model.py

import torch
import torch.nn as nn
import torchvision.models as models

class AstronomyClassifier(nn.Module):
    """Astronomy Image Classification Model"""
    
    def __init__(self, model_name='resnet50', num_classes=6, pretrained=False):
        super(AstronomyClassifier, self).__init__()
        
        self.model_name = model_name
        self.num_classes = num_classes
        
        # Load backbone
        if model_name == 'resnet50':
            self.backbone = models.resnet50(pretrained=pretrained)
            num_features = self.backbone.fc.in_features
            self.backbone.fc = nn.Identity()
        elif model_name == 'densenet121':
            self.backbone = models.densenet121(pretrained=pretrained)
            num_features = self.backbone.classifier.in_features
            self.backbone.classifier = nn.Identity()
        else:
            raise ValueError(f"Unsupported model: {model_name}")
        
        # Custom classifier
        self.classifier = nn.Sequential(
            nn.Dropout(0.5),
            nn.Linear(num_features, 512),
            nn.ReLU(),
            nn.BatchNorm1d(512),
            nn.Dropout(0.5),
            nn.Linear(512, 256),
            nn.ReLU(),
            nn.BatchNorm1d(256),
            nn.Dropout(0.5),
            nn.Linear(256, num_classes)
        )
    
    def forward(self, x):
        features = self.backbone(x)
        output = self.classifier(features)
        return output

# Model configuration
MODEL_CONFIG = {
    "model_name": "resnet50",
    "num_classes": 6,
    "class_names": ["constellation", "cosmos", "galaxies", "nebula", "planets", "stars"],
    "input_size": (224, 224),
    "mean": [0.485, 0.456, 0.406],
    "std": [0.229, 0.224, 0.225]
}