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docs: update README and config.json

README.md

@@ -23,10 +23,10 @@ model-index:
           name: AI-Generated Image Detection
         metrics:
           - type: accuracy
-            value: 0.656
+            value: 0.710
             name: Validation Accuracy
           - type: loss
-            value: 0.308
+            value: 0.216
             name: Validation Loss
 ---
 
@@ -84,24 +84,23 @@ The model uses a **Full Mode** architecture with three complementary analysis br
 
 | Parameter | Value |
 |-----------|-------|
-| Epochs | 3 |
-| Batch Size | 8 |
+| Epochs | 15 |
+| Batch Size | 16 |
 | Image Size | 256×256 |
-| Learning Rate | 1e-4 (with warmup) |
+| Learning Rate | 2e-6 |
 | Optimizer | AdamW |
-| Scheduler | Cosine Annealing |
-| Mixed Precision | Enabled (AMP) |
-| Hardware | NVIDIA RTX 3050 Ti (4GB VRAM) |
+| Scheduler | OneCycleLR |
+| Mixed Precision | Disabled |
+| Hardware | NVIDIA Tesla T4 (16GB VRAM) |
 
 ### Training Results
 
 | Epoch | Train Loss | Val Loss | Val Accuracy |
 |-------|------------|----------|--------------|
-| 0 | 0.4115 | 0.3262 | 61.46% |
-| 1 | 0.3707 | 0.3099 | 65.09% |
-| 2 | 0.3506 | **0.3079** | **65.55%** |
-
-**Note**: This is an early checkpoint after 3 epochs of training. The model is still learning and accuracy will improve with more training epochs (recommended: 30-50 epochs for production use).
+| 10 | 0.2555 | 0.2238 | 68.48% |
+| 11 | 0.2504 | 0.2207 | 69.57% |
+| 12 | 0.2501 | 0.2194 | 69.90% |
+| 15 | 0.2468 | **0.2163** | **71.03%** |
 
 ## Usage
 
@@ -234,7 +233,6 @@ The checkpoint file (`best_model.pt`) contains:
 | `model_state_dict` | Model weights |
 | `optimizer_state_dict` | Optimizer state (for resume training) |
 | `scheduler_state_dict` | LR scheduler state |
-| `scaler_state_dict` | AMP GradScaler state |
 | `epoch` | Training epoch number |
 | `global_step` | Global training step |
 | `best_val_loss` | Best validation loss achieved |
@@ -243,7 +241,6 @@ The checkpoint file (`best_model.pt`) contains:
 
 ## Limitations
 
-- **Early Training Stage**: This checkpoint is from early training (3 epochs). For production use, train for 30-50+ epochs.
 - **Dataset Bias**: Performance may vary on images from generators not represented in the training set.
 - **Resolution Dependency**: Best results at 256×256. Other resolutions are automatically resized.
 - **Adversarial Robustness**: Not specifically hardened against adversarial attacks.

config.json

@@ -12,13 +12,15 @@
     "fast_mode": false
   },
   "training_config": {
-    "epochs_trained": 3,
-    "batch_size": 8,
-    "learning_rate": 0.0001,
+    "epochs_trained": 15,
+    "batch_size": 16,
+    "learning_rate": 0.000002,
     "optimizer": "adamw",
-    "scheduler": "cosine",
-    "mixed_precision": true,
-    "warmup_epochs": 5
+    "scheduler": "onecycle",
+    "mixed_precision": false,
+    "regression_weight": 1.0,
+    "classification_weight": 0.2,
+    "ranking_weight": 0.1
   },
   "dataset_info": {
     "train_samples": 128776,
@@ -29,9 +31,9 @@
     "fake_ratio": 0.542
   },
   "metrics": {
-    "best_val_loss": 0.3079,
-    "best_val_accuracy": 0.6555,
-    "final_train_loss": 0.3506
+    "best_val_loss": 0.2163,
+    "best_val_accuracy": 0.7103,
+    "final_train_loss": 0.2468
   },
   "input_spec": {
     "type": "image",
@@ -51,22 +53,5 @@
       "1.0": "real/natural image",
       "0.0": "fake/generated image"
     }
-  },
-  "checkpoint_info": {
-    "format": "pytorch",
-    "keys": [
-      "model_state_dict",
-      "optimizer_state_dict", 
-      "scheduler_state_dict",
-      "scaler_state_dict",
-      "epoch",
-      "global_step",
-      "best_val_loss",
-      "train_history",
-      "val_history"
-    ]
-  },
-  "version": "0.1.0",
-  "library_name": "tigas",
-  "github_repo": "https://github.com/H1merka/TIGAS"
+  }
 }