Based on the mmdetection framework, compute various salience maps for object detection.

Use Deep Learning model to diagnose 14 pathologies on Chest X-Ray and use GradCAM Model Interpretation Method

This repo is special for those who want to start learning computer vision related tasks such as image classification.

Implementation or LRP and Object detection on Brain scans to detect Brain Tumor and Alzhimers

code for studying OpenAI's CLIP explainability

Making CNNs interpretable.

One of the first implementations of Grad-CAM ++ for time series / 1d signal.

这是一个用于计算ViT及其变种模型的GradCAM自动脚本，可以自动处理批量的图像 A GradCAM automatic script to visualize the model result

Custom Keras Callbacks for Feature Visualization, Class Activation Map, Grad-CAM

Used the Functional API to built custom layers and non-sequential model types in TensorFlow, performed object detection, image segmentation, and interpretation of convolutions. Used generative deep learning including Auto Encoding, VAEs, and GANs to create new content.

Saliency Enhancing with Scaling and Sliding

Three different DNN models Xception, In- ceptionV3, and VGG19 were used for the classification of crop disease from the image dataset, and explainable AI XAI was used to evaluate their performance. InceptionV3 was achieved as the best model with the highest accuracy of 97.20% accuracy.

This repository explores the fascinating world of brain tumor classification using cutting-edge Convolutional Neural Networks (CNNs) and eXplainable Artificial Intelligence (XAI) techniques.

Applying GradCAM method with 3 kinds of CNN-based model for NLP classification task on french dataset.

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

Keras implementation for GradCAM analysis for dual 3D CNN model.

Example of how to use MATLAB to produce post-hoc explanations (using Grad-CAM and image LIME) for a medical image classification task.

This repository consists of models of CNN for classifying different types of charts. Moreover, it also includes script of fine-tuned VGG16 for this task. On top of that CradCAM implementation of fine-tuned VGG16.

A CT-scan of your CNN

Tensorflow 2.0 implementation of GradCAM