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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]
} |