gradcam-plus-plus

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM, Finer-CAM, LeGrad, RefineCAM)

A Simple pytorch implementation of GradCAM and GradCAM++

Neural network visualization toolkit for tf.keras

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++.

tensorflow.keras implementation of gradcam and gradcam++

One of the first implementations of Grad-CAM ++ for time series / 1d signal.

Efficient explaining AI algorithms for Keras models

Frame-agnostic XAI Library for Computer Vision, for understanding why models behave that way.

Weakly supervised Classification and Localization of Chest X-ray images

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

A tool for classifying an image into a disaster type, utilizing Python

Weakly-Supervised Image Segmentation Based on Class Activation Map and SLIC Superpixels

GradCAM++ and GradCAM for Fastai_v1.0

My dissertation research project proposing novel Hybrid CNN–Transformer A/B models and scalable guidance modules for investigating shortcut-learning mitigation in brain tumour MRI classification when pixel-level masks are unavailable.

DermAI — Skin Cancer Classification System

A Multimodal Explainable AI agent combining X-rays and blood laboratory data for medical diagnosis using DenseNet-121 and Cross-Attention Fusion.

Implementation of the GradCam++ method and predictions explanation of the ResNet50 model on Skin Cancer MNIST dataset.

One of the firsts dataset level explanability libraries for 1d signal using GRAD-CAM++

Explainability tools for the Mosaico mosquito classification model, using GradCAM, GradCAM++, Guided GradCAM, and saliency maps to validate decisions against expert reasoning.