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model.py

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+"""
+SimpleNet — lightweight CNN for EuroSAT satellite image classification.
+4 convolutional blocks (double-and-halve pattern) + FC classifier.
+Input: 3×64×64 RGB | Output: 10 land-use classes | ~850K parameters
+"""
+
+import torch
+import torch.nn as nn
+
+
+CLASS_NAMES = [
+    "AnnualCrop", "Forest", "HerbaceousVegetation", "Highway",
+    "Industrial", "Pasture", "PermanentCrop", "Residential",
+    "River", "SeaLake"
+]
+
+
+class SimpleNet(nn.Module):
+    def __init__(self, num_classes: int = 10):
+        super().__init__()
+
+        # 64×64 → 32×32
+        self.block1 = nn.Sequential(
+            nn.Conv2d(3, 32, kernel_size=3, padding=1),
+            nn.BatchNorm2d(32),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+        )
+        # 32×32 → 16×16
+        self.block2 = nn.Sequential(
+            nn.Conv2d(32, 64, kernel_size=3, padding=1),
+            nn.BatchNorm2d(64),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+        )
+        # 16×16 → 8×8
+        self.block3 = nn.Sequential(
+            nn.Conv2d(64, 128, kernel_size=3, padding=1),
+            nn.BatchNorm2d(128),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+        )
+        # 8×8 → 4×4
+        self.block4 = nn.Sequential(
+            nn.Conv2d(128, 256, kernel_size=3, padding=1),
+            nn.BatchNorm2d(256),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+        )
+
+        self.classifier = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(256 * 4 * 4, 512),
+            nn.ReLU(),
+            nn.Dropout(0.3),
+            nn.Linear(512, num_classes),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.block1(x)
+        x = self.block2(x)
+        x = self.block3(x)
+        x = self.block4(x)
+        return self.classifier(x)