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)
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Updated
May 23, 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)
GiMeFive: Towards Interpretable Facial Emotion Classification 😄😲😭😡🤢😨 (PyTorch Implementation)
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
On the evaluation of deep learning interpretability methods for medical images under the scope of faithfulness
Deep Learning Breast MRI Segmentation and Classification
CAM, Grad-CAM, Grad-CAM++ and Guided Backpropagation post-hoc explanation methods
COVID-CXNet: Diagnosing COVID-19 in Frontal Chest X-ray Images using Deep Learning. Preprint available on arXiv: https://arxiv.org/abs/2006.13807
Explainable AI for Image Classification
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
Content-Based Radiogrphic Image Retrieval with Deep Learning and Interpretability of Results using Saliency and Grad-CAM
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
This project aims to differentiate among various diseases (multiclass prediction) present in mango leaves. Various machine learning techniques were employed in this project to achieve optimal performance in a model capable of predicting multiple classes.
Research on AutoML and Explainability.
Image classification using deep learning models with activation map visualisation and TensorRT support
AI for COVID-19
Computer vision visualization such as Grad-CAM, etc.
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