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