Introduction to In-House Data Labeling

Data annotation, often called data labeling, is a cornerstone of the AI integration pipeline. It acts as the bridge between the raw data and a functional ML model. The annotators or automated tools add labels or tags to the collected data, helping the model to understand the underlying structure and meaning of the data.

Data Labelling

Mostly, the data are labeled with relevant information to create a labeled training dataset. If you are building a computer vision project, you must deal with the images and videos. Here, the types of data annotation can be used:

1. Image Categorization

2. Semantic Segmentation

3. 2D Bounding Boxes

4. Cuboids

5. Polygonal Annotation

6. Key-point Annotation

7. Object Tracking

If you are dealing with the NLP project, the following are the types of text annotation where the annotators possess the proper linguistic knowledge:

1. Text Classification

2. Optical Character Recognition

3. Named Entity Recognition

4. Intent Analysis

5. Transcription

How Does Data Labeling Work?

Most Machine Learning models use supervised learning, where an algorithm maps inputs to outputs based on a set of labeled data by humans. The model learns from these labeled examples to decipher patterns in that data, emphasizing the importance of investing time and resources in accurate data labeling.

This labeled data is then used to train a machine learning model to find “meaning” in new, relevantly similar data. Throughout this process, machine learning practitioners strive for both quality and quantity. Accurately labeled data, coupled with a larger quantity creates more useful deep learning models, as the resulting machine learning model bases their decisions on all the labeled data.