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)

Attention-based Dropout Layer for Weakly Supervised Object Localization (CVPR 2019 Oral)

Time Series package for fastai v2

Wanna know what your model sees? Here's a package for applying EigenCAM on the new YOLO V8 model

The official code of Relevance-CAM

Class Activation Map (CAM) Visualizations in PyTorch.

Attention \ Saliency maps and features visualization for deep learning models in pytorch

PyTorch implementation of "Learning Deep Features for Discriminative Localization"

Code for the paper : "Weakly supervised segmentation with cross-modality equivariant constraints", available at https://arxiv.org/pdf/2104.02488.pdf

[SIGMOD 2022] Python code for "Dimension-wise Class Activation Map for Multivariate Time Series Classification"

High Resolution Class Activation Mapping for Discriminative Feature Localization

Attribution methods that explain image classification models, implemented in PyTorch, and support batch input and GPU.

Implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation (Fong, et. al., 2018)

Building models that can predict whether an area is at risk of a wildfire or not on satellite images.

An awesome list of papers and tools about the class activation map (CAM) technology.

Computer vision visualization such as Grad-CAM, etc.

An application of CAM (Class Activation Maps) of CNNs. Localizes the food.

Detection and localization of Asian hornets with a CNN using PyTorch

Code for our paper "Learning Visual Explanations for DCNN-Based Image Classifiers Using an Attention Mechanism", by I. Gkartzonika, N. Gkalelis, V. Mezaris, presented and included in the Proceedings of the ECCV 2022 Workshop on Vision with Biased or Scarce Data (VBSD), Oct. 2022.