Data Annotation and its Benefits Defined

Data annotation refers to tagging/labeling data from different formats e.g. text, video, images, etc. To build a practical AI/ML application, accurately labeled data is needed so that the application can learn and understand the patterns it’s designed for.

The value of having precisely annotated data to train a computer vision-based ML model cannot be underrated. Using a wide array of data annotation methods and tools, accurate data sets for practical computer vision training are created. By using tags or added metadata, data is made more informative to AI/ML models. 

Types of Data Annotation

Depending on an AI/ML model’s algorithm (which varies depending on the sector and use case) data annotation techniques employ a variety of tools, approaches, and data labeling expertise.

Most of the training data is mainly available in text, image, and video. These different data types are labeled using different annotation techniques. In this blog, we are going to cover the different types of annotation suitable for training AI/ML models.

Bounding Box Annotation

Sometimes referred to as 2D and 3D bounding boxes, it refers to drawing rectangular lines on an image thus making it visible to a Machine Learning model. This method is perfect for training models whose use cases are in retail, agriculture, and fashion, just to name a few.

Semantic Segmentation

Also referred to as image segmentation, it involves clustering areas of an image together as belonging to the same class. A form of pixel-level prediction since every pixel in an image is grouped differently depending on the category. Semantic segmentation is mainly employed in the automotive industry and agriculture.

Keypoint/Landmark Annotation

For landmark/keypoint annotation, one must label significant points at specific points. Keypoint annotation is mainly used for gesture and facial recognition. To build a logical image recognition AI model, accurately annotated points are crucial.

Polygonal Annotation

Polygonal annotation allows you to capture more lines and angles. Polygonal annotation is basically plotting/drawing more lines to capture more angles. This annotation technique is mainly used in drone and satellite imaging technology.

LIDAR Annotation

Lidar Annotation works by assigning anatomical or structural points of interest which leads to error-free data sets that ascertain the form of different-sized objects. This enables Artificial Intelligence and Machine Learning algorithms better recognize their surroundings when deployed.

There exists a wide range of practical use cases for data annotation for computer vision. Below is a mention of a few sectors where data annotation for computer vision is strongly put to use.

1. Autonomous Automobiles
2. Autonomous Flying
3. Sports and Gaming
4. Retail
5. Agriculture
6. Livestock Management
7. Forest Management
8. Media
9. Security and Surveillance
10. Robotics

Why Impact Outsourcing?

Impact Outsourcing offers annotation services be it Lidar, Semantic Segmentation, Keypoint, etc. With our professionally managed workforce headed by experienced project managers, we are well-positioned to deliver quality datasets for your AI/ML project.