A PyTorch implementation of Grad-CAM based on ICCV 2017 paper "Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization"

Pytorch implementation of gradCAM, guidedBackProp, smoothGrad

Tensorflow 2.0 implementation of GradCAM

GradCAM algorithm implementation for YOLOv3

A Keras based, DeepNet Age Classifier

Gcam is an easy to use Pytorch library that makes model predictions more interpretable for humans. It allows the generation of attention maps with multiple methods like Guided Backpropagation, Grad-Cam, Guided Grad-Cam and Grad-Cam++.

Implementation of a Grad-CAM analysis in TensorFlow 1 for frozen graph models.

Model-agnostic Class Activation Map Generator

Performer vs. Graph Attention Network on ShapeNet with GradCAM explanations.

Grad-CAM Implementation in PyTorch

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

Weakly supervised Classification and Localization of Chest X-ray images

GradCAM Pytorch

Use a neural network to classify an image of an eye to multiple levels of illness of the disease diabetic retinopathy. Used is the dataset ’Indian Diabetic Retinopathy Image Dataset’ (IDRID). To combat the imbalanced class distribution, over-, undersampling, and weighted loss were used. Implemented transfer learning to use more complex models wi…

Implementation of the Grad-CAM algorithm in an easy-to-use class, optimized for transfer learning projects and written using Keras and Tensorflow 2.x

Applying GradCAM method with 3 kinds of CNN-based model for NLP classification task on french dataset.

PCA, t-SNE, Guided backpropagation, Grad-CAM on multi-label image classification with CelebA as dataset; CNN on digit classification with SVHN as dataset

CIFAR10 image recognition using ResNet architecture, Gradcam images