Update config.py

config.py

@@ -1,45 +1,45 @@
-"""
-Configuration file for Automated Tablet Defect Detection System
-"""
-
-import os
-from pathlib import Path
-
-# ===================== PATH CONFIGURATION =====================
-PROJECT_ROOT = Path(__file__).parent
-DATA_DIR = PROJECT_ROOT / "capsule"
-TRAIN_DIR = DATA_DIR / "train" / "good"
-TEST_DIR = DATA_DIR / "test"
-GROUND_TRUTH_DIR = DATA_DIR / "ground_truth"
-MODEL_DIR = PROJECT_ROOT / "models"
-RESULTS_DIR = PROJECT_ROOT / "results"
-
-# Create directories if they don't exist
-MODEL_DIR.mkdir(exist_ok=True)
-RESULTS_DIR.mkdir(exist_ok=True)
-
-# ===================== MODEL CONFIGURATION =====================
-# Backbone architecture (ResNet18 for balance between speed and accuracy)
-BACKBONE = "resnet18"
-FEATURE_LAYERS = ["layer1", "layer2", "layer3"]  # Multi-scale features
-
-# Image preprocessing
-IMAGE_SIZE = (224, 224)  # Standard ImageNet size
-MEAN = [0.485, 0.456, 0.406]  # ImageNet normalization
-STD = [0.229, 0.224, 0.225]
-
-# PaDiM parameters
-REDUCE_DIM = 100  # Dimensionality reduction via random projection
-EPSILON = 1e-5    # Numerical stability for covariance matrix
-
-# ===================== INFERENCE CONFIGURATION =====================
-ANOMALY_THRESHOLD = 0.5  # Decision threshold (tunable)
-HEATMAP_COLORMAP = "jet"  # Colormap for visualization
-HEATMAP_ALPHA = 0.4      # Overlay transparency
-
-# ===================== TRAINING CONFIGURATION =====================
-BATCH_SIZE = 32
-NUM_WORKERS = 4  # Dataloader workers (set to 0 for Windows compatibility)
-
-# ===================== EVALUATION CONFIGURATION =====================
-DEFECT_TYPES = ["crack", "faulty_imprint", "poke", "scratch", "squeeze"]
+"""
+Configuration file for Automated Tablet Defect Detection System
+"""
+
+import os
+from pathlib import Path
+
+# ===================== PATH CONFIGURATION =====================
+PROJECT_ROOT = Path(__file__).parent
+DATA_DIR = PROJECT_ROOT / "capsule"
+TRAIN_DIR = DATA_DIR / "train" / "good"
+TEST_DIR = DATA_DIR / "test"
+GROUND_TRUTH_DIR = DATA_DIR / "ground_truth"
+MODEL_DIR = PROJECT_ROOT / "models"
+RESULTS_DIR = PROJECT_ROOT / "results"
+
+# Create directories if they don't exist
+MODEL_DIR.mkdir(exist_ok=True)
+RESULTS_DIR.mkdir(exist_ok=True)
+
+# ===================== MODEL CONFIGURATION =====================
+# Backbone architecture (ResNet18 for balance between speed and accuracy)
+BACKBONE = "resnet18"
+FEATURE_LAYERS = ["layer1", "layer2", "layer3"]  # Multi-scale features
+
+# Image preprocessing
+IMAGE_SIZE = (224, 224)  # Standard ImageNet size
+MEAN = [0.485, 0.456, 0.406]  # ImageNet normalization
+STD = [0.229, 0.224, 0.225]
+
+# PaDiM parameters
+REDUCE_DIM = 100  # Dimensionality reduction via random projection
+EPSILON = 1e-5    # Numerical stability for covariance matrix
+
+# ===================== INFERENCE CONFIGURATION =====================
+ANOMALY_THRESHOLD = 15.0  # Decision threshold for Mahalanobis distance (tunable)
+HEATMAP_COLORMAP = "jet"  # Colormap for visualization
+HEATMAP_ALPHA = 0.4      # Overlay transparency
+
+# ===================== TRAINING CONFIGURATION =====================
+BATCH_SIZE = 32
+NUM_WORKERS = 4  # Dataloader workers (set to 0 for Windows compatibility)
+
+# ===================== EVALUATION CONFIGURATION =====================
+DEFECT_TYPES = ["crack", "faulty_imprint", "poke", "scratch", "squeeze"]