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
Jun 6, 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)
Attention-based Dropout Layer for Weakly Supervised Object Localization (CVPR 2019 Oral)
Wanna know what your model sees? Here's a package for applying EigenCAM on the new YOLO V8 model
Time Series package for fastai v2
The official code of Relevance-CAM
Class Activation Map (CAM) Visualizations 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
High Resolution Class Activation Mapping for Discriminative Feature Localization
An application of CAM (Class Activation Maps) of CNNs. Localizes the food.
Attention \ Saliency maps and features visualization for deep learning models in pytorch
Building models that can predict whether an area is at risk of a wildfire or not on satellite images.
Attribution methods that explain image classification models, implemented in PyTorch, and support batch input and GPU.
An awesome list of papers and tools about the class activation map (CAM) technology.
Implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation (Fong, et. al., 2018)
[SIGMOD 2022] Python code for "Dimension-wise Class Activation Map for Multivariate Time Series Classification"
Detection and localization of Asian hornets with a CNN using PyTorch
Generate class activation map for face images
Class Activation Map | Stanford Cars | PyTorch
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