Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
-
Updated
Aug 5, 2024 - Python
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
Code for IMVIP 2024 paper "Analysing the Impact of Pre-training in ResUNet Architectures for Multiple Sclerosis Lesion Segmentation using EigenGradCAM"
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
This repository is the code basis for the paper titled "Balancing Privacy and Explainability in Federated Learning"
Interactive visualization of classification model's decisions (Using Grad-CAM theorem and Animint2 R package), which can help researches understand mechanisms of computer vision models' decisions
Deep Learning for SAR Ship classification: Focus on Unbalanced Datasets and Inter-Dataset Generalization
Gradient Class Activation Map (with pytorch): Visualize the model's prediction to help understand CNN and ViT models better
Neural network visualization toolkit for tf.keras
Attribution methods that explain image classification models, implemented in PyTorch, and support batch input and GPU.
Repository for uterine endoscopy image classification
Research on AutoML and Explainability.
Image classification using deep learning models with activation map visualisation and TensorRT support
Computer vision visualization such as Grad-CAM, etc.
Repository for the journal article 'SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction'
InterpretDL: Interpretation of Deep Learning Models,基于『飞桨』的模型可解释性算法库。
📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
Gradient-weighted Class Activation Mapping
Deep learning example for Vision
Framework for benchmarking black-box adversarial attacks, modeled with ES.
[a.a. 22/23] G. Antonucci, N. Pagliara
Add a description, image, and links to the grad-cam topic page so that developers can more easily learn about it.
To associate your repository with the grad-cam topic, visit your repo's landing page and select "manage topics."