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

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

Open Google Colab for live demo using MobileViT v2 classify disease

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