Tensorflow 2.0 implementation of GradCAM
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Updated
Mar 10, 2020 - Python
Tensorflow 2.0 implementation of GradCAM
GradCam Implementation on the VGGNet
Image Classification for Grey Natural Scene Images
Use Deep Learning model to diagnose 14 pathologies on Chest X-Ray and use GradCAM Model Interpretation Method
Grad-CAM Implementation in PyTorch
X-Ray diagnosis and heatmap visualization of disease area using deep learning models like VGG-16 and Grad-CAM with 86% accuracy
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Custom Keras Callbacks for Feature Visualization, Class Activation Map, Grad-CAM
Applying GradCAM method with 3 kinds of CNN-based model for NLP classification task on french dataset.
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.
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.
Making CNNs interpretable.
Example of how to use MATLAB to produce post-hoc explanations (using Grad-CAM and image LIME) for a medical image classification task.
code for studying OpenAI's CLIP explainability
Generating attention maps from resnet50 and densenet using ACDC and EMIDEC dataset
Keras implementation for GradCAM analysis for dual 3D CNN model.
A pytorch implementation of GradCAM
Face mask classification with convolutional neural networks via Tensorflow & Keras 😷
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