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

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

First position in Gran Canary Datathon 2021

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.

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)

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.

Exploring the Application of Attention Mechanisms in Conjunction with Baseline Models on the COVID-19-CT Dataset

Collecting fish image data, after training classifiers grad-cam is applied for the prediction interpretation

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.