In many image classification problems, the classes may not be evenly represented in the dataset. You should check for class imbalance by counting the number of images in each class and visualizing the distribution of classes. If there is an imbalance, we augment the minority class data by applying transformations such as rotations, flips, crops, and color variations. This increases the diversity of the minority class without needing to generate entirely new samples.
Identifying patterns and relationships
The goal of EDA is to identify patterns and relationships in the data that can inform your modeling decisions. You can use techniques such as clustering to identify patterns in the data or examine the relationship between different features using scatter plots or correlation matrices. Clustering, in the context of image dataset analysis, is a technique used to group similar images together based on their inherent patterns and characteristics. It’s a data exploration method that aids in understanding the structure of image data by identifying groups or clusters of images that share similar visual traits. Clustering algorithms analyze the visual properties of images, such as pixel values or extracted features, to group images that are visually similar into clusters. Images that share common visual traits are grouped together, forming distinct clusters.
Evaluating the impact of preprocessing
Finally, you should evaluate the impact of preprocessing on your image data. You can compare the performance of your model on preprocessed and unprocessed data to determine the effectiveness of your preprocessing techniques.
In summary, EDA is an important step in the process of building computer vision models. By visualizing the data, checking for outliers and class imbalance, identifying patterns and relationships, and evaluating the impact of preprocessing, you can gain a better understanding of your image data and make informed decisions about your modeling approach.