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0ba6002 f392f42 0ba6002 f392f42 0ba6002 f392f42 0ba6002 c61ba70 0ba6002 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | """
Configuration management for the project
"""
import os
from pathlib import Path
from typing import Optional
class Config:
"""
Central configuration class for the project
"""
def __init__(self):
# Project paths
self.PROJECT_ROOT = Path(__file__).parent.parent.parent
self.DATA_DIR = self.PROJECT_ROOT / "data"
self.RAW_DATA_DIR = self.DATA_DIR / "raw"
self.PROCESSED_DATA_DIR = self.DATA_DIR / "processed"
self.AUGMENTED_DATA_DIR = self.DATA_DIR / "augmented"
self.MODELS_DIR = self.DATA_DIR / "models"
self.LOGS_DIR = self.PROJECT_ROOT / "logs"
# Create directories if they don't exist
for directory in [
self.DATA_DIR,
self.RAW_DATA_DIR,
self.PROCESSED_DATA_DIR,
self.AUGMENTED_DATA_DIR,
self.MODELS_DIR,
self.LOGS_DIR,
]:
directory.mkdir(parents=True, exist_ok=True)
# Image processing settings
self.TARGET_IMAGE_SIZE = 256 # pixels (256×256)
self.IMAGE_CHANNELS = 3 # RGB
self.NORMALIZATION_RANGE = (0, 1) # Pixel normalization range
# Data augmentation settings
self.AUGMENTATION_FACTOR = 5 # Generate 5 variations per image
self.ROTATION_RANGE = 10 # ±10 degrees
self.BRIGHTNESS_RANGE = 0.15 # ±15%
self.ZOOM_RANGE = (0.95, 1.05) # 95-105%
# Dataset split settings
self.TEST_SIZE = 0.2 # 20% for testing
self.VAL_SIZE = 0.1 # 10% for validation
self.RANDOM_STATE = 42 # For reproducibility
self.CV_FOLDS = 5 # Stratified 5-fold cross-validation
# Deep Learning settings
self.DL_IMAGE_SIZE = 224 # ResNet50/EfficientNet input
self.DL_BATCH_SIZE = 8 # Reduced for MPS memory limits with dual backbone
self.DL_EPOCHS = 100 # More epochs with early stopping
self.DL_LEARNING_RATE = 1e-4 # Lower LR for fine-tuning backbone
self.DL_WEIGHT_DECAY = 1e-4
self.DL_PATIENCE = 15 # Early stopping patience
self.DL_BACKBONE_FROZEN = True # Freeze backbone, train only heads (faster)
self.DL_MODELS_DIR = self.DATA_DIR / "models" / "dl"
self.DL_EXPANDED_DATA_DIR = self.DATA_DIR / "raw" / "expanded"
self.DL_EXTERNAL_DATA_DIR = self.DATA_DIR / "raw" / "external"
# Multi-head model settings (SVDD + classifier heads)
self.DL_SVDD_EMBEDDING_DIM = 128 # Deep SVDD embedding dimension
self.DL_HEAD_A_ALPHA = 0.15 # Pokemon classifier (increased: now have non-Pokemon negatives)
self.DL_HEAD_B_BETA = 0.40 # Back authenticator loss weight
self.DL_HEAD_C_GAMMA = 0.45 # Front SVDD loss weight (primary mechanism)
# Training improvements for counterfeit detection
self.DL_BACK_COUNTERFEIT_WEIGHT = 2.5 # Class weight for counterfeit backs (ratio real/fake: 300/120)
self.DL_MINORITY_AUGMENT_FACTOR = 2 # Duplication factor for minority class (backs_fake)
self.DL_CALIBRATION_FBETA = 2.0 # F-beta for threshold calibration (2.0 = recall-weighted)
self.DL_USE_FOCAL_LOSS = True # Enable focal loss for Head A/B
self.DL_FOCAL_GAMMA = 2.0 # Focal loss gamma (focus on hard examples)
self.DL_SVDD_CONTRASTIVE_ETA = 1.0 # Weight for contrastive SVDD term (Deep SAD)
# Ensure DL directories exist
self.DL_MODELS_DIR.mkdir(parents=True, exist_ok=True)
self.DL_EXPANDED_DATA_DIR.mkdir(parents=True, exist_ok=True)
self.DL_EXTERNAL_DATA_DIR.mkdir(parents=True, exist_ok=True)
# Logging settings
self.LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
self.LOG_FORMAT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
self.LOG_FILE = self.LOGS_DIR / "cardauth.log"
def get_dataset_path(self, dataset_type: str = "raw") -> Path:
"""
Get path to dataset directory
Args:
dataset_type: One of 'raw', 'processed', 'augmented'
Returns:
Path to the dataset directory
"""
dataset_map = {
"raw": self.RAW_DATA_DIR,
"processed": self.PROCESSED_DATA_DIR,
"augmented": self.AUGMENTED_DATA_DIR,
}
if dataset_type not in dataset_map:
raise ValueError(
f"Unknown dataset type: {dataset_type}. "
f"Choose from: {list(dataset_map.keys())}"
)
return dataset_map[dataset_type]
def get_model_path(self, model_name: str) -> Path:
"""
Get path to save/load a model
Args:
model_name: Name of the model file
Returns:
Path to the model file
"""
return self.MODELS_DIR / model_name
def __repr__(self) -> str:
return f"Config(root={self.PROJECT_ROOT})"
# Global config instance
config = Config()
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