model.py

"""
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)