GradCam-Pytorch-Implementation

This is a PyTorch project that implements Grad-CAM (Gradient-weighted Class Activation Mapping) from scratch. For the sake of simplicity, the project uses pretrained models from ImageNet, including AlexNet and ResNet50.

Installation

To set up the project on your local machine, follow these steps:

1. Clone the repository to your local machine:

   git clone https://github.com/your-username/GradCam-Pytorch-Implementation.git

2. Navigate to the project directory:

   cd GradCam-Pytorch-Implementation

3. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use: venv\Scripts\activate

4. Install the required Python packages using pip:

   pip install -r requirements.txt

Usage

To generate heatmaps for images using Grad-CAM, you can run the project with the following command-line arguments:

python main.py --class_choice <class_name> --img_path <image_path> --model_name <model_name>

Where:

class_choice: Choose the target class name (e.g., "elephant" or "bear"). img_path: Provide the path to the image file you want to visualize. model_name: Choose the model for generating heatmaps ("resnet" or "alexnet"). By default, the script uses the following values:

class_choice: "elephant" img_path: "ILSVRC2012_val_00025941.JPEG" model_name: "resnet" You can adjust these command-line arguments according to your specific use case.

If you like to use different models other than resnet50 and alexnet, make sure that you get the outputs of the last conv layer in the get_features function in utils.py and modify the tensor sizes for each architecture respectively.