Abschluss-Projekt/TRANSFER_LEARNING_FEATURE.md

Transfer Learning Base Configuration Feature

Overview

This feature implements automatic loading of base configurations when "Train on COCO" transfer learning is selected. Base parameters are loaded from backend/data/ based on the selected YOLOX model, and these protected fields are displayed as greyed out and non-editable in the frontend.

Components Modified/Created

Backend

1. Base Configuration Files (backend/data/)

• yolox_s.py - Base config for YOLOX-Small (depth=0.33, width=0.50)
• yolox_m.py - Base config for YOLOX-Medium (depth=0.67, width=0.75)
• yolox_l.py - Base config for YOLOX-Large (depth=1.0, width=1.0)
• yolox_x.py - Base config for YOLOX-XLarge (depth=1.33, width=1.25)

Each file contains a BaseExp class with protected parameters:

• Model architecture (depth, width, activation)
• Training hyperparameters (max_epoch, warmup_epochs, scheduler, etc.)
• Optimizer settings (momentum, weight_decay)
• Augmentation probabilities (mosaic_prob, mixup_prob, etc.)
• Input/output sizes

2. Services (backend/services/generate_yolox_exp.py)

New functions:

• load_base_config(selected_model) - Dynamically loads base config using importlib
• Modified generate_yolox_inference_exp() to support use_base_config parameter
• Base config merging logic: base → user overrides → defaults

Behavior:

• transfer_learning='coco' → loads base config + applies user overrides
• transfer_learning='sketch' → uses only user-defined values
• Protected parameters from base config are preserved unless explicitly overridden

3. API Routes (backend/routes/api.py)

New endpoint:

@api_bp.route('/base-config/<model_name>', methods=['GET'])
def get_base_config(model_name):

Returns the base configuration JSON for a specific YOLOX model.

Frontend

1. HTML (edit-training.html)

Added:

• Info banner to indicate when base config is active
• CSS styles for disabled input fields (grey background, not-allowed cursor)
• Visual feedback showing which model's base config is loaded

Banner HTML:

<div id="base-config-info" style="display:none; ...">
    🔒 Base Configuration Active
    Protected parameters are loaded from [model] base config
</div>

CSS for disabled fields:

.setting-row input[type="number"]:disabled,
.setting-row input[type="text"]:disabled,
.setting-row input[type="checkbox"]:disabled {
    background: #d3d3d3 !important;
    color: #666 !important;
    cursor: not-allowed !important;
    border: 1px solid #999 !important;
}

2. JavaScript (js/start-training.js)

New functionality:

1. Base Config Loading:

   function loadBaseConfig(modelName)
   

   Fetches base config from /api/base-config/<model>

2. Apply Base Config:

   function applyBaseConfig(config, isCocoMode)
   

   • Applies config values to form fields
   • Disables and greys out protected fields
   • Shows/hides info banner
   • Adds tooltips to disabled fields

3. Update Transfer Learning Mode:

   function updateTransferLearningMode()
   

   • Monitors changes to "Transfer Learning" dropdown
   • Monitors changes to "Select Model" dropdown
   • Loads appropriate base config when COCO mode is selected
   • Clears base config when sketch mode is selected

4. Form Submission Enhancement:

   • Temporarily enables disabled fields before submission
   • Ensures protected parameters are included in form data
   • Re-disables fields after collection

Protected Fields List:

const protectedFields = [
    'depth', 'width', 'act', 'max_epoch', 'warmup_epochs', 'warmup_lr',
    'scheduler', 'no_aug_epochs', 'min_lr_ratio', 'ema', 'weight_decay',
    'momentum', 'input_size', 'mosaic_scale', 'test_size', 'enable_mixup',
    'mosaic_prob', 'mixup_prob', 'hsv_prob', 'flip_prob', 'degrees',
    'translate', 'shear', 'mixup_scale', 'print_interval', 'eval_interval'
];

User Flow

1. Normal Custom Training (Train from sketch)

• User selects model: e.g., "YOLOX-s"
• User selects "Train from sketch"
• All fields are editable (white background)
• User can customize all parameters
• Submission uses user-defined values only

2. COCO Transfer Learning (Train on COCO)

• User selects model: e.g., "YOLOX-s"
• User selects "Train on coco"
• Automatic actions:
  1. Frontend calls /api/base-config/YOLOX-s
  2. Base config is loaded and applied
  3. Protected fields become greyed out and disabled
  4. Green info banner appears: "🔒 Base Configuration Active"
  5. Tooltip on hover: "Protected by base config for YOLOX-s. Switch to 'Train from sketch' to customize."
• User can still edit non-protected fields
• On submit: both base config values AND user overrides are sent to backend
• Backend generates exp.py with merged settings

3. Switching Models

• User changes from "YOLOX-s" to "YOLOX-l" (while in COCO mode)
• Frontend automatically:
  1. Fetches new base config for YOLOX-l
  2. Updates field values (depth=1.0, width=1.0, etc.)
  3. Updates banner text to show "YOLOX-l"
• Protected parameters update to match new model's architecture

Testing

Manual Test Steps:

1. Test Base Config Loading:

   cd backend/data
   python test_base_configs.py
   

   Should display all parameters for yolox-s, yolox-m, yolox-l, yolox-x

2. Test API Endpoint:

   # Start Flask server
   cd backend
   python app.py
   
   # In another terminal:
   curl http://localhost:3000/api/base-config/YOLOX-s
   

   Should return JSON with depth, width, activation, etc.

3. Test Frontend:

   • Open edit-training.html?id=<project_id> in browser
   • Select "YOLOX-s" model
   • Select "Train on coco" → fields should grey out
   • Select "Train from sketch" → fields should become editable
   • Switch to "YOLOX-l" (in COCO mode) → values should update
   • Open browser console and check for: Applied base config. Protected fields: depth, width, ...

4. Test Form Submission:

   • With COCO mode active (fields greyed out)
   • Click "Save Parameters"
   • Check browser Network tab → POST to /api/yolox-settings
   • Verify payload includes protected parameters (depth, width, etc.)
   • Check Flask logs for successful save

Expected Behaviors:

✅ COCO mode + YOLOX-s:

• depth: 0.33 (greyed out)
• width: 0.50 (greyed out)
• activation: silu (greyed out)
• Info banner visible

✅ COCO mode + YOLOX-l:

• depth: 1.0 (greyed out)
• width: 1.0 (greyed out)
• activation: silu (greyed out)

✅ Sketch mode:

• All fields white/editable
• No info banner
• User can set any values

Documentation

• backend/data/README.md - Complete guide on base config system
• backend/data/test_base_configs.py - Test script for base configs

Benefits

1. Proven defaults: Users start with battle-tested COCO pretraining settings
2. Prevents mistakes: Can't accidentally break model architecture by changing depth/width
3. Easy customization: Can still override specific parameters if needed
4. Visual feedback: Clear indication of which fields are protected
5. Model-specific: Each model (s/m/l/x) has appropriate architecture defaults
6. Flexible: Can easily add new models by creating new base config files

Future Enhancements

• Add "Override" button next to protected fields to unlock individual parameters
• Show diff comparison between base config and user overrides
• Add validation warnings if user tries values far from base config ranges
• Export final merged config as preview before training