This repository contains a Jupyter Notebook that demonstrates the process of training an emotion recognition model using the ALEXNET architecture and the Adam optimizer. The model is trained on the Emotic dataset, which is a collection of images annotated with rich emotion labels.
- Introduction
- Dataset Preprocessing
- Model Architecture
- Training
- Evaluation
- Co-Occurrence Matrix
- Image Prediction
This project focuses on building an emotion recognition model using ALEXNET architecture and the Adam optimizer. Emotion recognition is a valuable application in fields like user experience analysis, mental health support, and more.
The Emotic dataset is preprocessed and split into training, validation, and test sets. Data augmentation techniques are applied to improve model performance.
The ALEXNET architecture is defined in the notebook. It comprises several convolutional and fully connected layers, designed to extract meaningful features from images.
The model is trained using the Adam optimizer with a set number of epochs. The training process is detailed in the notebook.
The trained model is evaluated on the validation set. Confusion matrices and classification reports are generated to assess its performance.
A co-occurrence matrix is created to analyze the relationships between predicted and true emotions. It provides insights into the model's performance across different emotion categories.
The trained model is used to predict emotions in sample images. The notebook showcases the predictions along with confidence scores.
Feel free to explore the notebook for a comprehensive understanding of the entire process.
- ALEXNET architecture: Original Paper
- Emotic Dataset: Dataset Link
MIT License
Copyright (c) 2023 Karrtik Baheti
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Welcome to the Emotion Recognition Model repository! This project showcases a state-of-the-art deep learning model trained on the Emotic dataset, a comprehensive collection of images and videos annotated with rich emotion labels.
Key Features:
๐ง Trained on Emotic Dataset: Our model has been meticulously trained on the Emotic dataset, which contains diverse real-world images and videos, enabling it to understand a wide range of emotions and expressions.
๐ Multi-Modal Emotion Recognition: We have extended the model's capabilities to perform multimodal emotion recognition, allowing it to handle both images and video frames seamlessly and accurately.
โก Easy Integration: The model comes with user-friendly APIs and examples that showcase how to integrate it into your projects effortlessly, whether you're working on a web application, mobile app, or research project.
๐ Fine-Tuned Performance: Our model has been fine-tuned for high accuracy, achieving competitive results on benchmark emotion recognition tasks. Detailed performance metrics are available in the repository.
๐ Comprehensive Documentation: We believe in making AI accessible. That's why we provide extensive documentation, guiding you through model usage, integration, and even the process of retraining on specific domains if needed.
๐ก Endless Possibilities: Emotion recognition has applications across industries, from entertainment and marketing to mental health support. Explore the potential of our model and contribute to its enhancement.
Get Involved: Want to contribute to the future of emotion recognition? We welcome contributions, bug reports, and feature requests. Join us in expanding the capabilities of this model and making emotions more understandable for machines.
Note: Remember to customize the description according to the actual features and capabilities of your trained model and the Emotic dataset. This is just a template to give you an idea; feel free to personalize it to match your project's unique attributes.