# 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
```