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config.json

@@ -1,120 +1,26 @@
 {
   "model_type": "ResidualConvAutoencoder",
-  "task": "image-feature-extraction",
+  "architecture": "autoencoder",
+  "latent_dim": 512,
+  "image_size": 128,
+  "input_channels": 3,
+  "dropout": 0.1,
+  "num_epochs_trained": 30,
+  "best_epoch": 29,
+  "batch_size": 1024,
+  "learning_rate": 0.0001,
+  "weight_decay": 1e-05,
+  "gradient_clip": 1.0,
+  "datasets": [
+    "CIFAR-10",
+    "CIFAR-100",
+    "STL-10-train",
+    "STL-10-unlabeled"
+  ],
+  "best_val_loss": 0.00797,
+  "training_time_minutes": 21.4,
   "framework": "pytorch",
-  "architecture": {
-    "encoder_stages": 5,
-    "decoder_stages": 5,
-    "latent_dim": 512,
-    "input_size": [
-      128,
-      128,
-      3
-    ],
-    "channels": [
-      64,
-      128,
-      256,
-      512,
-      512
-    ],
-    "residual_blocks": true
-  },
-  "training": {
-    "dataset": "CIFAR-10",
-    "train_samples": 50000,
-    "test_samples": 10000,
-    "batch_size": 1024,
-    "optimizer": "AdamW",
-    "learning_rate": 0.001,
-    "weight_decay": 1e-05,
-    "epochs": 100,
-    "loss_function": "MSE",
-    "scheduler": "ReduceLROnPlateau"
-  },
-  "performance": {
-    "test_loss": 0.00429,
-    "validation_loss": 0.004294,
-    "train_loss": 0.003344,
-    "parameters": 34849667,
-    "training_time_minutes": 26.24
-  },
-  "preprocessing": {
-    "image_size": [
-      128,
-      128
-    ],
-    "normalization": {
-      "mean": [
-        0.5,
-        0.5,
-        0.5
-      ],
-      "std": [
-        0.5,
-        0.5,
-        0.5
-      ]
-    },
-    "range": "[-1, 1]"
-  },
-  "calibration": {
-    "description": "Calibrated MSE reconstruction error thresholds",
-    "dataset_real": "CIFAR-10 test set",
-    "dataset_fake": "Random Noise test set",
-    "image_size": [
-      128,
-      128
-    ],
-    "normalization": "mean=0.5, std=0.5 (range [-1, 1])",
-    "thresholds": {
-      "strict": {
-        "value": 0.012767740525305271,
-        "description": "High confidence - 99th percentile of real images",
-        "expected_tpr": 1.0,
-        "expected_fpr": 0.01
-      },
-      "balanced": {
-        "value": 0.009065581485629082,
-        "description": "Balanced - 95th percentile of real images",
-        "expected_tpr": 1.0,
-        "expected_fpr": 0.05
-      },
-      "sensitive": {
-        "value": 0.00931915920227766,
-        "description": "Sensitive - mean + 2*std of real images",
-        "expected_tpr": 1.0,
-        "expected_fpr": 0.0448
-      },
-      "optimal": {
-        "value": 0.20403903722763062,
-        "description": "Optimal separation between distributions",
-        "expected_tpr": 1.0,
-        "expected_fpr": 0.0
-      }
-    },
-    "statistics": {
-      "real_images": {
-        "mean": 0.004293377045542002,
-        "std": 0.0025128910783678293,
-        "median": 0.003766049398109317,
-        "min": 0.00019204482669010758,
-        "max": 0.02400028333067894,
-        "percentile_95": 0.009065581485629082,
-        "percentile_99": 0.012767740525305271
-      },
-      "fake_images": {
-        "mean": 0.40168634057044983,
-        "median": 0.4016798138618469,
-        "min": 0.3955906331539154,
-        "max": 0.4081667363643646,
-        "percentile_5": 0.3990125060081482
-      },
-      "separation": {
-        "mean_difference": 0.39739295840263367,
-        "ratio": 93.55953216552734
-      }
-    },
-    "note": "These thresholds are calibrated on CIFAR-10 vs Random Noise. For real deepfake detection, recalibrate on actual fake images."
-  }
+  "task": "image-reconstruction",
+  "separation_ratio": 19.18,
+  "description": "Residual Convolutional Autoencoder trained on CIFAR-10, CIFAR-100, and STL-10 for deepfake detection via reconstruction error"
 }