grad-cam-visualization

📦 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

First position in Gran Canary Datathon 2021

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

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.

PyTorch MobileNetV2 Stanford Cars Dataset Classification (0.85 Accuracy)

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

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

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

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)

rad-Cam provides us with a way to look into what particular parts of the image influenced the whole model’s decision for a specifically assigned label. It is particularly useful in analyzing wrongly classified samples.

DEELE-Rad: Deep Learning-based Radiomics

Detection and localization of COVID-19 on chest X-rays

Develop and train image classification models using advanced deep learning techniques to identify diseases specific to apples.

Using LIME and Grad-CAM techniques to explain the results achieved by various image transfer learning techniques

Gradient Frequency Attention: Tell Neural Networks where speaker information is.