Overview:
This project focuses on detecting faces and classifying gender using the YOLOv8l model. With a dataset of 1369 images, the system classifies faces into two categories: male and female. The goal is to accurately detect and recognize gender in real-time using computer vision techniques.
Key Features:
1. YOLOv8l for Face Detection:
The system leverages the YOLOv8l model to detect faces in real-time with high accuracy, identifying facial features to distinguish between genders.
2. Gender Classification:
Once faces are detected, the system classifies them into two categories—male and female—based on the facial characteristics identified by the model.
3. Dataset of 1369 Images:
The model was trained using a dataset of 1369 images with two gender classes, ensuring a diverse training set for better accuracy.
4. High Performance:
The model achieved a mean average precision (mAP) of 0.912, with precision of 0.93 and recall of 0.82, reflecting its accuracy in real-world applications.
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