This Python project aims to detect emotions in images through supervised learning techniques. It utilizes deep learning models for image classification and emotion recognition. By leveraging pre-trained models and custom datasets, this project facilitates understanding and recognition of emotions portrayed in images.

• Emotion Detection: Detects and classifies emotions such as happiness, sadness, anger, etc., depicted in images.
• Image Classification: Utilizes supervised learning techniques to classify images based on predefined labels.
• Customizable: Easily extendable to incorporate new emotion categories or image classes.
• Pre-trained Models: Utilizes pre-trained deep learning models for efficient and accurate emotion recognition and image classification.

Sample dataset included in this repository consists of images categorized into various emotion classes, such as:

• Happy
• Sad
• Angry
• Surprised
• Neutral

The dataset is organized into separate folders for each emotion class, ensuring ease of use for training and testing purposes.

• Python 3.x
• TensorFlow
• Keras
• OpenCV
• Matplotlib
• NumPy