Demo GradCAM & Guided GradCAM

On Dogs vs. Cat data

Architecture: ResNet50 & ResNet50 + FC layers

An interactive demo for GradCAM and Guided GradCAM, implemented with Tensorflow 2.x

Prerequisite

bash requirements.txt

You can run on your own resources with the file Visualization.ipynb

Otherwise, you can run on Google Colab easily via this link: https://colab.research.google.com/github/nguyenhoa93/GradCAM_and_GuidedGradCAM_tf2/blob/master/Visualization.ipynb (Don't forget to change your runtim to Python3 and choose GPU as your hardware accelerator.)

Model: There are two trained models, which are
- VanilaResNet50: Keep the same architecture of ResNet50, replace the output layer on ImageNet and re-train with Dog vs. Cat data.
- ResNet50PlusFC: Add 2 fully connected layers between Average Pooling layer and output layer and train on Dog vs. Cat data.
Image: There are some available sample images in assets/samples, if you want to run your own ones, put them in this folder to be displayed on the dropdown list.
Class: This will be the class for GradCAM and Guided GradCAM visualization.

GradCAM paper: https://arxiv.org/abs/1610.02391
GradCAM tutorial by pyimagesearch: https://www.pyimagesearch.com/2020/03/09/grad-cam-visualize-class-activation-maps-with-keras-tensorflow-and-deep-learning/
Keras GradCAM with Tensorflow 1.x backend:
- https://github.com/jacobgil/keras-grad-cam
- https://github.com/eclique/keras-gradcam