alihassanml/Yolo11-Face-Emotion-Detection
This project implements a face emotion detection system using YOLOv11, trained on a custom dataset to classify emotions into five distinct classes. The model utilizes the Ultralytics YOLO framework for real-time inference.
This project helps you instantly identify emotions on faces captured by a camera. It takes live video feed or images as input and outputs the detected emotions (like happy, sad, or angry) overlaid on each face. This is ideal for researchers studying human behavior, educators, or anyone needing to analyze emotional responses in real-time.
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Use this if you need to automatically detect and classify one of five basic human emotions from faces in live video or image streams.
Not ideal if you require detection of a wider range of emotions beyond the five basic classes or need to analyze emotions from non-face data.
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Jupyter Notebook
License
MIT
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Last pushed
Oct 26, 2024
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