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 28, 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)
Paper Name: Utilizing Convolutional Neural Networks and Gradient-weighted Class Activation Mapping (Grad-CAM) for Dairy Cow Teat Image Classification. Testing the impact of CNN and Grad-CAM in the accuracy of dairy Cows Teat Imaga classification. Dataset and testing software from: https://github.com/YoushanZhang/SCTL
An Explainable Neural Network for Fault Diagnosis With a Frequency Activation Map
Activation maps visualizer for PyTorch
Class Activation Mapping
PyTorch code for "SOLAR: Second-Order Loss and Attention for Image Retrieval". In ECCV 2020
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.
Neural network visualization tool after an optional model compression with parameter pruning: (integrated) gradients, guided/visual backpropagation, activation maps for the cao model on the IndianPines dataset
Inception Modules with Connected Local and Global Features implemented with CAM.
Filter Visualizations, Heatmaps and Salience Maps
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