Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

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)

Official implementation of Score-CAM in PyTorch

Neural network visualization toolkit for tf.keras

Efficient explaining AI algorithms for Keras models

PyTorch implementation of various CAM (Class Activation Mapping) models using TorchVision pre-trained CNN Classifiers.

This repo shows a MATLAB implementation of score-CAM (Wang et al., CVPR workshop, 2020). This method is the first gradient-free CAM-based visualization method for explaining CNN decision.

Repository containing code to run Score-CAM algorithm available on https://arxiv.org/pdf/1910.01279v1.pdf.

Keras implementation of Augmented Score-CAM

Visualizing 3D ResNet for Medical Image Classification With Score-CAM

Advanced AI Explainability for computer vision.All the non Gradient Methods.

X-Brain: Explainable Automated Recognition of Brain Tumors using Robust Deep Attention CNN