Pytorch implementation of convolutional neural network visualization techniques

PyTorch implementation of Grad-CAM

[ICCV 2017] Torch code for Grad-CAM

An implementation of Grad-CAM with keras

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

tensorflow implementation of Grad-CAM (CNN visualization)

Implementation of Grad CAM in tensorflow

A generalized gradient-based CNN visualization technique

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

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...

[5 FPS - 150 FPS] Learning Deep Features for One-Class Classification (AnomalyDetection). Corresponds RaspberryPi3. Convert to Tensorflow, ONNX, Caffe, PyTorch. Implementation by Python + OpenVINO/Tensorflow Lite.

🌈 📷 Gradient-weighted Class Activation Mapping (Grad-CAM) Demo

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM)

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

Neural network visualization toolkit for tf.keras

In this part, i've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)

Faster and more precisely than Grad-CAM

Chainer implementation of Grad-CAM

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

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

Implementation of Grad-CAM for text.

Repository containing scripts to train and test a neural network whose goal is to detect presence of COVID-19

Grad-CAM (Gradient-weighted Class Activation Mapping)

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++.

Visualizing the Multi-scale dense network with Grad-CAM

Implementation of grad-cam for GoogLenet and ResNet architechtures

Explainability of Brain Tumour Segmentation Models

Class activate map

A customizable lightweight Grad-CAM implementation