--- a
+++ b/Unet/model.py
@@ -0,0 +1,73 @@
+import torch
+import torch.nn as nn
+import torchvision.transforms.functional as TF
+
+
+class DoubleConv(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(DoubleConv, self).__init__()
+        self.conv = nn.Sequential(
+            # kernel_size=3
+            # stride=1(evry rown a nd column is affected)
+            # padding=1(same conv)input height i width isti nakon konv
+            # bias=false
+            # batchnomr-normalizacija
+            # relu aktivacijska funk
+            nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True),
+
+            nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            ##inplace means that it will modify the input directly, without allocating any additional output
+            nn.ReLU(inplace=True),
+
+        )
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+class UNET(nn.Module):
+    ##moguce da broj out kanala promjenis,ovjde 1 jer je binary image segemntation
+    def __init__(self, in_channels=3, out_channels=2, features=[64, 128, 256, 512]):
+        super(UNET, self).__init__()
+        # jer hocemo evaluirat i sve to imamo tu listu zbog layera,spremamo sve te konvolucije
+        self.downs = nn.ModuleList()
+        self.ups = nn.ModuleList()
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        # Down part
+        for feature in features:
+            self.downs.append(DoubleConv(in_channels, feature))
+            in_channels = feature
+
+        # Up part
+        for feature in reversed(features):
+            self.ups.append(nn.ConvTranspose2d(feature * 2, feature, kernel_size=2,
+                                               stride=2))  ##kernel size is tride ce poduplat ovdje height i width slike
+            self.ups.append(DoubleConv(feature * 2, feature))
+
+        self.bottleneck = DoubleConv(features[-1], features[-1] * 2)  # onaj zasebni dio
+        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)
+
+    def forward(self, x):
+        skip_connections = []
+        for down in self.downs:
+            x = down(x)
+            skip_connections.append(x)
+            x = self.pool(x)
+
+        x = self.bottleneck(x)
+        skip_connections = skip_connections[::-1]  # obrnuti redoslijed
+
+        for idx in range(0, len(self.ups), 2):
+            x = self.ups[idx](x)
+            skip_connection = skip_connections[idx // 2]
+            if x.shape != skip_connection.shape:
+                x = TF.resize(x, size=skip_connection.shape[2:])  ##ako input nije djeljiv s 2, resize se
+
+            concat_skip = torch.cat((skip_connection, x), dim=1)
+            x = self.ups[idx + 1](concat_skip)
+
+        return self.final_conv(x)