📦 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

PyTorch MobileNetV2 Stanford Cars Dataset Classification (0.85 Accuracy)

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

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.

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

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

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

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

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

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

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

Fork of the Mario Kart 64 Gym Environment. Includes training scripts for RL algorithms and Grad-CAM visualization

DEELE-Rad: Deep Learning-based Radiomics

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

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