Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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Updated
May 28, 2024 - Python
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Deep functional residue identification
Official implementation of Score-CAM in PyTorch
Class-Weighted Convolutional Features for Image Retrieval (BMVC 2017)
TensorFlow implementations of visualization of convolutional neural networks, such as Grad-Class Activation Mapping and guided back propagation
COVID-CXNet: Diagnosing COVID-19 in Frontal Chest X-ray Images using Deep Learning. Preprint available on arXiv: https://arxiv.org/abs/2006.13807
Class Activation Map using Keras
I implemented a detection algorithm with a classification data set that does not have annotation information for the bounding box. Based on resnet50 network, I implemented text detector using class activation mapping method.
Evaluating surgical skills from kinematic data using convolutional neural networks
VGG transfer learning and Class Activation Mapping
This repository introduces different Explainable AI approaches and demonstrates how they can be implemented with PyTorch and torchvision. Used approaches are Class Activation Mappings, LIMA and SHapley Additive exPlanations.
Weakly supervised Classification and Localization of Chest X-ray images
Saliency Enhancing with Scaling and Sliding
This project propose a simple example to expose the implicit attention of Convolutional Neural Networks on the image.
A Deep Learning Humerus Bone Fracture Detection Model which classifies a broken humerus bone X-ray image from a normal X-ray image with no fracture using Back Propagation, Regularization, Convolutional Neural Networks (CNN), Auto-Encoders (AE) and Transfer Learning.
【瑞士军刀般的工具】用最短的代码完成对模型的分析,包含 ImageNet Val、FLOPs、Params、Throuthput、CAM 等
Similarity Differences and Uniqueness Explainable AI method
Code for the paper : "Weakly supervised segmentation with cross-modality equivariant constraints", available at https://arxiv.org/pdf/2104.02488.pdf
DeepInsight3D package to deal with multi-omics or multi-layered data
An awesome list of papers and tools about the class activation map (CAM) technology.
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