Given a masked face image our task is to create an approximate description of the facial features (or an unmasked face) using generative deep learning models.
PyTorch Implementation in models.py
Masked | Generated UnMasked Images |
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To test a running model of the program https://unmasker.herokuapp.com/
Clone the repository and cd into it
git clone https://github.com/dl-bl4ck/unmasker.git
cd unmasker
Make sure you have a Python3+ version. Run the following command -
pip install -r requirements.txt
Download the dataset from here and unzip the downloaded dataset. This will create a dataset/
in the root folder of the repository.
python3 train.py [--data_dir (str)] [--wandb (str)] [--max_train (int)] [--max_test (int)] [--init_model_cn (str)] [--steps_1 (int)] [--snaperiod_1 (int)] [--num_test_completions (int)] [--alpha (float)] [--batch_size (int)] [--learning_rate (float)]
Options :
--data_dir Path of directory of data/ folder
--wandb Name of the [wandb](https://wandb.ai/) project.
--max_train Maximum number of training images to load to use for testing.
--max_test Maximum number of testing images to load use for evaluation.
--init_model_cn Path of the model with which you want to initialise the model before training.
--steps_1 No of epochs you want to train your generative model.
--snaperiod_1 Number of batches after you want to print the loss for one epoch.
--num_test_completions Number of test images to use for evaluation while training
--alpha Regularisation Constant
--batch_size Batch size to be used while training
--learning_rate Learning Rate to be used while training
Download the trained model from here
To finally test your trained model run the following command
python3 predict.py [--model (str)] [--input_img (str)] [--output_img (int)]
Options :
--model Path of trained model
--input_img Path of input masked image
--output_img Path of output un-masked image
Copyright (c) 2021 Pragyan Mehrotra, Vrinda Narayan and Paras Mehan
For license information, see LICENSE or http://mit-license.org
Done by Pragyan Mehrotra, Vrinda Narayan and Paras Mehan