implemented locking in training setup

backend/0815/27/exp_infer.py

@@ -0,0 +1,28 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_27_train.json"
+        self.val_ann = "coco_project_27_valid.json"
+        self.test_ann = "coco_project_27_test.json"
+        self.num_classes = 80
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-s.pth'
+
+        
+        self.depth = 1.0
+        self.width = 1.0
+        self.input_size = (640.0, 640.0)
+        self.mosaic_scale = (0.1, 2.0)
+        self.random_size = (10, 20)
+        self.test_size = (640.0, 640.0)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/asdf/5/exp_infer.py

@@ -0,0 +1,25 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_5_train.json"
+        self.val_ann = "coco_project_5_valid.json"
+        self.test_ann = "coco_project_5_test.json"
+        self.num_classes = 4
+        self.depth = 1.0
+        self.width = 1.0
+        self.input_size = (640, 640)
+        self.mosaic_scale = (0.1, 2)
+        self.random_size = (10, 20)
+        self.test_size = (640, 640)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/data/README.md

@@ -0,0 +1,140 @@
+# YOLOX Base Configuration System
+
+## Overview
+
+This directory contains base experiment configurations for YOLOX models. These configurations define "protected" parameters that are preserved during transfer learning from COCO-pretrained models.
+
+## How It Works
+
+### Transfer Learning Flow
+
+1. **COCO Transfer Learning** (`transfer_learning = 'coco'`):
+   - Loads base configuration from `data/yolox_*.py` based on `selected_model`
+   - Base parameters are **protected** and used as defaults
+   - User settings from the form only override what's explicitly set
+   - Result: Best of both worlds - proven COCO settings + your customizations
+
+2. **Sketch/Custom Training** (`transfer_learning = 'sketch'`):
+   - No base configuration loaded
+   - Uses only user-defined parameters from the training form
+   - Full control over all settings
+
+### Base Configuration Files
+
+- `yolox_s.py` - YOLOX-Small (depth=0.33, width=0.50)
+- `yolox_m.py` - YOLOX-Medium (depth=0.67, width=0.75)
+- `yolox_l.py` - YOLOX-Large (depth=1.0, width=1.0)
+- `yolox_x.py` - YOLOX-XLarge (depth=1.33, width=1.25)
+
+### Protected Parameters
+
+These parameters are defined in base configs and **preserved** unless explicitly overridden:
+
+**Model Architecture:**
+- `depth` - Model depth multiplier
+- `width` - Model width multiplier
+- `activation` - Activation function (silu)
+
+**Training Hyperparameters:**
+- `basic_lr_per_img` - Learning rate per image
+- `scheduler` - LR scheduler (yoloxwarmcos)
+- `warmup_epochs` - Warmup epochs
+- `max_epoch` - Maximum training epochs
+- `no_aug_epochs` - No augmentation epochs
+- `min_lr_ratio` - Minimum LR ratio
+
+**Optimizer:**
+- `momentum` - SGD momentum
+- `weight_decay` - Weight decay
+
+**Augmentation:**
+- `mosaic_prob` - Mosaic probability
+- `mixup_prob` - Mixup probability
+- `hsv_prob` - HSV augmentation probability
+- `flip_prob` - Flip probability
+- `degrees` - Rotation degrees
+- `translate` - Translation
+- `shear` - Shear
+- `mosaic_scale` - Mosaic scale range
+- `mixup_scale` - Mixup scale range
+- `enable_mixup` - Enable mixup
+
+**Input/Output:**
+- `input_size` - Training input size
+- `test_size` - Testing size
+- `random_size` - Random size range
+
+**Evaluation:**
+- `eval_interval` - Evaluation interval
+- `print_interval` - Print interval
+
+## Customizing Base Configurations
+
+### Adding a New Model
+
+Create a new file `data/yolox_MODELNAME.py`:
+
+```python
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Base configuration for YOLOX-MODELNAME
+
+class BaseExp:
+    """Base experiment configuration for YOLOX-MODELNAME"""
+    
+    # Define protected parameters
+    depth = 1.0
+    width = 1.0
+    # ... other parameters
+```
+
+### Modifying Parameters
+
+Edit the corresponding `yolox_*.py` file and update the `BaseExp` class attributes.
+
+**Example:** To change YOLOX-S max epochs:
+```python
+# In data/yolox_s.py
+class BaseExp:
+    max_epoch = 500  # Changed from 300
+    # ... other parameters
+```
+
+## Parameter Priority
+
+The merge logic follows this priority (highest to lowest):
+
+1. **User form values** (if explicitly set, not None)
+2. **Base config values** (if transfer_learning='coco')
+3. **Default fallbacks** (hardcoded minimums)
+
+## Example
+
+### COCO Transfer Learning
+```
+User sets in form: max_epoch=100, depth=0.5
+Base config (yolox_s.py) has: depth=0.33, width=0.50, max_epoch=300
+
+Result: depth=0.5 (user override), width=0.50 (base), max_epoch=100 (user override)
+```
+
+### Sketch Training
+```
+User sets in form: max_epoch=100, depth=0.5
+No base config loaded
+
+Result: depth=0.5 (user), max_epoch=100 (user), width=1.0 (default fallback)
+```
+
+## Debugging
+
+To see which base config was loaded, check Flask logs:
+```
+Loaded base config for yolox-s: ['depth', 'width', 'activation', ...]
+```
+
+If base config fails to load:
+```
+Warning: Could not load base config for yolox-s: [error message]
+Falling back to custom settings only
+```

backend/data/__init__.py

@@ -0,0 +1 @@
+# Base experiment configurations for YOLOX models

backend/data/test_base_configs.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+"""
+Test script to demonstrate base configuration loading for YOLOX models
+"""
+
+import sys
+import os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+
+from services.generate_yolox_exp import load_base_config
+
+def test_base_configs():
+    """Test loading all base configurations"""
+    models = ['yolox-s', 'yolox-m', 'yolox-l', 'yolox-x']
+    
+    print("=" * 80)
+    print("YOLOX Base Configuration Test")
+    print("=" * 80)
+    
+    for model in models:
+        print(f"\n{'='*80}")
+        print(f"Model: {model.upper()}")
+        print(f"{'='*80}")
+        
+        try:
+            config = load_base_config(model)
+            
+            # Group parameters by category
+            arch_params = ['depth', 'width', 'activation']
+            training_params = ['max_epoch', 'warmup_epochs', 'basic_lr_per_img', 'scheduler', 
+                             'no_aug_epochs', 'min_lr_ratio']
+            optimizer_params = ['momentum', 'weight_decay']
+            augmentation_params = ['mosaic_prob', 'mixup_prob', 'hsv_prob', 'flip_prob', 
+                                 'degrees', 'translate', 'shear', 'mosaic_scale', 
+                                 'mixup_scale', 'enable_mixup']
+            input_params = ['input_size', 'test_size', 'random_size']
+            eval_params = ['eval_interval', 'print_interval']
+            
+            print("\n[Architecture]")
+            for param in arch_params:
+                if param in config:
+                    print(f"  {param:25s} = {config[param]}")
+            
+            print("\n[Training Hyperparameters]")
+            for param in training_params:
+                if param in config:
+                    print(f"  {param:25s} = {config[param]}")
+            
+            print("\n[Optimizer]")
+            for param in optimizer_params:
+                if param in config:
+                    print(f"  {param:25s} = {config[param]}")
+            
+            print("\n[Data Augmentation]")
+            for param in augmentation_params:
+                if param in config:
+                    print(f"  {param:25s} = {config[param]}")
+            
+            print("\n[Input/Output]")
+            for param in input_params:
+                if param in config:
+                    print(f"  {param:25s} = {config[param]}")
+            
+            print("\n[Evaluation]")
+            for param in eval_params:
+                if param in config:
+                    print(f"  {param:25s} = {config[param]}")
+            
+            print(f"\n✓ Successfully loaded {len(config)} parameters")
+            
+        except Exception as e:
+            print(f"✗ Error loading config: {e}")
+    
+    print("\n" + "="*80)
+    print("Test Complete")
+    print("="*80)
+
+if __name__ == '__main__':
+    test_base_configs()

backend/data/yolox_l.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Base configuration for YOLOX-L model
+# These parameters are preserved during transfer learning from COCO
+
+class BaseExp:
+    """Base experiment configuration for YOLOX-L"""
+    
+    # Model architecture (protected - always use these for yolox-l)
+    depth = 1.0
+    width = 1.0
+    
+    scheduler = "yoloxwarmcos"
+
+    activation = "silu"

backend/data/yolox_m.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Base configuration for YOLOX-M model
+# These parameters are preserved during transfer learning from COCO
+
+class BaseExp:
+    """Base experiment configuration for YOLOX-M"""
+    
+    # Model architecture (protected - always use these for yolox-m)
+    depth = 0.67
+    width = 0.75
+    
+    scheduler = "yoloxwarmcos"
+
+    activation = "silu"

backend/data/yolox_s.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Base configuration for YOLOX-S model
+# These parameters are preserved during transfer learning from COCO
+
+class BaseExp:
+    """Base experiment configuration for YOLOX-S"""
+    
+    # Model architecture (protected - always use these for yolox-s)
+    depth = 0.33
+    width = 0.50
+
+    scheduler = "yoloxwarmcos"
+
+    activation = "silu"
+    
+

backend/data/yolox_x.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Base configuration for YOLOX-X model
+# These parameters are preserved during transfer learning from COCO
+
+class BaseExp:
+    """Base experiment configuration for YOLOX-X"""
+    
+    # Model architecture (protected - always use these for yolox-x)
+    depth = 1.33
+    width = 1.25
+    
+    scheduler = "yoloxwarmcos"
+
+    activation = "silu"

backend/routes/api.py

@@ -529,3 +529,13 @@ def delete_training_project(id):
     except Exception as error:
         db.session.rollback()
         return jsonify({'message': 'Failed to delete training project', 'error': str(error)}), 500
+
+@api_bp.route('/base-config/<model_name>', methods=['GET'])
+def get_base_config(model_name):
+    """Get base configuration for a specific YOLOX model"""
+    try:
+        from services.generate_yolox_exp import load_base_config
+        config = load_base_config(model_name)
+        return jsonify(config)
+    except Exception as error:
+        return jsonify({'message': f'Failed to load base config for {model_name}', 'error': str(error)}), 404

backend/services/generate_yolox_exp.py

@@ -1,8 +1,31 @@
 import os
 import shutil
+import importlib.util
 from models.training import Training
 from models.TrainingProject import TrainingProject
 
+def load_base_config(selected_model):
+    """Load base configuration for a specific YOLOX model"""
+    model_name = selected_model.lower().replace('-', '_').replace('.pth', '')
+    base_config_path = os.path.join(os.path.dirname(__file__), '..', 'data', f'{model_name}.py')
+    
+    if not os.path.exists(base_config_path):
+        raise Exception(f'Base configuration not found for model: {model_name} at {base_config_path}')
+    
+    # Load the module dynamically
+    spec = importlib.util.spec_from_file_location(f"base_config_{model_name}", base_config_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    
+    # Extract all attributes from BaseExp class
+    base_exp = module.BaseExp()
+    base_config = {}
+    for attr in dir(base_exp):
+        if not attr.startswith('_'):
+            base_config[attr] = getattr(base_exp, attr)
+    
+    return base_config
+
 def generate_yolox_exp(training_id):
     """Generate YOLOX exp.py file"""
     # Fetch training row from DB
@@ -13,26 +36,14 @@ def generate_yolox_exp(training_id):
     if not training:
         raise Exception(f'Training not found for trainingId or project_details_id: {training_id}')
     
-    # If transfer_learning is 'coco', copy default exp.py
+    # If transfer_learning is 'coco', generate exp using base config + custom settings
     if training.transfer_learning == 'coco':
-        selected_model = training.selected_model.lower().replace('-', '_')
-        exp_source_path = f'/home/kitraining/Yolox/YOLOX-main/exps/default/{selected_model}.py'
-        
-        if not os.path.exists(exp_source_path):
-            raise Exception(f'Default exp.py not found for model: {selected_model} at {exp_source_path}')
-        
-        # Copy to project folder
-        project_details_id = training.project_details_id
-        project_folder = os.path.join(os.path.dirname(__file__), '..', f'project_23/{project_details_id}')
-        os.makedirs(project_folder, exist_ok=True)
-        
-        exp_dest_path = os.path.join(project_folder, 'exp.py')
-        shutil.copyfile(exp_source_path, exp_dest_path)
-        return {'type': 'default', 'expPath': exp_dest_path}
+        exp_content = generate_yolox_inference_exp(training_id, use_base_config=True)
+        return {'type': 'custom', 'expContent': exp_content}
     
     # If transfer_learning is 'sketch', generate custom exp.py
     if training.transfer_learning == 'sketch':
-        exp_content = generate_yolox_inference_exp(training_id)
+        exp_content = generate_yolox_inference_exp(training_id, use_base_config=False)
         return {'type': 'custom', 'expContent': exp_content}
     
     raise Exception(f'Unknown transfer_learning type: {training.transfer_learning}')
@@ -53,8 +64,14 @@ def save_yolox_exp(training_id, out_path):
     else:
         raise Exception('Unknown expResult type or missing content')
 
-def generate_yolox_inference_exp(training_id, options=None):
-    """Generate inference exp.py using DB values"""
+def generate_yolox_inference_exp(training_id, options=None, use_base_config=False):
+    """Generate inference exp.py using DB values
+    
+    Args:
+        training_id: The training/project_details ID
+        options: Optional overrides for data paths
+        use_base_config: If True, load base config and only override with user-defined values
+    """
     if options is None:
         options = {}
     
@@ -90,14 +107,69 @@ def generate_yolox_inference_exp(training_id, options=None):
     except Exception as e:
         print(f'Could not determine num_classes from TrainingProject.classes: {e}')
     
-    depth = options.get('depth', training.depth or 1.00)
-    width = options.get('width', training.width or 1.00)
-    input_size = options.get('input_size', training.input_size or [640, 640])
-    mosaic_scale = options.get('mosaic_scale', training.mosaic_scale or [0.1, 2])
-    random_size = options.get('random_size', [10, 20])
-    test_size = options.get('test_size', training.test_size or [640, 640])
-    exp_name = options.get('exp_name', 'inference_exp')
-    enable_mixup = options.get('enable_mixup', False)
+    # Initialize config dictionary
+    config = {}
+    
+    # If using base config (transfer learning from COCO), load protected parameters first
+    if use_base_config and training.selected_model:
+        try:
+            base_config = load_base_config(training.selected_model)
+            config.update(base_config)
+            print(f'Loaded base config for {training.selected_model}: {list(base_config.keys())}')
+        except Exception as e:
+            print(f'Warning: Could not load base config for {training.selected_model}: {e}')
+            print('Falling back to custom settings only')
+    
+    # Override with user-defined values from training table (only if they exist and are not None)
+    user_overrides = {
+        'depth': training.depth,
+        'width': training.width,
+        'input_size': training.input_size,
+        'mosaic_scale': training.mosaic_scale,
+        'test_size': training.test_size,
+        'enable_mixup': training.enable_mixup,
+        'max_epoch': training.max_epoch,
+        'warmup_epochs': training.warmup_epochs,
+        'warmup_lr': training.warmup_lr,
+        'basic_lr_per_img': training.basic_lr_per_img,
+        'scheduler': training.scheduler,
+        'no_aug_epochs': training.no_aug_epochs,
+        'min_lr_ratio': training.min_lr_ratio,
+        'ema': training.ema,
+        'weight_decay': training.weight_decay,
+        'momentum': training.momentum,
+        'print_interval': training.print_interval,
+        'eval_interval': training.eval_interval,
+        'test_conf': training.test_conf,
+        'nms_thre': training.nms_thre,
+        'mosaic_prob': training.mosaic_prob,
+        'mixup_prob': training.mixup_prob,
+        'hsv_prob': training.hsv_prob,
+        'flip_prob': training.flip_prob,
+        'degrees': training.degrees,
+        'translate': training.translate,
+        'shear': training.shear,
+        'mixup_scale': training.mixup_scale,
+        'activation': training.activation,
+    }
+    
+    # Only override if value is explicitly set (not None)
+    for key, value in user_overrides.items():
+        if value is not None:
+            config[key] = value
+    
+    # Apply any additional options overrides
+    config.update(options)
+    
+    # Set defaults for any missing required parameters
+    config.setdefault('depth', 1.00)
+    config.setdefault('width', 1.00)
+    config.setdefault('input_size', [640, 640])
+    config.setdefault('mosaic_scale', [0.1, 2])
+    config.setdefault('random_size', [10, 20])
+    config.setdefault('test_size', [640, 640])
+    config.setdefault('enable_mixup', False)
+    config.setdefault('exp_name', 'inference_exp')
     
     # Build exp content
     exp_content = f'''#!/usr/bin/env python3
@@ -115,7 +187,7 @@ class Exp(MyExp):
         self.data_dir = "{data_dir}"
         self.train_ann = "{train_ann}"
         self.val_ann = "{val_ann}"
-        self.test_ann = "coco_project_{training_id}_test.json"
+        self.test_ann = "{test_ann}"
         self.num_classes = {num_classes}
 '''
     
@@ -127,26 +199,30 @@ class Exp(MyExp):
             exp_content += f"        self.pretrained_ckpt = r'{yolox_base_dir}/pretrained/{selected_model}.pth'\n"
     
     # Format arrays
-    input_size_str = ', '.join(map(str, input_size)) if isinstance(input_size, list) else str(input_size)
-    mosaic_scale_str = ', '.join(map(str, mosaic_scale)) if isinstance(mosaic_scale, list) else str(mosaic_scale)
-    random_size_str = ', '.join(map(str, random_size)) if isinstance(random_size, list) else str(random_size)
-    test_size_str = ', '.join(map(str, test_size)) if isinstance(test_size, list) else str(test_size)
+    def format_value(val):
+        if isinstance(val, (list, tuple)):
+            return '(' + ', '.join(map(str, val)) + ')'
+        elif isinstance(val, bool):
+            return str(val)
+        elif isinstance(val, str):
+            return f'"{val}"'
+        else:
+            return str(val)
     
-    exp_content += f'''        self.depth = {depth}
-        self.width = {width}
-        self.input_size = ({input_size_str})
-        self.mosaic_scale = ({mosaic_scale_str})
-        self.random_size = ({random_size_str})
-        self.test_size = ({test_size_str})
-        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
-        self.enable_mixup = {str(enable_mixup)}
+    # Add all config parameters to exp
+    for key, value in config.items():
+        if key not in ['exp_name']:  # exp_name is handled separately
+            exp_content += f"        self.{key} = {format_value(value)}\n"
+    
+    # Add exp_name at the end (uses dynamic path)
+    exp_content += f'''        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
 '''
     
     return exp_content
 
 def save_yolox_inference_exp(training_id, out_path, options=None):
     """Save inference exp.py to custom path"""
-    exp_content = generate_yolox_inference_exp(training_id, options)
+    exp_content = generate_yolox_inference_exp(training_id, options, use_base_config=False)
     with open(out_path, 'w') as f:
         f.write(exp_content)
     return out_path

backend/services/push_yolox_exp.py

@@ -5,32 +5,88 @@ from database.database import db
 def push_yolox_exp_to_db(settings):
     """Save YOLOX settings to database"""
     normalized = dict(settings)
-    
-    # Map 'act' from frontend to 'activation' for DB
-    if 'act' in normalized:
-        normalized['activation'] = normalized['act']
-        del normalized['act']
-    
-    # Convert 'on'/'off' to boolean for save_history_ckpt
-    if isinstance(normalized.get('save_history_ckpt'), str):
-        normalized['save_history_ckpt'] = normalized['save_history_ckpt'] == 'on'
-    
-    # Convert comma-separated strings to arrays
+
+    # Map common frontend aliases to DB column names
+    alias_map = {
+        'act': 'activation',
+        'nmsthre': 'nms_thre',
+        'select_model': 'selected_model'
+    }
+    for a, b in alias_map.items():
+        if a in normalized and b not in normalized:
+            normalized[b] = normalized.pop(a)
+
+    # Convert 'on'/'off' or 'true'/'false' strings to boolean for known boolean fields
+    for bool_field in ['save_history_ckpt', 'ema', 'enable_mixup']:
+        if bool_field in normalized:
+            val = normalized[bool_field]
+            if isinstance(val, str):
+                normalized[bool_field] = val.lower() in ('1', 'true', 'on')
+            else:
+                normalized[bool_field] = bool(val)
+
+    # Convert comma-separated strings to arrays for JSON fields
     for key in ['input_size', 'test_size', 'mosaic_scale', 'mixup_scale']:
-        if isinstance(normalized.get(key), str):
-            arr = [float(v.strip()) for v in normalized[key].split(',')]
+        if key in normalized and isinstance(normalized[key], str):
+            parts = [p.strip() for p in normalized[key].split(',') if p.strip()]
+            try:
+                arr = [float(p) for p in parts]
+            except Exception:
+                arr = parts
             normalized[key] = arr[0] if len(arr) == 1 else arr
-    
-    # Find TrainingProjectDetails for this project
-    details = TrainingProjectDetails.query.filter_by(project_id=normalized['project_id']).first()
+
+    # Ensure we have a TrainingProjectDetails row for project_id
+    project_id = normalized.get('project_id')
+    if not project_id:
+        raise Exception('Missing project_id in settings')
+    details = TrainingProjectDetails.query.filter_by(project_id=project_id).first()
     if not details:
-        raise Exception(f'TrainingProjectDetails not found for project_id {normalized["project_id"]}')
-    
+        raise Exception(f'TrainingProjectDetails not found for project_id {project_id}')
     normalized['project_details_id'] = details.id
-    
+
+    # Filter normalized to only columns that exist on the Training model
+    valid_cols = {c.name: c for c in Training.__table__.columns}
+    filtered = {}
+    for k, v in normalized.items():
+        if k in valid_cols:
+            col_type = valid_cols[k].type.__class__.__name__
+            # Try to coerce types for numeric/boolean columns
+            try:
+                if 'Integer' in col_type:
+                    if v is None or v == '':
+                        filtered[k] = None
+                    else:
+                        filtered[k] = int(float(v))
+                elif 'Float' in col_type:
+                    if v is None or v == '':
+                        filtered[k] = None
+                    else:
+                        filtered[k] = float(v)
+                elif 'Boolean' in col_type:
+                    if isinstance(v, str):
+                        filtered[k] = v.lower() in ('1', 'true', 'on')
+                    else:
+                        filtered[k] = bool(v)
+                elif 'JSON' in col_type:
+                    filtered[k] = v
+                elif 'LargeBinary' in col_type:
+                    # If a file path was passed, store its bytes; otherwise store raw bytes
+                    if isinstance(v, str):
+                        try:
+                            filtered[k] = v.encode('utf-8')
+                        except Exception:
+                            filtered[k] = None
+                    else:
+                        filtered[k] = v
+                else:
+                    filtered[k] = v
+            except Exception:
+                # If conversion fails, just assign raw value
+                filtered[k] = v
+
     # Create DB row
-    training = Training(**normalized)
+    training = Training(**filtered)
     db.session.add(training)
     db.session.commit()
-    
+
     return training