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

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

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

Making CNNs interpretable.

code for studying OpenAI's CLIP explainability

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

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

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.

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

Saliency Enhancing with Scaling and Sliding

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.

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.

Tensorflow 2.0 implementation of GradCAM

A CT-scan of your CNN

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.

Easy to follow GradCAM visualization - Google collab notebooks where you just have to upload the image and mention the target class to get the feature visualization for models trained on COCO and Imagenet dataset.

A pytorch implementation of GradCAM

CNN architectures Resnet-50 and InceptionV3 have been used to detect whether the CT scan images is covid affected or not and prediction is validated using explainable AI frameworks LIME and GradCAM.