There are 7 repositories under class-activation-maps topic.
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
Deep functional residue identification
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
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.
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.
Visualizing where the Convolution Network is looking through CAM.
【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
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.
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
An awesome list of papers and tools about the class activation map (CAM) technology.
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.
DeepInsight3D package to deal with multi-omics or multi-layered data
Code and data for our learning-based eXplainable AI (XAI) method TAME: M. Ntrougkas, N. Gkalelis, V. Mezaris, "TAME: Attention Mechanism Based Feature Fusion for Generating Explanation Maps of Convolutional Neural Networks", Proc. IEEE Int. Symposium on Multimedia (ISM), Naples, Italy, Dec. 2022.
Satellite photographs taken by the Sentinel-2 were classified with pre-trained ResNet-50 and VGG16 models. In addition made CAM model.
Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues
Building a multi-label classifier from scratch and using transfer learning for the PASCAL VOC image dataset.
Enhanced CNN model for malaria cell classification, featuring Class Activation Mapping (CAM) as a non-agnstic technique for anomaly localization and LIME (Local Interpretable-agnostic Explanation) for interpretability, ensuring high accuracy and transparent AI diagnostics.
Tutorial to show how to extract object localization
Similarity Differences and Uniqueness Explainable AI method
Visualizing Class Activation Maps for Convolutional Neural Networks
Repository for the paper "Neural Networks for Classification and Unsupervised Segmentation of Visibility Artifacts on Monocular Camera Image"
Lightweight Neural Network for Semantic Segmentation using Knowledge Distillation (Accepted by AICAS 2022)
saliency map, adversarial image, (gradient) class activation map
Repository containing code to run Score-CAM algorithm available on https://arxiv.org/pdf/1910.01279v1.pdf.
DeviceScope: An Interactive App to Detect and Localize Appliance Patterns in Electrical Consumption Time Series