Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Pytorch implementation of convolutional neural network visualization techniques

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)

PyTorch re-implementation of Grad-CAM (+ vanilla/guided backpropagation, deconvnet, and occlusion sensitivity maps)

pytorch实现Grad-CAM和Grad-CAM++,可以可视化任意分类网络的Class Activation Map (CAM)图,包括自定义的网络;同时也实现了目标检测faster r-cnn和retinanet两个网络的CAM图;欢迎试用、关注并反馈问题...

An implementation of Grad-CAM with keras

Official implementation of Score-CAM in PyTorch

Neural network visualization toolkit for tf.keras

A generalized gradient-based CNN visualization technique

Implementation of Grad CAM in tensorflow

InterpretDL: Interpretation of Deep Learning Models，基于『飞桨』的模型可解释性算法库。

📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

TensorFlow implementations of visualization of convolutional neural networks, such as Grad-Class Activation Mapping and guided back propagation

Visualizations for understanding the regressed wheel steering angle for self driving cars

[ECCV 2018] code for Choose Your Neuron: Incorporating Domain Knowledge Through Neuron Importance

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++.

Official PyTorch implementation for our ICCV 2019 paper - Fooling Network Interpretation in Image Classification

Class activate map

Grad-CAM in TensorFlow, presented in Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization.

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