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Dataverse offers an advanced auto-tagging feature to simplify data management, automatically generating image tags for users. This functionality allows for efficient filtering, data categorization, and visualization of dataset performance under various conditions. In this guide, we will explore the auto-tagging capabilities of Dataverse and how they can benefit your data management process.
You can choose whether to perform auto-tagging when importing a dataset.

Quick Start

Import data with auto-tagging. Click on the video below for a quick overview:

Auto-tagging Categories:

Dataverse's auto-tagging feature can generate image tags based on the following categories while dataset import:
  • Weather: sunny, cloudy, rainy, snowy, foggy
  • Time of day: daytime, night, dawn or dusk
  • Scene: tunnel, residential, parking lot, city streets, gas stations, highway
The auto-tagging function is particularly suitable for detection of road scene data in scenarios like Autonomous Driving, ADAS.

Utilizing Auto-generated Tags

Once the tags are generated, you can use them to filter and manage your dataset effectively:

Data Visualization

Visualize your dataset by applying tag-based filters to focus on specific conditions. This allows you to assess your model's performance under various scenarios and identify potential areas for improvement.

Data Categorization

Organize your dataset into meaningful categories based on auto-generated tags. This can help streamline data management and make it easier to locate specific subsets of data.

Performance Analysis

Evaluate the performance of your AI model across different tag categories to understand its strengths and weaknesses. This can help guide model optimization and training strategies to improve overall performance.
By leveraging DataVerse's auto-tagging feature, you can enhance your data management process and gain valuable insights into your AI model's performance under different conditions. The ability to filter, visualize, and categorize data based on auto-generated tags simplifies the evaluation process and helps you make data-driven decisions for model optimization and deployment.