LearnCV

When developing computer vision (CV), machine learning (ML), or deep learning (DL) projects, it's important to understand that the real-world images we work with are not always high-definition (HD) and perfect. Images may come with various variations, and part of the process involves classifying these variations to determine if we need to include them in our training data. 1. Occlusion :  This refers to a situation where part of an image is hidden or partially obscured. For example, if an algorithm is trained on perfectly clear images of a person, and it is later given an image where the person is partially covered (e.g., with a hat, glasses, or another object blocking part of the view), the algorithm may struggle to identify or predict accurately. This can lead to reduced performance of the system. Solution : To ensure the robustness of the algorithm, it's essential to include diverse training examples that account for occlusions and other variations that could be present in r...