diff --git a/cv_model_architectures.py b/cv_model_architectures.py
new file mode 100644
index 0000000..0bd5e31
--- /dev/null
+++ b/cv_model_architectures.py
@@ -0,0 +1,79 @@
+import torch, torchvision
+import numpy as np
+import torch.nn.functional as F
+from torch import nn
+
+
+class Model_resnet(nn.Module):
+    def __init__(self, freeze=False, pretrained=True):
+        super(Model_resnet, self).__init__()
+        if pretrained:
+            self.resnet = torchvision.models.resnet34(pretrained=True)
+        else:
+            self.resnet = torchvision.models.resnet34(pretrained=False)
+        if freeze:
+            for param in self.resnet.parameters():
+                param.requires_grad = False
+        self.resnet.fc = torch.nn.Linear(512, 1024)
+        self.addition = nn.Sequential(
+            nn.ReLU(),
+            nn.BatchNorm1d(1024),
+            nn.Dropout(p=0.5),
+            nn.Linear(1024, 512),
+            nn.ReLU(),
+            nn.BatchNorm1d(512),
+            nn.Dropout(p=0.5),
+            nn.Linear(512, 6))
+
+    def forward(self, image):
+        z = self.resnet(image)
+        z = self.addition(z)
+        return z
+
+
+
+class Model_inception(nn.Module):
+    def __init__(self, freeze=False, pretrained=True):
+        super(Model_inception, self).__init__()
+        if pretrained:
+            self.inceptionnet = torchvision.models.inception_v3(pretrained=True)
+        else:
+            self.inceptionnet = torchvision.models.inception_v3(pretrained=False)
+        if freeze:
+            for param in self.inceptionnet.parameters():
+                param.requires_grad = False
+        self.inceptionnet.aux_logits = False
+        self.inceptionnet.fc = torch.nn.Linear(2048, 1024)
+        self.addition = nn.Sequential(
+            nn.ReLU(),
+            nn.BatchNorm1d(1024),
+            nn.Dropout(p=0.5),
+            nn.Linear(1024, 512),
+            nn.ReLU(),
+            nn.BatchNorm1d(512),
+            nn.Dropout(p=0.5),
+            nn.Linear(512, 6))
+
+    def forward(self, image):
+        z = self.inceptionnet(image)
+        z = self.addition(z)
+        return z
+
+
+class Model_mobilenet(nn.Module):
+    def __init__(self, freeze=False, pretrained=True):
+        super(Model_mobilenet, self).__init__()
+        if pretrained:
+            self.mobilenet = torchvision.models.mobilenet_v2(pretrained=True)
+        else:
+            self.mobilenet = torchvision.models.mobilenet_v2(pretrained=False)
+        if freeze:
+            for param in self.mobilenet.parameters():
+                param.requires_grad = False
+        self.mobilenet.classifier = nn.Sequential(
+            nn.Dropout(p=0.2, inplace=False),
+            nn.Linear(1280, 6, bias=True))
+
+    def forward(self, image):
+        z = self.mobilenet(image)
+        return z