This repository contains a deep learning project that focuses on emotion classification using Convolutional Neural Networks (CNNs). The goal of this project is to separate images into two distinct categories: "Sad" and "Happy" emotions.
Technologies Used:
TensorFlow:
The popular deep learning framework is utilized for building and training the CNN model.
Keras: A high-level neural networks API, running on top of TensorFlow, which simplifies the model construction process.
Python: The programming language used for implementing the CNN model and handling data processing tasks.
YouTube Channel: A video tutorial explaining the project is available on my YouTube channel.
Dataset:
The emotion classification model is trained and evaluated on a custom dataset consisting of labeled images. The dataset is divided into two classes: "Sad" and "Happy." Each image is appropriately labeled to ensure accurate training and validation.
Model Architecture:
The CNN model architecture is designed to effectively extract features from the input images. It consists of multiple convolutional layers, followed by pooling layers for downsampling and dropout layers for regularization. The final layers include fully connected layers for classification.
Training and Validation:
The dataset is split into training and validation sets to train and assess the model's performance. The CNN is trained using the training set and fine-tuned with hyperparameter tuning to achieve optimal results. The validation set is used to evaluate the model's accuracy and identify potential overfitting.
Results:
The trained CNN model achieves impressive performance in classifying emotions, demonstrating its ability to distinguish between "Sad" and "Happy" images. Detailed performance metrics, such as accuracy, precision, recall, and F1 score, are reported in the project.
YouTube Tutorial:
A comprehensive video tutorial explaining the entire project, including data preprocessing, model creation, training, and evaluation, is available on my YouTube channel. Viewers can follow along with the step-by-step guide to understand the process better.
https://www.youtube.com/watch?v=LeQj2Dp_tRE&t=1073s
Feel free to explore the project, experiment with different architectures or datasets, and provide feedback or suggestions for further improvements. Happy coding!