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src/cat_cnn.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5848645750c42c14efbf3510a02656df894a717bc6d531822ba159e1fcbb37c5
+size 67505560

src/cnn_model.py

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+import torch.nn as nn
+
+class CatBreedCNN(nn.Module):
+    def __init__(self, num_classes):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Conv2d(3, 32, kernel_size=3, padding=1),
+            nn.BatchNorm2d(32),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+
+            nn.Conv2d(32, 64, kernel_size=3, padding=1),
+            nn.BatchNorm2d(64),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+
+            nn.Conv2d(64, 128, kernel_size=3, padding=1),
+            nn.BatchNorm2d(128),
+            nn.ReLU(),
+            nn.MaxPool2d(2),  
+
+            nn.Flatten(),
+            nn.Linear(128 * 16 * 16, 512),
+            nn.ReLU(),
+            nn.Dropout(0.5),
+
+            nn.Linear(512, num_classes)
+        )
+
+    def forward(self, x):
+        return self.net(x)