implemented locking in training setup

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"