grad-cam-visualization

Generation of 3D/ 2D attention maps for both classification and segmentation

📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

Going deeper into Deep CNNs through visualization methods: Saliency maps, optimize a random input image and deep dreaming with Keras

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

Deep Learning Breast MRI Segmentation and Classification

Deep Learning for SAR Ship classification: Focus on Unbalanced Datasets and Inter-Dataset Generalization

We will build and train a Deep Convolutional Neural Network (CNN) with Residual Blocks to detect the type of scenery in an image. In addition, we will also use a technique known as Gradient-Weighted Class Activation Mapping (Grad-CAM) to visualize the regions of the inputs and help us explain how our CNN models think and make decision.

Curso de Redes Neuronales Convolucionales con PyTorch

Deep learning pipeline for classification of Cataract, Diabetic Retinopathy, Glaucoma and Normal using fundus images

First position in Gran Canary Datathon 2021

Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet

Heat Map 🔥 Generation codes for using PyTorch and CAM Localization Algorithm.

PyTorch MobileNetV2 Stanford Cars Dataset Classification (0.85 Accuracy)

AI-powered skin disease classification system using a fine-tuned ResNet CNN with Grad-CAM explainability. Built with PyTorch and Streamlit to predict 8 skin conditions and visualize model attention for transparent, confidence-aware predictions.

This study tries to compare the detection of lung diseases using xray scans from three different datasets using three different neural network architectures using Pytorch and perform an ablation study by changing learning rates. The dimensional understanding is visualised using t-SNE and Grad-CAM for visualisation of diseases in x-ray scans.

A deep learning-powered medical diagnosis system that detects Pneumonia from chest X-rays and Brain Tumors from MRI scans using two trained CNN models. Includes a FastAPI backend and Streamlit UI for real-time predictions. Built for practical deployment with explainability (Grad-CAM) and modular architecture.

Intracerebral Hemorrhage Detection on Computed Tomography Images Using a Residual Neural Network

Repository of the course project of CMU 16-824 Visual Learning and Recognition

Generate explanations for the ResNet50 classification using Grad-CAM and LIME (XAI Method)

A complete, straightforward digit classification project built with PyTorch, featuring CNN-based training, evaluation metrics, confusion matrix visualization, and XAI using Grad-CAM.