What is a Project?

A project in Datature Nexus is your dedicated workspace for managing computer vision workflows - from data storage and annotations to model training and deployment. Each project serves as an isolated environment where you and your team can collaborate on specific computer vision tasks.

Key Project Features

Data Management

Projects allow you to organize your data and annotations in a structured manner. You can upload images, create annotations, and manage your datasets all within a single project space.

Model Development

Each project supports creating workflows, running training, and deploying models. You can experiment with different model architectures and training parameters while keeping your work organized.

Team Collaboration

Projects in Datature Nexus are designed for team collaboration. Multiple users can work simultaneously on various tasks like data uploading, annotation creation, and training parameter configuration. Learn more about team management in Managing Project Collaborators.

Project Dashboard

The Project Dashboard serves as your central hub for:

• Monitoring project progress
• Managing team access
• Accessing different project components
• Viewing project metrics

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Feature availability varies by plan tier. Check Plans and Pricing for details and learn about Upgrading Your Plan to access additional features.

Common Questions

How do I get started with a new project?

Create your first project by following the steps in Creating a Project. You can later rename or delete your project as needed.

What's the difference between projects and workflows?

Projects are top-level containers for your computer vision work, while workflows are specific model configurations within a project. You can have multiple workflows in a single project to experiment with different training parameters.

How many collaborators can I add to a project?

The number of collaborators depends on your plan tier. Visit Managing Project Collaborators for details on adding and managing team members.

Should I create separate projects for different model experiments?

If you're using the same dataset for the same type of task (e.g., object detection), create multiple workflows instead of new projects. This helps maintain data consistency and reduces duplicate work.