aanyachauhan9/dog_emotion_classification

Dog Emotion Recognition Using Transfer Learning

This project aims to recognize and classify dog emotions using transfer learning techniques. We leverage pre-trained models, particularly VGG16, to build an effective classifier for various dog emotions.

Table of Contents

• Introduction
• Dataset
• Model Architecture
• Transfer Learning Approach
• Training and Evaluation
• Results
• Conclusion
• Future Work
• Installation
• Usage
• Acknowledgements

Introduction

Dog emotions are vital for understanding their behavior and well-being. This project explores the application of transfer learning to recognize emotions in dogs from images.

Dataset

The dataset used in this project consists of labeled images of dogs exhibiting different emotions.

Model Architecture

We utilized the VGG16 model, pre-trained on ImageNet, as the base model. A custom classification head was added on top to adapt it to our specific task.

Transfer Learning Approach

Transfer learning allows us to leverage the pre-trained weights of VGG16, fine-tuning it on our dog emotion dataset. This approach helps in achieving better performance with limited data.

Training and Evaluation

The model was trained using:

• Optimizer: Adam
• Loss Function: Categorical Crossentropy
• Metrics: Accuracy

We split the dataset into training, validation, and test sets and monitored performance using accuracy and loss metrics.

Conclusion

The project successfully demonstrates that transfer learning can be applied to classify dog emotions. With further improvements, this approach can be extended to more complex emotion recognition tasks.

Future Work

• Collecting more diverse and extensive datasets.
• Experimenting with other pre-trained models.
• Implementing real-time emotion recognition in videos.

Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/dog-emotion-recognition.git
   cd dog-emotion-recognition
   

2. Install required dependencies:

   pip install -r requirements.txt
   
   Download the dataset and place it in the data directory.
   

3. Usage

   To train the model, run:

   python train.py

   To evaluate the model, run:

   python evaluate.py

Acknowledgements

Thanks to the creators of the dataset. This project was inspired by various works on transfer learning and emotion recognition.