diff --git a/mask_detector/train.py b/mask_detector/train.py
deleted file mode 100644
index ede8224..0000000
--- a/mask_detector/train.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from argparse import ArgumentParser
-
-import torch
-from torch.nn.functional import binary_cross_entropy
-from torch.utils.data import DataLoader, random_split
-from torch.optim import Adam
-from pytorch_lightning import LightningModule, Trainer, seed_everything
-from pytorch_lightning.callbacks import ModelCheckpoint
-from sklearn.metrics import accuracy_score
-
-from model import BasicCNN
-from dataset import MaskDataset
-from utils import train_val_test_split
-
-
-class MaskClassifier(LightningModule):
-    def __init__(self, net, learning_rate=1e-3):
-        super().__init__()
-        self.save_hyperparameters()
-        self.net = net
-
-    def forward(self, x):
-        return self.net(x)
-
-    def training_step(self, batch, batch_idx):
-        x, y = batch
-        out = self.net(x)
-
-        loss = binary_cross_entropy(out, y)
-
-        self.log('train_loss', loss, on_epoch=True)
-
-        return loss
-
-    def validation_step(self, batch, batch_idx):
-        x, y = batch
-        out = self.net(x)
-        loss = binary_cross_entropy(out, y)
-
-        self.log('valid_loss', loss, on_step=True)
-
-    def test_step(self, batch, batch_idx):
-        x, y = batch
-        out = self.net(x)
-        loss = binary_cross_entropy(out, y)
-
-        _, out = torch.max(out, dim=1)
-        val_acc = accuracy_score(out.cpu(), y.cpu())
-        val_acc = torch.tensor(val_acc)
-
-        return {'test_loss': loss, 'test_acc': val_acc}
-
-    def configure_optimizers(self):
-        # self.hparams available because we called self.save_hyperparameters()
-        return Adam(self.parameters(), lr=self.hparams.learning_rate)
-
-
-def cli_main():
-    seed_everything(1234)
-
-    # ------------
-    # data
-    # ------------
-    dataset = MaskDataset(csv_file='data/dataframe/mask_df.csv')
-    ds_train, ds_validate, ds_test = train_val_test_split(
-        dataset, train_ratio=0.8, validate_ratio=0.1, test_ratio=0.1)
-
-    train_loader = DataLoader(ds_train, batch_size=128)
-    val_loader = DataLoader(ds_validate, batch_size=128)
-    test_loader = DataLoader(ds_test, batch_size=128)
-
-    # ------------
-    # model
-    # ------------
-    net = BasicCNN()
-    model = MaskClassifier(net, learning_rate=0.0001)
-
-    # ------------
-    # training
-    # ------------
-    checkpoint_callback = ModelCheckpoint(
-        verbose=True,
-        monitor='test_acc',
-        mode='max'
-    )
-    trainer = Trainer(max_epochs=1, checkpoint_callback=checkpoint_callback)
-    trainer.fit(model, train_loader, val_loader)
-
-    # ------------
-    # testing
-    # ------------
-    result = trainer.test(test_dataloaders=test_loader)
-    print(result)
-
-
-if __name__ == '__main__':
-    cli_main()