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 # Class Activation Mapping 
-This repository contains the code and presentation to our (Gerrit Bartels [@GerritBartels](https://github.com/GerritBartels) and Jacob Dudek [@jmdudek](https://github.com/jmdudek)) topic in Deep Neural Network Analysis. We have implemented CAM, Grad-CAM, Grad-CAM++ and the guided version of the latter two and apply them on various CNNs trained on ImageNet. 
+
+This repository contains the code and presentation for our ([@GerritBartels](https://github.com/GerritBartels) and [@jmdudek](https://github.com/jmdudek)) topic in Deep Neural Network Analysis. We have implemented Class Activation Mapping (CAM), Grad-CAM, Grad-CAM++, and their guided versions. These techniques are applied to various Convolutional Neural Networks (CNNs) trained on the ImageNet dataset.
+
+## Table of Contents
+- [Repository Usage](#repository-usage)
+- [Overview](#overview)
+- [Examples](#examples)
+    - [CAM](#cam)
+    - [Counterfactual Explanation using Grad-CAM](#counterfactual-explanation-using-grad-cam)
+    - [Guided Grad-CAM](#guided-grad-cam)
+- [References](#references)
+
+## Repository Usage
+
+1. Clone the repository:
+    ```shell
+    $ git clone https://github.com/GerritBartels/class-activation-mapping.git
+    ```
+
+2. Navigate to the project directory:
+    ```shell
+    $ cd class-activation-mapping
+    ```
+
+3. Set up a virtual environment with Python 3.11.* and install the required packages from the `requirements.txt` file:
+    ```shell
+    $ python3 -m venv venv
+    $ source venv/bin/activate
+    $ pip install -r requirements.txt
+    ```
+    or with conda:
+    ```shell
+    $ conda create --name cam python=3.11
+    $ conda activate cam
+    $ pip install -r requirements.txt
+    ```
+
+4. Run the Jupyter Notebook `cam.ipynb` to try out the CAM, Grad-CAM and Grad-CAM++ visualizations, as well as the guided variants for the latter two.
+
+## Overview
+
+Class Activation Mapping (CAM) is a technique used to visualize the regions of an input image that contribute the most to the prediction made by a CNN. Grad-CAM and Grad-CAM++ are extensions of the original CAM mathod that are more versatile and capable. The guided versions of Grad-CAM and Grad-CAM++ further enhance the interpretability of the visualizations by combining the class descriptive power of both methods with the high resolution from Guided Backpropagation.
+
+An overview over all three CAM methods can be seen in the following image:
+
+![CAM Visualization](/images/cam_gc_gc++.png)
+
+## Examples
+
+### CAM
+
+![CAM Visualization](/images/cam_elephant.png)
+
+
+### Counterfactual Explanation using Grad-CAM
+
+![Counterfactual Explanation](/images/counterfactual_explanation.png)
+
+
+### Guided Grad-CAM
+
+![Guided Grad-CAM](/images/guided_gc.png)
+
+
+## References
+[1] Zhou, Bolei, et al. "Learning deep features for discriminative localization." *Proceedings of the IEEE conference on computer vision and pattern recognition.* 2016. https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhou_Learning_Deep_Features_CVPR_2016_paper.pdf
+
+[2] Selvaraju, Ramprasaath R., et al. "Grad-cam: Visual explanations from deep networks via gradient-based localization." *Proceedings of the IEEE international conference on computer vision.* 2017. https://arxiv.org/pdf/1610.02391.pdf
+
+[3]  Chattopadhyay, Aditya, et al. "Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks." *Proceedings of the IEEE Winter Conf. on Applications of Computer Vision.* 2019. https://arxiv.org/pdf/1710.11063.pdf
+
+[4] All other images are from: https://unsplash.com/
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