5 Questions to Ask Before Getting Started with Data Annotation
What is data annotation? Why is it essential? What are common label types for models? How do AI-powered data annotation tools help with the computer vision labeling process? What’s the best way to get started?
What is Data Annotation for Computer Vision?
Data annotation, also called data labeling, is the process of adding labels or other information to a collection of data. A labeled dataset is often needed to train machine learning models. Most computer vision models need many annotated images or videos to learn patterns.
Data annotation can be quite a time-consuming process, especially when done manually. A rise in AI-powered labeling tools and an increasing quantity of data are revolutionizing how data annotation is being approached by providing features such as auto labeling, smart polygon selection, and tracking labeled objects from frame to frame.
What Are Common Label Types of Computer Vision Data Annotation?
Currently, most computer vision applications use a form of supervised machine learning, which means we need to label datasets to train the applications.
Choosing the correct label type for an application depends on what the computer vision model needs to learn. Below are four common types of computer vision models and annotations.
Object detection models can learn to detect objects and estimate their location within a frame. These models are often used for counting and tracking objects in images or videos.
Object detection models usually require rectangle labels, also known as bounding boxes, to annotate objects inside the frames.
Labeling jets with a bounding box for object detection
Instance segmentation models learn to detect objects, identify each object’s location in the frame, and estimate the exact pixels of each object. These models can be useful if you need more precise pixel estimates for object interactions and higher accuracy.
These types of models require polygon labels to annotate the distinct pixels belonging to an object. Labeling polygons manually is known to be tedious and time-consuming, which is where AI-powered tools can shine.
Classification models learn to predict if a defined object appears within an image or video but do not estimate its location or how many instances appear.
These models use classification or multi-classification labels that are applied to the entire image signaling if the frame contains a specific class.
Body pose estimation, hand gestures recognition, and face keypoint models are typical examples of keypoint estimation models. These models learn from labeled points of specific features, such as the joints of a body.
Why is Accurate Data Annotation Important?
When creating robust datasets for computer vision, many factors are essential to consider, such as data bias, source quality, project scope, and sample quantity. We’ll cover these topics in future posts. But for now, let’s focus on some common data annotation problems that may affect computer vision projects.
Drawing labels incorrectly around objects:
When labeling bounding boxes and polygons, it’s important to draw the lines just outside the object, but not too far outside and not inside the outline.
Example of improper and proper labels for object detection
Not labeling all object instances:
If an instance of an object type is not labeled while performing data annotation, the machine learning model may not learn the correct patterns needed for detection.
An example of skipping an instance of an object while performing data annotation
Unclear or no labeling instructions:
It’s essential to have precise data annotation instructions for every project, especially when handing off the task to a separate data labeling team or service. Embedding good and bad examples of labeled objects can help ensure the data annotation is performed correctly.
- Data Annotation for computer vision is the process of adding labels to images or video frames.
- Choosing the right type of label depends on the type of computer vision model you want to build.
- Without the use of an AI-powered data annotation tool, data annotation can be time-consuming and tedious.