Advanced COVID-19 Detection From Lung X-Rays With Deep Learning

A deepfake detection framework using the pre-trained Xception model for deepfake videos.

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

Support PointRend, Fast_SCNN, HRNet, Deeplabv3_plus(xception, resnet, mobilenet), ContextNet, FPENet, DABNet, EdaNet, ENet, Espnetv2, RefineNet, UNet, DANet, HRNet, DFANet, HardNet, LedNet, OCNet, EncNet, DuNet, CGNet, CCNet, BiSeNet, PSPNet, ICNet, FCN, deeplab)

Classification of flowers using Convolutional Neural Network

This project utilizes VGG19, Xception, and a custom CNN to classify retinal diseases from OCT images. The custom CNN achieved 95.47% accuracy, demonstrating AI's potential in improving diagnostic accuracy for ophthalmic disorders. Additionally, a Flask-based web app enables users to upload images for real-time predictions.

A react application with a deep learning model to generate caption for images

🎉A comprehensive project for skin cancer detection using a CNN model.

This repository contains code for comparing and evaluating various CNN classification models on a waste image dataset.

Xception implemented with caffe

A Novel Approach for Alzheimer's Classification Utilizing Ensemble Learning on Pre-trained Neural Networks Fine-tuned on Pre-processed and Augmented Alzheimer's Dataset

Xception model predict dog breeds from dog picture , flask web site

This GitHub repository contains instructions for downloading and utilizing the AI4Food-NutritionDB food image database, as well as different food recognition systems based on Xception and EfficientNetV2 architectures.

A Python-based computer vision and AI system for skin disease recognition and diagnosis. Led end-to-end project pipeline, including data gathering, preprocessing, and training models. Utilized Keras, TensorFlow, OpenCV, and other libraries for image processing and CNN models, showcasing expertise in deep learning and machine learning techniques.

Stanford dogs dataset breed classification with Xception (CNN)

Workshop CDK Template to provision infra for the Deep Visual Search workshop

Transfer Learning models in PyTorch

Notebooks of pre trained models using the HAM10000 dataset

Address the crowd counting problem on the Mall dataset (sparse) by exploring regression-based (Xception) and density-based (CSRNet) approaches.

Deep fake detection using cnn, Xception, Denesenet121, GAN on four different datasets.