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from dataclasses import dataclass
@dataclass
class Config:
# Data paths
train_csv_path: str = "data/train.csv"
val_csv_path: str = "data/val.csv"
test_csv_path: str = "data/test.csv" # Added for test data
# Model parameters
input_size: tuple[int, int] = (224, 224) # Input image size
num_classes: int = 5 # Number of output classes
batch_size: int = 32
epochs: int = 10
learning_rate: float = 0.001
model_architecture: str = "PretrainedResNet50" # Specify the backbone architecture
loss_function: str = "cross_entropy"
optimizer: str = "Adam"
lr_scheduler: str = "StepLR"
dropout_rate: float = 0.5
weight_decay: float = 0.001
early_stopping: bool = True
use_gpu: bool = True
random_seed: int = 42
data_augmentation: bool = True
# Model paths
model_save_path: str = "models/model.pth"
logs_path: str = "logs/"
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