Data annotation is the process of structuring information so that a computer can "understand" it. It is the foundation for training machine learning models and artificial intelligence (AI) systems. Without properly annotated data, it is impossible to build a high-quality model.

Data annotation is done by annotators. An annotator is a person who manually labels data, indicating what is shown in an image, said in audio, or written in text. Their task is not just to "draw," but to turn raw data into training material for AI.

Here is how the process of annotating a car using a bounding box looks. In this way, we define the location of the car in the image and label it as “Car.”
For AI, this means: "This is what a car looks like — learn to recognize such objects on your own."

Where is data annotation used?

Data annotation is used in many fields where artificial intelligence and machine learning are involved.

Automotive & Autonomous Driving

Annotation is the basis for teaching smart machines to understand what’s happening on the road.

What is annotated:

• Pedestrians, cars, road signs, traffic lights
  
• Lane markings, road structure
  
• Danger zones and obstacles
  

Examples:
Tesla, Waymo — use annotated video data to train autopilot systems.

Medicine

Annotation can save lives — it helps AI learn to detect diseases in medical scans.

What is annotated:

• Tumors, blood clots, fractures in MRI, CT, and X-rays
  
• Organ contours (lungs, heart, brain)
  
• Cells, viruses, bacteria in microscope images
  

Examples:
Cancer detection, tuberculosis, COVID-19, automatic blood cell counting.

Retail & Stores

AI helps optimize sales by analyzing customer behavior.

What is annotated:

• Products on shelves, their positioning
  
• Visitors: how many people, where they look, where they stand
  
• Emotions, age, gender (for personalized advertising)
  

Examples:
Cameras in stores record video used to train models to recognize inventory and customer activity.

Satellite & Drone Imagery

Annotation is used in agriculture, ecology, and defense.

What is annotated:

• Fields, roads, buildings, bodies of water
  
• Trees, animals, crops, fires, destruction
  

Examples:
Google Maps, agricultural monitoring, illegal deforestation detection.

Security & Surveillance

AI recognizes suspicious behavior or identities in a video feed.

What is annotated:

• Faces, human figures, suspicious actions
  
• Access to restricted zones
  

Examples:
Monitoring in train stations, stadiums, airports.

Education & AI Research

Universities and research labs create datasets to train and test new models.

What is annotated:

• Images, text, audio, and video for open science projects
  

Examples:
COCO, ImageNet, Cityscapes — world-famous datasets created by manual annotation.

What problem does annotation solve?

Annotation is not just adding labels to data — it solves the core problem of making data understandable for machine learning.

The main goal of annotation is to prepare raw data so that a neural network can learn from it and make intelligent predictions.

In practice, machines don't understand images like humans — they only see pixels.
Annotation explains: “This is a pedestrian,” “Here’s a dog,” “This is a field boundary.”
This information becomes the “correct answer” for training the model.

Without annotation, a model would:

• Have no idea what is right or wrong
  
• Learn “blindly” from noisy, unstructured data
  
• Make unacceptable errors in real-world tasks (especially in medicine or transportation)
  

Examples of problems annotation solves:

• A car must recognize pedestrians → people must be annotated in images
  
• Medical AI for diagnosis → tumors must be annotated in scans
  
• AI for shelf inventory → every product must be labeled
  

In summary: annotation turns data into knowledge — the foundation for artificial intelligence.

Main Types of Annotation in CVAT

When working with CVAT (Computer Vision Annotation Tool), you'll encounter various types of annotation. Here's a quick overview:

Classification (What is in the image?)

Definition:
The model sees the entire image and determines what is depicted, without specifying object locations.

Process:
The annotator selects an appropriate label for the whole image using tags.

Example:

Key point:

• No location specified
  
• Only the meaning of the image is captured
  

Object Detection (What and where is it?)

Definition:
Now it’s important not only what is in the image, but also where the objects are located.
Rectangles (bounding boxes) are drawn around objects, each labeled accordingly.

Process:
Draw a box around each object and label it (e.g., “car,” “person,” “bike”).

Example:

Key point:

• Object count and location are visible
  

Segmentation (Precise shape of the object)

Definition:
More advanced than bounding boxes — draw the exact shape of the object to show its outline in detail.

Process:
Draw the object’s boundary using polygons or masks.

Example:

Key point:

• Maximum precision
  
• Annotation of an object with background exclusion
  

Tracking (Follow the object across video)

Definition:
In video, it’s important not only to find an object but to follow it across frames.
The goal is to understand that it’s the same object moving through the scene.

Process:
Draw a box or contour in each frame
Assign an ID to track the same object through time

Example:

Key point:

• Object is tracked over time
  
• Enables analysis of movement, speed, direction

Skeleton Annotation (Keypoints and body pose)

Definition:
This type of annotation focuses on identifying the precise position of specific keypoints on an object — most often a human or animal. These keypoints are then connected to form a skeleton that represents the pose or posture.

Process:
The annotator marks each keypoint (e.g., head, shoulders, elbows, knees, etc.) and connects them according to a predefined skeleton structure.
Each keypoint can also have visibility flags (e.g., visible, occluded, not labeled).

Example:

Key point:

• Allows detailed analysis of body movements and postures
• Essential in sports analytics, gesture recognition, animation, healthcare, and safety monitoring
  
• Often used together with tracking to follow poses across video frames

3D Annotation (Depth and spatial position of objects)

Definition:
3D annotation allows annotators to mark objects in three-dimensional space, adding depth information to traditional 2D labeling. This is essential in applications like autonomous driving, robotics, and augmented reality.

Process:
Use cuboids (3D boxes) to surround the object. Each cuboid includes not only width and height, but also depth and orientation in space. The annotator adjusts the cuboid’s perspective to align it with the object's shape and position in the scene.

‍Example:

Key point:

• Captures real-world spatial orientation and size
  
• Crucial for depth-aware models
  
• Used with multi-view camera systems or LiDAR data
  
• Adds complexity, but provides richer geometric context

‍

‍

‍Roles in the Annotation Process

Annotator

The main worker who manually annotates data according to the instructions.

Responsibilities:

• Study the guidelines carefully before starting
  
• Annotate accurately: use the correct tools and name object classes properly
  
• Meet deadlines and required volume
  
• Ask the team lead when in doubt
  

Validator

Checks completed annotation against instructions and identifies errors. Often works after annotation is done.

Responsibilities:

• Assess quality: accuracy of contours, correct classes, completeness
  
• Mark and fix errors (if allowed)
  
• Request revision and provide feedback
  
• Ensure project quality standards are met
  

Team Lead

Manages and oversees the annotation process. Connects the team with the client.

Responsibilities:

• Create and explain the guidelines
  
• Train team members, answer questions
  
• Distribute tasks among annotators and validators
  
• Monitor deadlines and progress
  
• Perform spot checks
  
• Communicate with the client and deliver results
  

Annotation Accuracy

Annotation accuracy is a critical quality metric. Well-annotated data directly impacts the effectiveness of machine learning models. Even small errors can degrade results.

• Precise object positioning — the contours or bounding boxes must closely follow the shape of the object without excessive background or omissions. If capturing more background is acceptable in the project, such tolerances must be clearly described in the specification.
  

In this example, the person on the right is inaccurately annotated. Red arrows point to critical issues such as large gaps and clipped regions. In most projects, this type of annotation would be considered invalid.

• Correct class assignment — each object must be annotated strictly according to the guidelines.
  

In the example on the right, the object has been assigned an incorrect label. The correct label should be "bicycle" rather than "motorcycle." Such mistakes significantly reduce annotation quality and should be avoided.

• Annotation completeness — all required objects in the image must be annotated, none should be missing.
  

In this example, one person was not annotated. An annotation task must always be complete, with all required objects properly labeled.

• Guideline compliance — each tool (bounding box, polygon, line, etc.) must be used appropriately, as specified in the project documentation. All nuances described in the specification must be taken into account.
  

In this example, the goose indicated by the red arrow is annotated using a segmentation mask, while the others are annotated with bounding boxes. Annotation must be consistent and strictly follow the project specification. If the specification requires the use of bounding boxes only, using any other annotation tools is not allowed.

Conclusion

Data annotation is not just a technical task — it's a foundational step in building modern AI systems. The accuracy of annotation determines how “smart” and reliable a model will be when solving real-world problems.

Annotation requires attention to detail, full understanding of the guidelines, and responsibility for the final result. The annotator transforms raw data into training material; the validator ensures precision; the team lead coordinates everything to run smoothly.

Even the most advanced AI algorithms are useless without high-quality annotated data. That’s why annotation isn’t a side job — it’s the core of a successful AI project.