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# models/cnn_model.py
import torch.nn as nn
class MonkeyCNN(nn.Module):
def __init__(self, num_classes):
super(MonkeyCNN, self).__init__()
self.net = nn.Sequential(
# Conv Block 1
nn.Conv2d(3, 32, kernel_size=3, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(2),
# Conv Block 2
nn.Conv2d(32, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(2),
# Conv Block 3
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(2),
# Conv Block 4 (Optional: add more depth)
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.AdaptiveAvgPool2d((1, 1)), # Output size: [B, 256, 1, 1]
nn.Flatten(),
nn.Dropout(0.3),
nn.Linear(256, num_classes)
)
def forward(self, x):
return self.net(x)
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