Overview
This project implements an object detector from scratch using basic machine learning principles to differentiate between facial images of masked and unmasked individuals. It's designed to provide a foundational understanding of object detection without relying on complex, pre-built frameworks.
Features
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Masked vs. Unmasked Detection: Identifies whether a person in an image is wearing a mask or not.
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From Scratch Implementation: Built using fundamental libraries like NumPy, scikit-learn, and OpenCV for core operations, providing insights into the underlying mechanisms of object detection.
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Simple Data Handling: Demonstrates how to load, preprocess, and prepare image data for a custom object detection model.
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Training and Evaluation: Includes the necessary steps to train the model and evaluate its performance.