diff --git a/models/SiaN/.gitignore b/models/SiaN/.gitignore
index 39162b9..25bf8d2 100644
--- a/models/SiaN/.gitignore
+++ b/models/SiaN/.gitignore
@@ -1,2 +1,3 @@
 runs
-*.gen.py
\ No newline at end of file
+*.gen.py
+*.img
\ No newline at end of file
diff --git a/models/SiaN/_schema.md b/models/SiaN/_schema.md
index a82577a..a8d3746 100644
--- a/models/SiaN/_schema.md
+++ b/models/SiaN/_schema.md
@@ -1,4 +1,8 @@
-# _schema.md
+# SiaN Schema
+
+Notebook structure definition for SiaN model.
+
+---
 
 ## Format
 
@@ -10,14 +14,18 @@
 # markdown
 """Description"""
 
-# inline:
-<custom code>
+# shell:
+<shell commands>
 ```
 
 ## Directives
 
-- `# code:` - include file from src/
-- `# markdown` - description block
-- `# inline:` - custom code cell
+| Directive      | Description                        |
+|----------------|------------------------------------|
+| `# code:`      | Include file from src/             |
+| `# markdown`   | Description block                  |
+| `# shell:`     | Shell script cell                  |
+
+---
 
 `build.py` generates notebook from `_schema.py`.
diff --git a/models/SiaN/_schema.py b/models/SiaN/_schema.py
index 9dfdd29..6bc23f5 100644
--- a/models/SiaN/_schema.py
+++ b/models/SiaN/_schema.py
@@ -20,28 +20,87 @@ from tqdm import tqdm
 
 # code: ./src/utils.py
 # markdown
-"""# SiaN Model"""
+"""# Configuration
+
+Global settings for:
+- Data paths and image parameters
+- Training hyperparameters
+- Model architecture options
+
+Contains the `config` dictionary used across all modules."""
 
 # code: ./src/dataloader.py
 # markdown
-"""Dataset for Google/Yandex image pairs with homography augmentation."""
+"""## Dataset
+
+Google/Yandex image pair loader with homography augmentation.
+
+**Features:**
+- Loads paired images from dual camera sources
+- Applies random homography transformations
+- Supports configurable train/val split
+
+**Returns:**
+- Batch dict with `google_img`, `yandex_img`, `homography_params`"""
 
 # code: ./src/model.py
 # markdown
-"""HomographyCNN6 predicts 6 params: rx, ry, rz, tx, ty, scale."""
+"""## Model
+
+`HomographyCNN6` — CNN architecture for homography estimation.
+
+**Output:** 6 parameters
+- `rx, ry, rz` — rotation angles (radians)
+- `tx, ty` — translation offsets
+- `scale` — isotropic scale factor
+
+**Architecture:**
+- Dual-branch CNN (Google + Yandex images)
+- Shared backbone (configurable: resnet18/34/50)
+- Fusion head with dropout regularization"""
 
 # code: ./src/train.py
 # markdown
-"""HomographyTrainer manages training loop with validation."""
+"""## Training
+
+`HomographyTrainer` — training loop with validation and checkpointing.
+
+**Features:**
+- Epoch-based training with tqdm progress bar
+- Adam optimizer with configurable LR
+- Validation after each epoch
+- Best model auto-save
+- Periodic checkpoints (every N epochs via `save_every_n_epochs`)
+
+**Checkpoint saving:**
+- `best_model.pt` — lowest validation loss
+- `checkpoint_epoch_N.pt` — periodic saves"""
 
 # code: ./src/analyze.py
 # markdown
-"""Visualization and analysis of predictions."""
+"""## Analysis
+
+Visualization and evaluation tools:
+
+- Training metrics plots (loss curves)
+- Prediction visualization on sample images
+- Error analysis and statistics"""
 
 # code: ./src/main.py
 # markdown
-"""Run main() to execute the full pipeline."""
+"""## Main Pipeline
 
-# inline:
-if __name__ == "__main__":
-    main()
+Executes the full training workflow:
+1. Load dataset info
+2. Create data loaders
+3. Initialize model
+4. Train with validation
+5. Analyze and export results
+
+**Outputs:**
+- Model checkpoints in `runs/checkpoints/`
+- TensorBoard logs in `runs/`
+- Analysis plots"""
+
+# # shell:
+# !zip artefacts.zip runs/gan_training/checkpoints/best_model.pt
diff --git a/models/SiaN/build.py b/models/SiaN/build.py
index 7e0b240..cdf5fbd 100644
--- a/models/SiaN/build.py
+++ b/models/SiaN/build.py
@@ -42,23 +42,34 @@ def parse_schema(schema_path):
             i += 1
             continue
         
-        if stripped.startswith('# inline:'):
+        if stripped.startswith('# inline:') or stripped.startswith('# # shell:'):
+            directive = 'inline' if stripped.startswith('# inline:') else 'shell'
             i += 1
             code_lines = []
             while i < len(lines):
                 stripped = lines[i].strip()
-                if stripped.startswith('#'):
-                    if stripped.startswith('# code:') or stripped.startswith('# inline:') or stripped == '# markdown' or stripped.startswith('# ==='):
-                        break
-                    i += 1
-                    continue
+                if directive == 'shell':
+                    if stripped.startswith('# ') or stripped.startswith('# # '):
+                        line_content = lines[i].strip()
+                        if line_content.startswith('# # '):
+                            line_content = line_content[3:].lstrip()
+                        else:
+                            line_content = line_content[2:]
+                        code_lines.append(line_content)
+                        i += 1
+                        continue
+                    if stripped.startswith('#'):
+                        if stripped.startswith('# code:') or stripped.startswith('# inline:') or stripped.startswith('# # shell:') or stripped == '# markdown' or stripped.startswith('# ==='):
+                            break
+                        i += 1
+                        continue
                 if stripped == '':
                     i += 1
                     continue
                 code_lines.append(lines[i])
                 i += 1
             if code_lines:
-                items.append(('inline', '\n'.join(code_lines).rstrip()))
+                items.append((directive, '\n'.join(code_lines).rstrip()))
             continue
         
         if stripped == '# markdown':
@@ -180,6 +191,20 @@ def build_notebook(schema_path, output_path=None):
                 "source": [line + "\n" for line in lines]
             }
             cells.append(cell)
+        elif item_type == 'shell':
+            lines = item_content.split('\n')
+            while lines and lines[-1].strip() == '':
+                lines.pop()
+            if lines:
+                lines.append('')
+            cell = {
+                "cell_type": "code",
+                "execution_count": None,
+                "metadata": {},
+                "outputs": [],
+                "source": [line + "\n" for line in lines]
+            }
+            cells.append(cell)
         elif item_type == 'code':
             try:
                 content = read_src_file(item_content, schema_dir)
diff --git a/models/SiaN/notebook.gen.ipynb b/models/SiaN/notebook.gen.ipynb
index b602f46..a53e91e 100644
--- a/models/SiaN/notebook.gen.ipynb
+++ b/models/SiaN/notebook.gen.ipynb
@@ -42,6 +42,7 @@
     "    \"dropout_rate\": 0.3,\n",
     "    \"backbone\": \"resnet18\",\n",
     "    \"output_dir\": r\"C:\\Users\\admin\\Projects\\autopilot\\models\\SiaN\\runs\",\n",
+    "    \"save_every_n_epochs\": 15,\n",
     "}\n",
     "\n",
     "\n",
@@ -88,7 +89,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# SiaN Model\n"
+   "source": "# Configuration\n\nGlobal settings for:\n- Data paths and image parameters\n- Training hyperparameters\n- Model architecture options\n"
   },
   {
    "cell_type": "code",
@@ -185,7 +186,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "Dataset for Google/Yandex image pairs with homography augmentation.\n"
+   "source": "## Dataset\n\nGoogle/Yandex image pair loader with homography augmentation.\n\n**Features:**\n- Loads paired images from dual camera sources\n- Applies random homography transformations\n- Supports configurable train/val split\n\n**Returns:**\n"
   },
   {
    "cell_type": "code",
@@ -237,7 +238,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "HomographyCNN6 predicts 6 params: rx, ry, rz, tx, ty, scale.\n"
+   "source": "## Model\n\n`HomographyCNN6` — CNN architecture for homography estimation.\n\n**Output:** 6 parameters\n- `rx, ry, rz` — rotation angles (radians)\n- `tx, ty` — translation offsets\n- `scale` — isotropic scale factor\n\n**Architecture:**\n- Dual-branch CNN (Google + Yandex images)\n- Shared backbone (configurable: resnet18/34/50)\n"
   },
   {
    "cell_type": "code",
@@ -310,6 +311,10 @@
     "                self.save_checkpoint(epoch, is_best=True)\n",
     "                print(f\"Best model saved (val loss: {val_metrics['loss']:.4f})\")\n",
     "\n",
+    "            if epoch % config[\"save_every_n_epochs\"] == 0:\n",
+    "                self.save_checkpoint(epoch, is_best=False)\n",
+    "                print(f\"Checkpoint saved at epoch {epoch}\")\n",
+    "\n",
     "        self.writer.close()\n",
     "\n",
     "    def save_checkpoint(self, epoch, is_best=False):\n",
@@ -325,7 +330,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "HomographyTrainer manages training loop with validation.\n"
+   "source": "## Training\n\n`HomographyTrainer` — training loop with validation and checkpointing.\n\n**Features:**\n- Epoch-based training with tqdm progress bar\n- Adam optimizer with configurable LR\n- Validation after each epoch\n- Best model auto-save\n- Periodic checkpoints (every N epochs via `save_every_n_epochs`)\n\n**Checkpoint saving:**\n- `best_model.pt` — lowest validation loss\n"
   },
   {
    "cell_type": "code",
@@ -388,7 +393,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "Visualization and analysis of predictions.\n"
+   "source": "## Analysis\n\nVisualization and evaluation tools:\n\n- Training metrics plots (loss curves)\n- Prediction visualization on sample images\n"
   },
   {
    "cell_type": "code",
@@ -403,75 +408,52 @@
     "logging.basicConfig(level=logging.INFO, format=\"%(asctime)s - %(message)s\")\n",
     "logger = logging.getLogger(__name__)\n",
     "\n",
+    "logger.info(\"=\" * 50)\n",
+    "logger.info(\"SiaN Training Pipeline\")\n",
+    "logger.info(\"=\" * 50)\n",
     "\n",
-    "def create_dataset():\n",
-    "    logger.info(\"Creating data loaders...\")\n",
-    "    train_loader, val_loader = create_data_loaders(\n",
-    "        root_dir=config[\"data_dir\"],\n",
-    "        batch_size=config[\"batch_size\"],\n",
-    "        train_split=config[\"train_split\"],\n",
-    "        num_workers=config[\"num_workers\"],\n",
-    "        image_size=config[\"image_size\"],\n",
-    "    )\n",
-    "    logger.info(f\"Data loaders created: train={len(train_loader.dataset)}, val={len(val_loader.dataset)}\")\n",
-    "    return train_loader, val_loader\n",
+    "dataset_info = get_dataset_info()\n",
+    "logger.info(f\"Dataset: {dataset_info['size']} samples, keys={dataset_info['sample_keys']}\")\n",
     "\n",
+    "train_loader, val_loader = create_data_loaders(\n",
+    "    root_dir=config[\"data_dir\"],\n",
+    "    batch_size=config[\"batch_size\"],\n",
+    "    train_split=config[\"train_split\"],\n",
+    "    num_workers=config[\"num_workers\"],\n",
+    "    image_size=config[\"image_size\"],\n",
+    ")\n",
+    "logger.info(f\"Data loaders created: train={len(train_loader.dataset)}, val={len(val_loader.dataset)}\")\n",
     "\n",
-    "def create_model():\n",
-    "    logger.info(\"Creating model...\")\n",
-    "    model = HomographyCNN6(\n",
-    "        input_channels=3,\n",
-    "        backbone_name=config[\"backbone\"],\n",
-    "        pretrained=True,\n",
-    "        dropout_rate=config[\"dropout_rate\"]\n",
-    "    )\n",
-    "    logger.info(f\"Model created with {count_parameters(model):,} parameters\")\n",
-    "    return model\n",
+    "model = HomographyCNN6(\n",
+    "    input_channels=3,\n",
+    "    backbone_name=config[\"backbone\"],\n",
+    "    pretrained=True,\n",
+    "    dropout_rate=config[\"dropout_rate\"]\n",
+    ")\n",
+    "logger.info(f\"Model created with {count_parameters(model):,} parameters\")\n",
     "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "logger.info(f\"Using device: {device}\")\n",
     "\n",
-    "def train_model(model, train_loader, val_loader):\n",
-    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "    logger.info(f\"Using device: {device}\")\n",
-    "    \n",
-    "    trainer = HomographyTrainer(model, train_loader, val_loader, device)\n",
-    "    logger.info(\"Starting training...\")\n",
-    "    trainer.train(config[\"epochs\"])\n",
-    "    logger.info(\"Training completed\")\n",
-    "    return trainer\n",
+    "trainer = HomographyTrainer(model, train_loader, val_loader, device)\n",
+    "logger.info(\"Starting training...\")\n",
+    "trainer.train(config[\"epochs\"])\n",
+    "logger.info(\"Training completed\")\n",
     "\n",
+    "logger.info(\"Analyzing model...\")\n",
+    "results = analyze_training(trainer)\n",
+    "logger.info(f\"Analysis complete: best_val_loss={results['best_val_loss']:.4f}\")\n",
     "\n",
-    "def analyze_model(trainer):\n",
-    "    logger.info(\"Analyzing model...\")\n",
-    "    results = analyze_training(trainer)\n",
-    "    logger.info(f\"Analysis complete: best_val_loss={results['best_val_loss']:.4f}\")\n",
-    "    return results\n",
-    "\n",
-    "\n",
-    "def main():\n",
-    "    logger.info(\"=\" * 50)\n",
-    "    logger.info(\"SiaN Training Pipeline\")\n",
-    "    logger.info(\"=\" * 50)\n",
-    "    \n",
-    "    dataset_info = get_dataset_info()\n",
-    "    logger.info(f\"Dataset: {dataset_info['size']} samples, keys={dataset_info['sample_keys']}\")\n",
-    "    \n",
-    "    train_loader, val_loader = create_dataset()\n",
-    "    model = create_model()\n",
-    "    trainer = train_model(model, train_loader, val_loader)\n",
-    "    results = analyze_model(trainer)\n",
-    "    \n",
-    "    logger.info(\"=\" * 50)\n",
-    "    logger.info(\"Pipeline completed successfully\")\n",
-    "    logger.info(\"=\" * 50)\n",
-    "    \n",
-    "    return trainer, results\n",
+    "logger.info(\"=\" * 50)\n",
+    "logger.info(\"Pipeline completed successfully\")\n",
+    "logger.info(\"=\" * 50)\n",
     "\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "Run main() to execute the full pipeline.\n"
+   "source": "## Main Pipeline\n\nExecutes the full training workflow:\n1. Load dataset info\n2. Create data loaders\n3. Initialize model\n4. Train with validation\n5. Analyze and export results\n\n**Outputs:**\n- Model checkpoints in `runs/checkpoints/`\n- TensorBoard logs in `runs/`\n"
   },
   {
    "cell_type": "code",
@@ -479,8 +461,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "if __name__ == \"__main__\":\n",
-    "    main()\n",
+    "!zip artefacts.zip runs/gan_training/checkpoints/best_model.pt\n",
     "\n"
    ]
   }
diff --git a/models/SiaN/src/dataloader.py b/models/SiaN/src/dataloader.py
index dc8a5b7..3142cc4 100644
--- a/models/SiaN/src/dataloader.py
+++ b/models/SiaN/src/dataloader.py
@@ -78,7 +78,7 @@ def create_data_loaders(root_dir, batch_size=32, train_split=0.8, num_workers=0,
     split = int(train_split * len(indices))
     
     train_ds = Subset(aug_ds if augment_train else full_ds, indices[:split])
-    val_ds = Subset(full_ds, indices[split:])
+    val_ds = Subset(aug_ds, indices[split:])
 
     return (DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=True),
             DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=True))
diff --git a/models/SiaN/src/main.py b/models/SiaN/src/main.py
index b69c08a..8e62df9 100644
--- a/models/SiaN/src/main.py
+++ b/models/SiaN/src/main.py
@@ -1,4 +1,5 @@
 import logging
+import os
 
 import torch
 
@@ -12,69 +13,42 @@ from .utils import config
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(message)s")
 logger = logging.getLogger(__name__)
 
+logger.info("=" * 50)
+logger.info("SiaN Training Pipeline")
+logger.info("=" * 50)
 
-def create_dataset():
-    logger.info("Creating data loaders...")
-    train_loader, val_loader = create_data_loaders(
-        root_dir=config["data_dir"],
-        batch_size=config["batch_size"],
-        train_split=config["train_split"],
-        num_workers=config["num_workers"],
-        image_size=config["image_size"],
-    )
-    logger.info(f"Data loaders created: train={len(train_loader.dataset)}, val={len(val_loader.dataset)}")
-    return train_loader, val_loader
+dataset_info = get_dataset_info()
+logger.info(f"Dataset: {dataset_info['size']} samples, keys={dataset_info['sample_keys']}")
 
+train_loader, val_loader = create_data_loaders(
+    root_dir=config["data_dir"],
+    batch_size=config["batch_size"],
+    train_split=config["train_split"],
+    num_workers=config["num_workers"],
+    image_size=config["image_size"],
+)
+logger.info(f"Data loaders created: train={len(train_loader.dataset)}, val={len(val_loader.dataset)}")
 
-def create_model():
-    logger.info("Creating model...")
-    model = HomographyCNN6(
-        input_channels=3,
-        backbone_name=config["backbone"],
-        pretrained=True,
-        dropout_rate=config["dropout_rate"]
-    )
-    logger.info(f"Model created with {count_parameters(model):,} parameters")
-    return model
+model = HomographyCNN6(
+    input_channels=3,
+    backbone_name=config["backbone"],
+    pretrained=True,
+    dropout_rate=config["dropout_rate"]
+)
+logger.info(f"Model created with {count_parameters(model):,} parameters")
 
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+logger.info(f"Using device: {device}")
 
-def train_model(model, train_loader, val_loader):
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    logger.info(f"Using device: {device}")
-    
-    trainer = HomographyTrainer(model, train_loader, val_loader, device)
-    logger.info("Starting training...")
-    trainer.train(config["epochs"])
-    logger.info("Training completed")
-    return trainer
+trainer = HomographyTrainer(model, train_loader, val_loader, device)
+logger.info("Starting training...")
+trainer.train(config["epochs"])
+logger.info("Training completed")
 
+logger.info("Analyzing model...")
+results = analyze_training(trainer)
+logger.info(f"Analysis complete: best_val_loss={results['best_val_loss']:.4f}")
 
-def analyze_model(trainer):
-    logger.info("Analyzing model...")
-    results = analyze_training(trainer)
-    logger.info(f"Analysis complete: best_val_loss={results['best_val_loss']:.4f}")
-    return results
-
-
-def main():
-    logger.info("=" * 50)
-    logger.info("SiaN Training Pipeline")
-    logger.info("=" * 50)
-    
-    dataset_info = get_dataset_info()
-    logger.info(f"Dataset: {dataset_info['size']} samples, keys={dataset_info['sample_keys']}")
-    
-    train_loader, val_loader = create_dataset()
-    model = create_model()
-    trainer = train_model(model, train_loader, val_loader)
-    results = analyze_model(trainer)
-    
-    logger.info("=" * 50)
-    logger.info("Pipeline completed successfully")
-    logger.info("=" * 50)
-    
-    return trainer, results
-
-
-if __name__ == "__main__":
-    main()
+logger.info("=" * 50)
+logger.info("Pipeline completed successfully")
+logger.info("=" * 50)
diff --git a/models/SiaN/src/train.py b/models/SiaN/src/train.py
index 092fed0..b604014 100644
--- a/models/SiaN/src/train.py
+++ b/models/SiaN/src/train.py
@@ -72,6 +72,10 @@ class HomographyTrainer:
                 self.save_checkpoint(epoch, is_best=True)
                 print(f"Best model saved (val loss: {val_metrics['loss']:.4f})")
 
+            if epoch % config["save_every_n_epochs"] == 0:
+                self.save_checkpoint(epoch, is_best=False)
+                print(f"Checkpoint saved at epoch {epoch}")
+
         self.writer.close()
 
     def save_checkpoint(self, epoch, is_best=False):
diff --git a/models/SiaN/src/utils.py b/models/SiaN/src/utils.py
index 32a19da..d83d906 100644
--- a/models/SiaN/src/utils.py
+++ b/models/SiaN/src/utils.py
@@ -12,6 +12,7 @@ config = {
     "dropout_rate": 0.3,
     "backbone": "resnet18",
     "output_dir": r"C:\Users\admin\Projects\autopilot\models\SiaN\runs",
+    "save_every_n_epochs": 15,
 }