You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
It uses a simple Convolutional Neural Network structure built on Tensorflow.
Dataset
The dataset used for training comes from [Kaggle's Deepfake Detection Challenge](https://www.kaggle.com/robikscube/kaggle-deepfake-detection-introduction). It features fake images and real images. Also includes the model faces images handpicked by the team.
/real_and_fake_face
The /real_and_fake_face folder contains 2 subfolders:
training_real : real facial images. Labeled by '0' in code
training_fake : fake facial images. Labeled by '1' in code
Create, train and save the model. Primary function is to perform 50 epochs for each image size to find the optimal epoch to maximize validation accuracy. 10% of data used for validation
/model_train_and_evaluate.py
Create, train, evaluate and save the model. Primary function is to create models of image sizes with their respective optimal epoch number. 20% of data is used for evaluation.
/model_evaluate
Load any existing model and re-evaluate on the evaluation dataset.
/savedModels
Location to save models. Currently has the final models used for discussion
/savedModels/For EPOCH Selection
Contains models of sizes 32, 64, 128, 256px (length of square) with EPOCHS of 50. Used to analyze the change in validation accuracy over EPOCHS.
Currently, the following EPOCHS maximizes the respective validation accuracy for the corresponding inmage size:
32px: 12 EPOCHS
64px: 8 EPOCHS
128px: 4 EPOCHS
256px: 8 EPOCHS
/savedModels/For IMG_SIZE Selection
Contains models of sizes 32, 64, 128, 256px with their respectively selected EPOCHS
/log
Contains summaries and log files of the saved models
Currently Saved Model's Performance
Evaluated on the evaluation dataset. Performed in /model_train_and_evaluate.py