Tool
      • Graph (Keypoints) Tool
      • Frame-based tagging
    • Labeling Jobs
      • Labeling Queues
      • Labeling Consensus
      • Labeling Statistics
    • Labeling with AI-Assistance
  • 🤝Collaboration
    • Admin panel
      • Users management
      • Teams management
      • Server disk usage
      • Server trash bin
      • Server cleanup
      • Server stats and errors
    • Teams & workspaces
    • Members
    • Issues
    • Guides & exams
    • Activity log
    • Sharing
  • 🖥️Agents
    • Installation
      • Linux
      • Windows
      • AMI AWS
      • Kubernetes
    • How agents work
    • Restart and delete agents
    • Status and monitoring
    • Storage and cleanup
    • Integration with Docker
  • 🔮Neural Networks
    • Overview
    • Inference & Deployment
      • Overview
      • Supervisely Serving Apps
      • Deploy & Predict with Supervisely SDK
      • Using trained models outside of Supervisely
    • Model Evaluation Benchmark
      • Object Detection
      • Instance Segmentation
      • Semantic Segmentation
      • Custom Benchmark Integration
    • Custom Model Integration
      • Overview
      • Custom Inference
      • Custom Training
    • Legacy
      • Starting with Neural Networks
      • Train custom Neural Networks
      • Run pre-trained models
  • 👔Enterprise Edition
    • Get Supervisely
      • Installation
      • Post-installation
      • Upgrade
      • License Update
    • Kubernetes
      • Overview
      • Installation
      • Connect cluster
    • Advanced Tuning
      • HTTPS
      • Remote Storage
      • Single Sign-On (SSO)
      • CDN
      • Notifications
      • Moving Instance
      • Generating Troubleshoot Archive
      • Storage Cleanup
      • Private Apps
      • Data Folder
      • Firewall
      • HTTP Proxy
      • Offline usage
      • Multi-disk usage
      • Managed Postgres
      • Scalability Tuning
  • 🔧Customization and Integration
    • Supervisely .JSON Format
      • Project Structure
      • Project Meta: Classes, Tags, Settings
      • Tags
      • Objects
      • Single-Image Annotation
      • Single-Video Annotation
      • Point Cloud Episodes
      • Volumes Annotation
    • Developer Portal
    • SDK
    • API
  • 💡Resources
    • Changelog
    • GitHub
    • Blog
    • Ecosystem
Powered by GitBook
On this page
  • Json format for project meta
  • Fields definitions

Was this helpful?

  1. Customization and Integration
  2. Supervisely .JSON Format

Project Meta: Classes, Tags, Settings

PreviousProject StructureNextTags

Last updated 3 months ago

Was this helpful?

Each project in Supervisely has a set of predetermined classes and tags. This information is called Project Meta and stored in a corresponding JSON-based meta.json file. This file contains all of the necessary data from the project's classes and tags. Also, it has information about the project's type and settings:

Json format for project meta

{
    "classes": [
        {
            "title": "bike",
            "shape": "rectangle",
            "color": "#F6FF00",
            "geometry_config": {},
            "id": 6509759,
            "hotkey": ""
        },
        {
            "title": "car",
            "shape": "polygon",
            "color": "#BE55CE",
            "geometry_config": {},
            "id": 6509764,
            "hotkey": ""            
        },
        {
            "title": "person",
            "shape": "bitmap",
            "color": "#00FF12",
            "geometry_config": {},
            "id": 6509777,
            "hotkey": ""            
        },
        {
            "title": "dog",
            "shape": "alpha_mask",
            "color": "#C4D68A",
            "geometry_config": {},
            "id": 6509789,
            "hotkey": ""            
        }
    ],
    "tags": [
        {
            "name": "cars_number",
            "color": "#A0A08C",
            "value_type": "any_number",
            "id": 27855,
            "hotkey": "",
            "applicable_type": "all",
            "classes": []            
        },
        {
            "name": "like",
            "color": "#D98F7E",
            "value_type": "none",
            "id": 27856,
            "hotkey": "",
            "applicable_type": "all",
            "classes": []               
        },
        {
            "name": "situated",
            "color": "#855D79",
            "value_type": "oneof_string",
            "values": [
                "inside",
                "outside"
            ],
            "id": 27857,
            "hotkey": "",
            "applicable_type": "all",
            "classes": []               
        },
        {
            "name": "car_color",
            "color": "#ED68A1",
            "value_type": "any_string",
            "id": 27858,
            "hotkey": "",
            "applicable_type": "all",
            "classes": ["car"]
        }
    ],
    "projectType": "images",
    "projectSettings": {
        "multiView": {
            "enabled": true,
            "tagName": "cars_number", 
            "tagId": null, 
            "isSynced": false
        }
    }
}

Fields definitions

  • classes(string) - list of all possible object classes. Each class has the following fields assigned:

    • title(string) - the unique identifier of a class

    • color(string) - hex color code

    • geometry_config(dictionary) [optional] - additional settings of the geometry. May be used with keypoints.

    • id (int) [optional] - the unique identification value of the class on the server

    • hotkey (string) [optional] - hotkey for the Labeling Tool to quickly change active annotation class

    • name(string) - the unique identifier of a tag

    • value_type(string) - one of the possible tag

    • color(string) - hex color code

    • values(string) [optional] - initially predefined set of possible values

    • id (int) [optional] - the unique identification value of the tag

    • hotkey (string) [optional] - hotkey for the Labeling Tool to quickly assign tag to object or image

    • applicable_type (string) [optional] - defines the applicability of Tag only to images (imagesOnly), objects (objectsOnly), or both (all). By default tag can be assigned to both images and objects.

    • classes (list of strings) [optional] - defines the applicability of Tag only to certain classes

    • target_type (string) [optional] - Defines the scope of application. It can be applied globally for the entire duration or to individual frames, with the following values: entitiesOnly,framesOnly, all. Since images do not have "frames," the all option is used for them.

  • projectType(string) - one of the possible project types: images, videos, volumes, point_clouds, and point_cloud_episodes

    • multiView - additional properties for the multiview mode

      • enabled(bool) - enable multiview mode

      • tagName(string) (optional) - the name of the tag which will be used as a group tag

      • tagId(int) [optional] - the id of the tag which will be used as a group tag

      • isSynced(bool) - enable synchronization of views for the multiview mode

Please note, that it is necessary that the group tag in multiView should have the corresponding name or the id in the tags field. Also, the value_type should not be none.

shape(string) - class shape, read more

tags(string) - list of all possible tags that can be assigned to images or objects. Read more

projectSettings(string) [optional] - additional project properties. For example, multiview settings. Read more

🔧
here
here
here