📦 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
#计算机科学#Code for the paper : "Weakly supervised segmentation with cross-modality equivariant constraints", available at https://arxiv.org/pdf/2104.02488.pdf
#计算机科学#Deep Learning Breast MRI Segmentation and Classification
First position in Gran Canary Datathon 2021
#计算机科学#Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet
#计算机科学#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 C...
PyTorch MobileNetV2 Stanford Cars Dataset Classification (0.85 Accuracy)
Heat Map 🔥 Generation codes for using PyTorch and CAM Localization Algorithm.
Deep Learning for SAR Ship classification: Focus on Unbalanced Datasets and Inter-Dataset Generalization
#计算机科学#Deep learning pipeline for classification of Cataract, Diabetic Retinopathy, Glaucoma and Normal using fundus images
#计算机科学#Intracerebral Hemorrhage Detection on Computed Tomography Images Using a Residual Neural Network
#计算机科学#Repository of the course project of CMU 16-824 Visual Learning and Recognition
Curso de Redes Neuronales Convolucionales con PyTorch
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 c...
#计算机科学#DEELE-Rad: Deep Learning-based Radiomics
Detection and localization of COVID-19 on chest X-rays
#计算机科学#Develop and train image classification models using advanced deep learning techniques to identify diseases specific to apples.
#计算机科学#Using LIME and Grad-CAM techniques to explain the results achieved by various image transfer learning techniques