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)
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Updated
Oct 25, 2024 - Python
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)
A tool for classifying an image into a disaster type, utilizing Python
Explainable AI for Image Classification
Neural network visualization toolkit for tf.keras
One of the first implementations of Grad-CAM ++ for time series / 1d signal.
One of the firsts dataset level explanability libraries for 1d signal using GRAD-CAM++
Frame-agnostic XAI Library for Computer Vision, for understanding why models behave that way.
Efficient explaining AI algorithms for Keras models
tensorflow.keras implementation of gradcam and gradcam++
A web application that classifies an uploaded image into a disaster type, utilizing Angular
Weakly supervised Classification and Localization of Chest X-ray images
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Analysis of visualization methods for relevant areas within images for a trained CNN. Used the GTSRB dataset as well as Activation Maximization, Saliency Map, GradCam, and Gradcam++ methods.
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++.
Keras implementation of GradCam & GradCam++ to Dogs vs. Cats classification model
GradCAM++ and GradCAM for Fastai_v1.0
A Simple pytorch implementation of GradCAM and GradCAM++
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