What Are Tags in Data Annotation?

Tags are labels used to add additional information to annotated data. Unlike bounding boxes, polygons, or other annotation shapes that define specific regions, tags apply to an entire image or video frame, providing metadata that describes the overall scene rather than individual objects.

Tags help structure data, making it easier to filter and process. They can indicate shooting conditions, scene type, or even technical characteristics of an image.

What Tasks Use Tags?

1. Image Classification

In classification tasks, tags serve as primary labels that indicate an image’s category. For example, if a dataset contains images of urban and rural areas, the following tags can be used:

• City
• Village
• Suburb

2. Video Annotation and Frame Processing

When annotating videos, tags help classify scenes and frames. For example:

• Moving camera
• Static shot
• Fast object movement

These tags assist algorithms in determining the complexity of video processing and adapting models accordingly.

3. Automatic Data Filtering

Tags can be used to automatically exclude images from the training set if they don’t meet specific criteria (e.g., "too dark image", "blurry photo").

Advantages and Disadvantages of Tags

Advantages:

• Simplified classification – Tags allow quick categorization of images.
• Flexibility – Custom labels can be added without modifying the data structure.
• Filtering and search – Tags help quickly locate the necessary images.
• Support for automated processing – Specific images can be automatically labeled and excluded from training.

Disadvantages:

• Limited detail – Tags provide information about the overall scene but not specific objects.
• Potential data redundancy – If certain information is duplicated in object attributes, it may lead to unnecessary data complexity.

Examples of Tag Usage in Popular Datasets

COCO (Common Objects in Context)

The COCO dataset includes object annotations but also features metadata similar to tags, such as:

• "Outdoor/Indoor" (outdoor or indoor scene)
• "Weather conditions" (e.g., rainy, sunny)

Open Images Dataset

This dataset uses tags for image classification and to indicate shooting conditions, such as:

• "Aerial view" (top-down perspective)
• "Black and white" (grayscale image)

Conclusion

Tags in CVAT play a crucial role in data annotation, especially in classification and video annotation tasks. They help structure datasets, speeding up filtering and data analysis.