Data labeling: why it is important to manage it efficiently

In today’s technically advanced world, Computer Vision has enabled a lot of ways to automate processes. With different machine learning models, understanding and classifying data has become an easy task.?What human efficiency is incapable of, computer vision can do it very easily. It might take months to label 1000 images but your machine can do it in a much easier way-basically they annotate data. You must have come across the term data labeling.

Have you wondered why managing or labeling data plays an important role while applying computer vision technology to your use case application?

Since the quality of the input data directly affects the performance of the model, data labeling is an important stage in the training of Machine Learning algorithms. Indeed, the most effective technique to enhance an algorithm is to increase the quality and quantity of training data.

What is data labeling?

Data labeling in machine learning is the procedure of classifying unlabeled data (such as photos, text files, video, etc.) and putting one or more insightful labels to give the data perspective so that a machine-learning model may learn from it. Labels might say, for instance, if a photograph shows a bird or an automobile, which words were spoken in a voice recording, or whether a tumor is visible on an x-ray. For several use cases, such as natural language processing (NLP), computer vision, as well as speech recognition, data labeling is necessary.

Types of data labeling

There are types of data labeling. Here are some instances of the most typical types since every data form has a distinct labeling process:

1. Text annotation

The technique of categorizing phrases or paragraphs in a document according to the topic is known as text annotation. This material can be anything, from customer reviews to product feedback on e-commerce sites, from social network mention to email messages. Text annotation offers several opportunities to extract relevant information from texts because they clearly express intents. Because machines don't understand concepts and feelings like humor, sarcasm, rage, and other abstract concepts, text annotation is a complicated process with many steps.

2. Image annotation

Annotating an image makes sure that a machine will recognize the annotated region as a different object. These models receive captions, IDs, and keywords as attributes when they are trained. The algorithms then recognize and comprehend these factors and develop their internal knowledge. To be employed in a variety of AI-based technologies like face detection, computer vision, robotics vision, and autonomous vehicles, among others, it typically includes the usage of bounding boxes or semantic segmentation.

3. Video annotation

Similar to image annotation, video annotation makes use of tools like bounding boxes to identify motion frame-by-frame. For computer vision algorithms that carry out object location and tracking, the information gathered through video annotation is crucial. The systems can easily incorporate ideas like location, image noise, and detection and tracking thanks to video annotation.

4. Audio Annotation

All types of sounds, including speech, animal noises (growls, whistling, or chirps), and construction noises (breaking glass, scanners, or alarms), are transformed into structured formats during audio processing so they can be employed in machine learning. It is frequently necessary to manually convert audio files into text before processing them. The audio can then be tagged and categorized to reveal more details about it. Your training dataset is this audio that has been categorized.

5. Key-point Annotation

With key-point annotation, dots are added to the image and connected by edges. You receive the x and y-axis coordinates of important locations that are labeled in a specific order at the output. The method finds little objects and forms variations that share the same composition (e.g., facial expressions and features, parts of the human body and poses).

Read our detailed blog on the topic to know different approaches, best practices, and method to increase efficiency here.