Creating Your First Project

Flexible configuration of the ontology required for the project.

Before using all the features of the platform, it is recommended to set up your project information in advance, as this can help you quickly configure your data and model objectives in the future.

Hint

Establish a project and set up ontology before beginning any data operations.

Quick Start

Explore the step-by-step process of creating your first AI project. Click on the video below for a quick overview:

Project List

On the Project page, you can see a list of all Projects.

Clicking on "Detail" will allow you to view the details, including the specifications and sensor settings of the project.

Creating a New Project

Clicking on "Create Project" will create a new project. In the popup window, fill in the relevant information as follows:

Step 1: Project Setting

• Name: Project Name

• Description: Description

• Sensor (Stream): The sensor settings and names used in this project, affect the data format of the project. Please choose the sensor combination according to your needs.

• Click "Next" to enter the "Ontology" settings for the target object class and details of this project.

Sensor Name

The category and name of your project sensor need to correspond to your annotation format. If you'll be uploading using the visionai format in the future, make sure the data structure and the sensor in visionai.json match what's set here.

More infomation on visionai format

Step 2: Ontology Setting

• Name: Ontology Name

• Type: Annotation data format, currently offering the following choices:

  • Object Detection (bbox)

  • Class: Add the class name that needs to be detected for this project and set a specific color.

  • Sub-Class: Do classification for this item in Sub-Class.

  • Attribute: Add or delete attributes flexibly for this class, set the name, type, and field items, and currently have the following types to choose from:

    • Option: option (vec)

    • Number: number (double)

    • Text: text

    • Boolean: true/false

Ontology Setting

The annotation data format selected in "Ontology" will affect the corresponding dataset and the available AI models.

Details can correspond to Vision AI Format

Step 3: Tag

Tag: For the taggings required for this project, you can flexibly set the name, type, and field items, and currently have the following types to choose from:

• Option: option (vec)

• Number: number (double)

• Text: text

• Boolean: true/false

Tags on Ground Truth

The system only recognizes tags present in the ground truth data and does not read tags from other annotation sources. If you require specific tags to be associated with your data and displayed on the platform, please ensure they are included in your ground truth annotations.

Adding a Project using Python SDK.

Use Dataverse-SDK for Python to help you to interact with the Dataverse platform by Python. Currently, the library supports:

• Create Project with your input ontology and sensors

• Get Project by project-id

https://github.com/linkervision/dataverse-sdk/blob/master/python/README.md

Create Project Example The create_project method will create project on the connected site with the defined ontology and sensors.

# 1) Create ontology with ontologyclass object
ontology = Ontology(
    name="sample ontology",
    image_type=OntologyImageType._2D_BOUNDING_BOX,
    pcd_type = None,
    classes=[
        OntologyClass(name="Pedestrian", rank=1, color="#234567"),
        OntologyClass(name="Truck", rank=2, color="#345678"),
        OntologyClass(name="Car", rank=3, color="#456789"),
        OntologyClass(name="Cyclist", rank=4, color="#567890"),
        OntologyClass(name="DontCare", rank=5, color="#6789AB"),
        OntologyClass(name="Misc", rank=6, color="#789AB1"),
        OntologyClass(name="Van", rank=7, color="#89AB12"),
        OntologyClass(name="Tram", rank=8, color="#9AB123"),
        OntologyClass(name="Person_sitting", rank=9, color="#AB1234"),
    ],
)