This project focuses on implementing emotion detection using Convolutional Neural Networks (CNN) and a hybrid approach combining K-Nearest Neighbors (KNN) and CNN. It aims to accurately classify emotions from input data, exploring the strengths of both traditional and deep learning methods.
Emotion detection is a critical application in various fields, from human-computer interaction to sentiment analysis. This project leverages both CNN and a hybrid KNN+CNN approach to achieve accurate emotion classification. By combining traditional KNN with deep learning, we explore the potential for improved performance in real-world scenarios.
- Emotion classification using Convolutional Neural Networks (CNN).
- Hybrid approach combining K-Nearest Neighbors (KNN) and CNN for emotion detection.
- Data preprocessing and augmentation techniques.
- Model training, evaluation, and result analysis.
- Visualizations of accuracy and loss trends.
-Prepare your emotion dataset or use a sample dataset.(I ue FER-2013 dataset from Karrgel) -Preprocess the data and augment it if necessary. -Train the CNN model using cnn_model.py. -Implement the hybrid KNN+CNN approach using hybrid_knn_cnn.py. -Evaluate the models and analyze the results. -Visualize accuracy and loss trends using suitable libraries.
The CNN model achieved an accuracy of [insert CNN accuracy] on the test dataset. The hybrid KNN+CNN model demonstrated an improved accuracy of [insert hybrid model accuracy] due to the integration of traditional KNN's instance-based learning with CNN's feature extraction capabilities.
##Contributing Contributions are welcome! If you have ideas for improvements or find any issues, feel free to open a pull request or create an issue.