If you've been exploring the ever-rising boom of AI/ML, it is imperative that you come across data annotation. Data Annotation is essential to teach the computer what's what in the real world and how to identify different things. To easily understand what Data Annotation is, let's break it down from the middle.
Data: The raw information or input that needs to be labeled or annotated. This data can include images, videos, audio files, text, or any other form of information relevant to the AI task.
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Annotation: The process of adding labels, tags, or metadata to the data to provide additional context or meaning. Annotations can be in the form of bounding boxes, segmentation masks, keypoint locations, text labels, sentiment scores, etc.
Now with that in mind, let's take a look at some other terms you'll come across.
Annotator: The human or AI agent responsible for performing the data annotation. Human annotators use their expertise to accurately label the data, while AI agents can automate certain annotation tasks using predefined algorithms.
Annotation Types: Different AI tasks require specific types of annotations. Common annotation types include image classification, object detection, semantic segmentation, named entity recognition, sentiment analysis, etc.
Annotation Guidelines: Clear instructions and guidelines provided to annotators to ensure consistency and quality in the annotation process. These guidelines help in standardizing the labeling criteria.
Data Bias: The presence of skewed or unrepresentative data in the annotated dataset, leading to biased AI models. Avoiding data bias is crucial to ensure fairness and ethical use of AI systems.
Label: The category or class assigned to a data sample during annotation. For example, labeling images of animals with "cat" or "dog."
Bounding Box: A rectangular region drawn around an object of interest in an image or video to indicate its location and boundaries.
Segmentation: The process of dividing an image into distinct regions and assigning each pixel to a specific category or class.
Keypoints: Specific points marked on objects in an image to identify their unique features or characteristics.
Crowdsourcing: Obtaining annotations from a large group of individuals (crowd) through online platforms to speed up the annotation process.
In no way is this the comprehensive list, but it should help you make sense of some basic terminologies in Data Annotation and get started!
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