GradCam Project

This project implements Grad-CAM (Gradient-weighted Class Activation Mapping) for visualizing and understanding convolutional neural networks.

Overview

Grad-CAM is a technique that provides visual explanations for predictions made by deep learning models. It highlights the important regions in an input image that influence the model's decisions.

Contents

• COPY_OF_GRAD_CAM_TRAINING_TUTORIAL.ipynb: Jupyter Notebook containing the implementation and training tutorial.
• data.csv: Dataset with cool dog images.

Requirements

• Python 3.x
• Necessary libraries as specified in the notebook (e.g., TensorFlow or PyTorch, NumPy, Matplotlib)

Usage

1. Clone the repository:

   git clone https://github.com/stuyai/GradCam.git

2. Navigate to the project directory:

   cd GradCam

3. Install required dependencies:

   pip install -r requirements.txt

4. Open the Jupyter Notebook:

   jupyter notebook COPY_OF_GRAD_CAM_TRAINING_TUTORIAL.ipynb

5. Follow the instructions in the notebook to train the model and generate Grad-CAM visualizations.

License

This project is licensed under the MIT License.

Acknowledgments

• Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
• The blog post on the slides