diff --git a/main.py b/main.py
deleted file mode 100644
index 06ede62..0000000
--- a/main.py
+++ /dev/null
@@ -1,105 +0,0 @@
-#models
-from unet import UNet
-from myloss import *
-import torch
-from torch.autograd import Variable
-from torch import optim
-
-#data manipulation
-import numpy as np
-import pandas as pd
-import PIL
-
-#load files
-import os
-
-#data visualization
-from utils import *
-import matplotlib.pyplot as plt
-
-#quit after interrupt
-import sys
-
-
-
-dir = 'data'
-ids = []
-
-for f in os.listdir(dir + '/train'):
-    id = f[:-4]
-    ids.append([id, 0])
-    ids.append([id, 1])
-
-np.random.shuffle(ids)
-#%%
-
-
-net = UNet(3, 1)
-net.cuda()
-
-def train(net):
-    optimizer = optim.Adam(net.parameters(), lr=1)
-    criterion = DiceLoss()
-
-    epochs = 5
-    for epoch in range(epochs):
-        print('epoch {}/{}...'.format(epoch+1, epochs))
-        l = 0
-
-        for i, c in enumerate(ids):
-            id = c[0]
-            pos = c[1]
-            im = PIL.Image.open(dir + '/train/' + id + '.jpg')
-            im = resize_and_crop(im)
-
-            ma = PIL.Image.open(dir + '/train_masks/' + id + '_mask.gif')
-            ma = resize_and_crop(ma)
-
-            left, right = split_into_squares(np.array(im))
-            left_m, right_m = split_into_squares(np.array(ma))
-
-            if pos == 0:
-                X = left
-                y = left_m
-            else:
-                X = right
-                y = right_m
-
-
-            X = np.transpose(X, axes=[2, 0, 1])
-            X = torch.FloatTensor(X / 255).unsqueeze(0).cuda()
-            y = Variable(torch.ByteTensor(y)).cuda()
-
-            X = Variable(X).cuda()
-
-            optimizer.zero_grad()
-
-            y_pred = net(X).squeeze(1)
-
-
-            loss = criterion(y_pred, y.unsqueeze(0).float())
-
-            l += loss.data[0]
-            loss.backward()
-            if i%10 == 0:
-                optimizer.step()
-                print('Stepped')
-
-            print('{0:.4f}%\t\t{1:.6f}'.format(i/len(ids)*100, loss.data[0]))
-
-        l = l / len(ids)
-        print('Loss : {}'.format(l))
-        torch.save(net.state_dict(), 'MODEL_EPOCH{}_LOSS{}.pth'.format(epoch+1, l))
-        print('Saved')
-
-try:
-    net.load_state_dict(torch.load('MODEL_INTERRUPTED.pth'))
-    train(net)
-
-except KeyboardInterrupt:
-    print('Interrupted')
-    torch.save(net.state_dict(), 'MODEL_INTERRUPTED.pth')
-    try:
-        sys.exit(0)
-    except SystemExit:
-        os._exit(0)