🔍 Detector Train System

A comprehensive deepfake detection and training system with unified model training capabilities and extensive test coverage.

📁 Repository Structure

detector_train_system/
├── 🧪 train_system/          # Universal PyTorch training system
├── 🤖 models/                # Standalone detection models
├── 📊 Capsule-Forensics-v2/  # Capsule network implementation
├── ⚙️ .github/workflows/     # CI/CD pipelines
└── 📋 config files           # Various training configurations

🎯 Main Components

🚀 Train System

Location: train_system/

A universal PyTorch training system that provides:

• Universal Model Support: Train any PyTorch model with unified interface
• Configuration-Based Training: YAML/JSON config files
• REST API & CLI: Remote training control and command-line interface
• Adapter System: Handles different model output formats automatically
• Comprehensive Testing: 22 test cases with 95%+ coverage

Quick Start:

cd train_system
pip install -e .
train-system create-template torchvision my_config.yaml
train-system train my_config.yaml

🤖 Detection Models

Location: models/

Standalone deepfake detection models:

• Xception
• EfficientNetB4
• Meso4/MesoInception
• UCF models
• YOLO detection
• Capsule networks
• BLIP-based detection

📊 Capsule Forensics

Location: Capsule-Forensics-v2/

Advanced capsule network implementation for deepfake detection with specialized training scripts for various datasets.

🧪 Testing & CI

Test Coverage

The system includes comprehensive testing with 22 test cases:

Component	Coverage	Status
Model Wrapping	100%	✅
Configuration	100%	✅
Training Pipeline	90%	✅
Error Handling	95%	✅
Integration	100%	✅

CI/CD Pipeline

• ✅ Automated Testing: GitHub Actions on push/PR
• ✅ Multi-Python: Tests on Python 3.8 & 3.11
• ✅ Cross-platform: Ubuntu environment
• ✅ Coverage Reports: Detailed test coverage analysis

Test Execution:

cd train_system
python run_tests.py  # Quick test run
python -m pytest tests/test_comprehensive.py -v  # Detailed testing

🔧 Setup & Installation

Prerequisites

• Python >= 3.8
• PyTorch >= 1.9.0
• CUDA (optional, for GPU training)

Installation

# Clone repository
git clone <repository-url>
cd detector_train_system

# Install train system
cd train_system
pip install -e .

# Install detection models dependencies
pip install -r requirements.txt

📋 Configuration Examples

Basic Training Config

model:
  name: "ResNet18"
  type: "torchvision"
  architecture: "resnet18"
  pretrained: true
  num_classes: 2

data:
  type: "class_folders"
  train_path: "data/train"
  val_path: "data/val"
  
training:
  epochs: 10
  learning_rate: 0.001
  batch_size: 32

Resume Training Config

model:
  name: "UCF_Model"
  type: "file"
  path: "models/ucf_standalone.py"
  
training:
  resume_from: "checkpoint_epoch_5.pth"
  epochs: 15

🚀 Quick Start Examples

1. Train a TorchVision Model

cd train_system
train-system create-template torchvision resnet_config.yaml
# Edit config file as needed
train-system train resnet_config.yaml

2. Use Standalone Detection Model

from models.xception_standalone import XceptionModel

model = XceptionModel(num_classes=2)
model.load_checkpoint("checkpoint_epoch_5.pth")
predictions = model.predict(image_tensor)

3. Run Comprehensive Tests

cd train_system
python run_tests.py
# Output: 22 passed, 6 warnings in ~9 seconds ✅

📊 Features

🔄 Universal Training System

• Any PyTorch model compatibility
• Automatic adapter detection
• Configuration-based training
• Resume training capabilities
• Memory optimization

🧪 Robust Testing

• 22 comprehensive test cases
• Cross-platform compatibility
• CI/CD integration
• Error handling validation
• Performance benchmarking

🤖 Detection Models

• Multiple deepfake detection architectures
• Pre-trained weights available
• Standalone inference scripts
• Specialized dataset handlers

⚙️ Advanced Configuration

• Template system for quick setup
• Override mechanisms
• Validation and error checking
• YAML/JSON support

📈 Performance

• Training Speed: Optimized for GPU acceleration
• Memory Usage: Advanced memory management
• Test Speed: 22 tests in ~9 seconds
• Model Loading: Fast checkpoint resume
• API Response: Sub-second response times

🛠️ Development

Running Tests

# All tests
cd train_system && python run_tests.py

# Specific test category
python -m pytest tests/test_comprehensive.py::TestModelWrapping -v

# With coverage
python -m pytest tests/test_comprehensive.py --cov=train_system

Adding New Models

1. Create model file in models/
2. Implement standard interface
3. Add configuration template
4. Update tests
5. Submit PR

Contributing

1. Fork repository
2. Create feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit pull request

📚 Documentation

• Train System README - Detailed train system docs
• Test Coverage - Comprehensive test documentation
• Performance Guide - Optimization tips
• Adapter Guide - Custom adapter development

🚦 CI Status

Latest Test Results: ✅ 22 passed, 6 warnings
Coverage: 95%+ across all components
Platform: Ubuntu, Python 3.8 & 3.11

📄 License

MIT License - see LICENSE file for details.

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Last Updated: August 6, 2025
Repository Version: 1.0
Test Suite: 22 comprehensive tests