Popular GitHub Repositories for Xception

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

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)

DeepLab v3+ model in PyTorch. Support different backbones.

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

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.

Deep learning based tool for image processing. No need for Programing and GPU.

Lightweight Facial Expression(emotion) Recognition model

Simple Eye Blink Detection with CNN

Generating image captions using Xception Network and Beam Search in Keras - My Bachelor's thesis project

Regression in Convolutional Neural Network applied to Plant Leaf Count

VerifyVision-Pro是一个全面的图像伪造篡改检测解决方案，利用深度学习（deep learning）和计算机视觉技术（cv）精确识别各类图像篡改，包括deepfake、AI生成内容、拼接操作和复制-移动篡改。基于PyTorch实现，集成了从数据处理、模型训练到部署的完整工作流程。

Implementation of state-of-the-art models to do segmentation over our own dataset.

Notebooks of pre trained models using the HAM10000 dataset

A PyTorch implementation of the DeepLab-v3+ model under development. This is basically a subset of a clone of the pytorch-deeplab-xception repo authored by @jfzhang95. This repo is intended for further research on instance-level semantic segmentation.

ImageClassifier-Keras Custom Image Classifier using Custom model and pre-trained models like MobileNet, VGG, ResNet, EfficientNet,..etc with help of Tensorflow and Keras

A pretrained model Xception to classify skin disease with high accuracy which is 99.33%. This code is used in research work which is published in one of the IEEE conferences.

The purpose of this project is to develop an AI-powered system capable of detecting deepfake facial data in biometric systems. By leveraging machine learning, specifically XceptionNet architecture, the project aims to classify facial data as real or fake with high accuracy and reliability.

? A deep learning project for classifying teeth diseases using CNNs, with fine-tuned VGG16 and Xception models. Includes a Streamlit web app for real-time dental image predictions.

Proiect Deep Learning (DL) Anul 3, Semestrul 2, Facultatea de Matematica si Informatica, Universitatea din Bucuresti

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