xception-net
There are 13 repositories under xception-net topic.
raghavm1/CZ4041--Machine-Learning
The project for NTU's course on Machine Learning (Plant Seedling Classifier), CZ4041
farhan1503001/Image-Forgery-Detection
Comparison between different DL models such as VGGnet,InceptionV3,Resnet for copy move forgery detection
ameencaslam/deepfake-detection-project-v4
Detect Deepfaked Faces Using Multiple Deeplearning Models
hrsht-13/PneumoniaDetection
Using an External dataset to get the pre-trained weights of the NIH dataset and training on the provided dataset to detect the presence of pneumonia.
utkuatasoy/AI-Powered-Deepfake-Detection
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.
avd1729/Xception
Flower image classification using Transfer learning (Xception)
farhan1503001/Breast-Cancer-Classification-From-Histopathological-Images
Improved Deep Learning Model has been used to classify Breast Cancer from Histopathological Tissue Images.
parham1998/CNN_Image_Annotaion
Implementation of some basic Image Annotation methods (using various loss functions & threshold optimization) on Corel-5k dataset with PyTorch library
achraf-oujjir/xception-on-ham10k
In this project, we used a transfer learning approach to build an image classification model for the classification of skin lesion, we trained our model specifically on the ham10000 dataset available on kaggle and we were able to achieve a 93.6% accuracy
Aditya1Jhaveri/Cervical-Cancer-Image-Classification-in-Deep-Learning
This repository hosts the Cervical Cancer Image Classification project, a comprehensive effort aimed at improving the classification accuracy of Squamous Cell Carcinoma (SCC) through advanced deep learning models and ensemble techniques. The project utilizes the Herlev dataset.
Afrid1045/Brain-Tumor-Severity-Prediction-using-Multi-Modal-Squeeze-and-Excitation-Network
The project focuses on classifying brain tumors using the Multi-Modal Squeeze and Excitation Network.
Pranav-Nagpure/Dog-Breed-Prediction-NB
IPython Notebook to build the model for Dog Breed Prediction
Venn1998/GlaucomaDetection
Development and analysis of various deep NN models to detect glaucoma cases from fundus images. The performance of the best model was evaluated with cross-validation. Mean F1-score: 0.95975, with a standard deviation of 0.02274.