class-activation-maps

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

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

Deep functional residue identification

Class Activation Map using Keras

COVID-CXNet: Diagnosing COVID-19 in Frontal Chest X-ray Images using Deep Learning. Preprint available on arXiv: https://arxiv.org/abs/2006.13807

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.

Visualizing where the Convolution Network is looking through CAM.

Evaluating surgical skills from kinematic data using convolutional neural networks

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.

【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

VGG transfer learning and Class Activation Mapping

Code for the paper : "Weakly supervised segmentation with cross-modality equivariant constraints", available at https://arxiv.org/pdf/2104.02488.pdf

This project propose a simple example to expose the implicit attention of Convolutional Neural Networks on the image.

Scripts that utilize class activation maps and self-attention layers within Keras models to classify faces from FEI Faces Dataset

Weakly supervised Classification and Localization of Chest X-ray images

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.

Visualizing Class Activation Maps for Convolutional Neural Networks

Tutorial to show how to extract object localization

Saliency Enhancing with Scaling and Sliding

MVA Master School Project - Weakly Supervised Semantic Segmentation

Building a multi-label classifier from scratch and using transfer learning for the PASCAL VOC image dataset.

Repository containing code to run Score-CAM algorithm available on https://arxiv.org/pdf/1910.01279v1.pdf.

Similarity Differences and Uniqueness Explainable AI method

In this project we use a Lightweight-CNN based model to classify instruments from the Freesound audio data set. We make use of Mel-Spectrogram features from the input audio data as the input to the CNN model. To add robustness to the model, we use a novel data augmentation technique based on the Cut-Mix algorithm.

saliency map, adversarial image, (gradient) class activation map

A Class Activation Map is a weighted activation map generated for each image, helping us to identify the region a CNN is looking at while classifying an image.