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Deep Learning Object Recognition Project

Overview

This project explores various deep learning architectures for object recognition. It's structured into three main approaches: Feedforward Neural Networks (ffNN), Convolutional Neural Networks (CNN), and Transfer Learning (tLearning). Each method is designed to evaluate different strategies and improve the accuracy of object recognition.

Contents

  • Deep_Learning___Report.pdf: Contains the comprehensive report detailing the methodologies, experiments, results, and analysis of the object recognition project.
  • DeepLearningAssignment.pdf: Provides the project assignment and objectives that were addressed throughout the course of this research.
  • Architectures: Directory containing different model architectures used in the project:
    • ffNN: Implementations and experiments using feedforward neural networks.
    • CNN: Notebooks and resources related to convolutional neural networks.
    • tLearning: Application of transfer learning methods.

Getting Started

To get started with this project, clone this repository and explore the Jupyter notebooks contained within each architecture's folder. The notebooks are self-contained and include comments explaining each step of the process.

Prerequisites

Ensure you have Python installed along with the following libraries:

  • TensorFlow
  • Keras
  • NumPy
  • Matplotlib

You can install the necessary libraries using pip:

pip install tensorflow keras numpy matplotlib Running the Notebooks To run the notebooks, navigate to the specific architecture directory in your terminal and launch Jupyter Notebook:

cd path_to_directory jupyter notebook

Project Structure

DeepLearningProject/ │ ├── Deep_Learning___Report.pdf ├── DeepLearningAssignment.pdf │ └── Architectures/ ├── ffNN/ │ └── ... (Jupyter notebooks and resources for ffNN) ├── CNN/ │ └── ... (Jupyter notebooks and resources for CNN) └── tLearning/ └── ... (Jupyter notebooks and resources for Transfer Learning)

Authors

  • Federico Paschetta
  • Cecilia Peccolo

License

This project is licensed under the MIT License.

Acknowledgments

Special thanks to Universidad Politécnica de Madrid for guidance and resources throughout the project.