Detect Deepfaked Faces Using Multiple Deeplearning Models
open-source ai classification deeplearning efficientnet deepfake-detection xception-net swin-transformer cross-attention aimodels
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- Python
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xception-net
Here are 17 public repositories matching this topic...
An Image Analyzer that can analyze and give Real or Fake predictions with confidence scores
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- JavaScript
Comparison between different DL models such as VGGnet,InceptionV3,Resnet for copy move forgery detection
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The code and submissions for the Kaggle Plant Seedling Classification Challenge
machine-learning convolutional-neural-networks kmeans-clustering svm-classifier knn-classification inception-resnet-v2 xception-net
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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.
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Flower image classification using Transfer learning (Xception)
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The project focuses on classifying brain tumors using the Multi-Modal Squeeze and Excitation Network.
python deep-neural-networks deep-learning tensorflow jupyter-notebook classification transfer-learning inceptionv2 multimodal-deep-learning xception-net squeeze-and-excitation-networks
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Improved Deep Learning Model has been used to classify Breast Cancer from Histopathological Tissue Images.
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- Jupyter Notebook
Deep Learning Course Project
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A proof of concept in Detecting Deceptive Deepfake Media Using Generative Adversarial Networks (GAN))
machine-learning artificial-intelligence cybersecurity convolutional-neural-network general-adversarial-network deepfake xception-net
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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.
torch biometrics xception-model biometric-systems deepfakes torchvision faceforensics deepfake-detection biometric-facial-recognition biometric-recognition xception-net celebdf
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Implementation of some basic Image Annotation methods (using various loss functions & threshold optimization) on Corel-5k dataset with PyTorch library
deep-neural-networks image-annotation pytorch convolutional-neural-networks resnext multilabel-classification focal-loss resnet101 pairwise-ranking binary-crossentropy tresnet xception-net asymmetric-loss corel-5k matthews-correlation-coefficient
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- Python
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.
deep-learning image-classification transfer-learning augmentation inception-resnet-v2 ensemble-methods resnet101 densenet121 densenet169 cervical-cancer xception-net resnet50v2 herlev-dataset
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IPython Notebook to build the model for Dog Breed Prediction
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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.
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real time waste classification using artificial neural network
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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
python data-science computer-vision deep-learning jupyter-notebook cnn kaggle transfer-learning skin-lesion-classification ham10000 xception-net
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- Jupyter Notebook
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