Visualizing image data using Matplotlib in Python

In this section, we explore the power of visualization tools and techniques to gain meaningful insights into the characteristics and patterns of image data. Using Python libraries such as Matplotlib and Seaborn, we learn how to create visualizations that showcase image distributions, class imbalances, color distributions, and other essential features. By visualizing the image data, we can uncover hidden patterns, detect anomalies, and make informed decisions for data labeling.

Exploratory Data Analysis (EDA) is an important step in the process of building computer vision models. In EDA, we analyze the image data to understand its characteristics and identify patterns and relationships that can inform our modeling decisions.

Some real-world examples of image data analysis and AI applications are as follows:

• Autonomous vehicles: Image data plays a crucial role in enabling autonomous vehicles to perceive their surroundings...