Style Transfer Models for AR/visionOS

End-to-end repository for training, optimizing, and deploying neural style transfer models for real-time AR experiences on iOS and visionOS devices.

🎯 Overview

This repository provides a complete pipeline for:

Dataset Preparation: Download and preprocess training datasets (COCO, WikiArt, custom)
Model Training: PyTorch and TensorFlow implementations of fast neural style transfer
Core ML Conversion: Export models to Apple's Core ML format with optimization
Validation & Benchmarking: Test performance, accuracy, and visual quality
Deployment Manifest: Generate metadata for seamless app integration

Key Features:

✅ Dual framework support (PyTorch & TensorFlow)
✅ Optimized for Apple Neural Engine
✅ Configurable quantization and compute units
✅ Real-time inference (<50ms on modern devices)
✅ StyleDescriptor manifest for easy app integration
✅ Comprehensive validation and testing tools

📁 Repository Structure

style-transfer-models/
├── datasets/               # Dataset management
│   ├── scripts/
│   │   └── download_prepare.py
│   ├── sample/            # Sample images for testing
│   └── README.md
├── training/              # Training pipelines
│   ├── pytorch/
│   │   ├── train.py
│   │   ├── model.py
│   │   └── utils.py
│   ├── tensorflow/
│   │   ├── train.py
│   │   ├── model.py
│   │   └── utils.py
│   └── README.md
├── conversion/            # Core ML conversion
│   ├── convert_coreml.py
│   ├── validate_coreml.ipynb
│   └── README.md
├── models/                # Trained models
│   ├── exported/          # .mlmodel files
│   └── checkpoints/       # Training checkpoints
├── manifest/              # StyleDescriptor manifest
│   ├── styles.json
│   ├── styles_schema.json
│   └── generate_manifest.py
├── tests/                 # Unit tests
├── requirements.txt       # Python dependencies
├── LICENSE               # MIT License
└── README.md             # This file

🚀 Quick Start

Prerequisites

Python 3.9 or higher
macOS (recommended for Core ML conversion)
8GB+ RAM
GPU (optional but recommended for training)

Installation

Clone the repository:

git clone https://github.com/yourusername/style-transfer-models.git
cd style-transfer-models

Create a virtual environment:

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

For Apple Silicon Macs, TensorFlow Metal acceleration will be automatically installed.

30-Second Training Test

Train a style transfer model with sample data:

# Prepare sample dataset
cd datasets/scripts
python download_prepare.py \
    --dataset custom \
    --source-dir ../sample/content \
    --output-dir ../processed/sample

# Train PyTorch model (2 epochs, fast)
cd ../../training/pytorch
python train.py \
    --content-dir ../../datasets/processed/sample/train \
    --style-image ../../datasets/sample/styles/sci-fi.jpg \
    --style-name sci-fi \
    --epochs 2 \
    --batch-size 4

# Convert to Core ML
cd ../../conversion
python convert_coreml.py \
    --framework pytorch \
    --model-path ../models/checkpoints/sci-fi_best.pth \
    --output-path ../models/exported/sci-fi.mlmodel \
    --style-name sci-fi \
    --quantize

📖 Detailed Workflows

1. Dataset Preparation

Download COCO Dataset

cd datasets/scripts
python download_prepare.py --dataset coco --data-dir ../

This downloads the COCO 2017 training set (~18GB).

Preprocess Images

python download_prepare.py \
    --dataset custom \
    --source-dir ../coco/train2017 \
    --output-dir ../processed/coco_256 \
    --target-size 256 256 \
    --split-ratios 0.8 0.1 0.1

Prepare Style Images

Organize your style images by category:

datasets/wikiart_styles/
├── sci-fi/
│   ├── style_001.jpg
│   └── style_002.jpg
├── fantasy/
│   └── style_001.jpg
└── modern/
    └── style_001.jpg

Process them:

python download_prepare.py \
    --dataset custom \
    --source-dir ../wikiart_styles \
    --output-dir ../processed/styles \
    --prepare-styles

See datasets/README.md for more details.

2. Model Training

PyTorch Training

cd training/pytorch

python train.py \
    --content-dir ../../datasets/processed/coco_256/train \
    --val-dir ../../datasets/processed/coco_256/val \
    --style-image ../../datasets/processed/styles/sci-fi/sci-fi_001.jpg \
    --style-name sci-fi \
    --epochs 10 \
    --batch-size 8 \
    --image-size 256 \
    --lr 1e-3 \
    --content-weight 1.0 \
    --style-weight 1e5 \
    --tv-weight 1e-6 \
    --save-interval 2

Key Arguments:

--content-weight: Controls content preservation (default: 1.0)
--style-weight: Controls style strength (default: 1e5)
--tv-weight: Total variation loss for smoothness (default: 1e-6)
--lr: Learning rate (default: 1e-3)

TensorFlow Training

cd training/tensorflow

python train.py \
    --content-dir ../../datasets/processed/coco_256/train \
    --val-dir ../../datasets/processed/coco_256/val \
    --style-image ../../datasets/processed/styles/fantasy/fantasy_001.jpg \
    --style-name fantasy \
    --epochs 10 \
    --batch-size 8 \
    --image-size 256

Performance Tips:

Use --num-workers 4 for faster data loading
Enable --lr-scheduler for better convergence
Use --early-stopping to prevent overfitting
Reduce --batch-size if running out of memory

See training/README.md for advanced options.

3. Core ML Conversion

Convert PyTorch Model

cd conversion

python convert_coreml.py \
    --framework pytorch \
    --model-path ../models/checkpoints/sci-fi_best.pth \
    --output-path ../models/exported/sci-fi.mlmodel \
    --style-name sci-fi \
    --input-size 256 256 \
    --compute-units ALL \
    --quantize

Convert TensorFlow Model

python convert_coreml.py \
    --framework tensorflow \
    --model-path ../models/checkpoints/fantasy_best.h5 \
    --output-path ../models/exported/fantasy.mlmodel \
    --style-name fantasy \
    --input-size 256 256 \
    --compute-units CPU_AND_NE \
    --quantize

Compute Units Options:

ALL: CPU, GPU, and Neural Engine (recommended)
CPU_AND_GPU: CPU and GPU only
CPU_AND_NE: CPU and Neural Engine (best for modern devices)
CPU_ONLY: CPU only (slowest)

Quantization:

--quantize: Apply float16 quantization (~50% size reduction, minimal quality loss)

4. Model Validation

Open the Jupyter notebook for interactive validation:

cd conversion
jupyter notebook validate_coreml.ipynb

The notebook provides:

Model metadata inspection
Inference testing with sample images
Performance benchmarking (FPS, latency)
Visual quality assessment
Automated validation reports

See conversion/README.md for details.

5. Generate Manifest

Create the styles.json manifest for app integration:

cd manifest

# Generate from exported models
python generate_manifest.py generate \
    --models-dir ../models/exported \
    --output styles.json \
    --version 1.0.0

# Validate manifest
python generate_manifest.py validate \
    --manifest styles.json \
    --schema styles_schema.json

The manifest maps each model to a StyleDescriptor with:

Model path and metadata
Input/output specifications
Performance metrics
Recommended compute units

📊 Performance Benchmarks

Typical performance on Apple devices (256x256 input):

Device	Neural Engine	Avg Latency	FPS	Notes
iPhone 15 Pro	Yes	~35ms	28	Quantized, ALL compute units
iPhone 14	Yes	~45ms	22	Quantized, ALL compute units
iPad Pro M2	Yes	~30ms	33	Quantized, CPU_AND_NE
Vision Pro	Yes	~40ms	25	Real-time AR capable
Mac M1/M2	Yes	~25ms	40	Desktop testing

Benchmarks with float16 quantization and optimized compute units.

🎨 StyleDescriptor Integration

The generated manifest/styles.json provides a standardized interface for your iOS/visionOS app:

{
  "id": "sci-fi",
  "displayName": "Sci-Fi",
  "description": "Futuristic aesthetic...",
  "category": "sci-fi",
  "modelPath": "../models/exported/sci-fi.mlmodel",
  "inputSize": {"width": 256, "height": 256},
  "computeUnits": "ALL",
  "performance": {
    "avgInferenceMs": 45.0,
    "fps": 22.0
  },
  "metadata": {
    "framework": "pytorch",
    "quantized": true
  }
}

App Integration Example (Swift):

struct StyleDescriptor: Codable {
    let id: String
    let displayName: String
    let modelPath: String
    let inputSize: ImageSize
    let computeUnits: String
    // ... other fields
}

// Load manifest
let manifestURL = Bundle.main.url(forResource: "styles", withExtension: "json")
let manifest = try JSONDecoder().decode(StyleManifest.self, from: Data(contentsOf: manifestURL))

// Use styles in your app
for style in manifest.styles {
    let model = try MLModel(contentsOf: URL(fileURLWithPath: style.modelPath))
    // Apply style transfer...
}

🧪 Testing

Run unit tests:

pytest tests/ -v --cov=.

Test coverage includes:

Dataset loading and preprocessing
Model architecture validation
Conversion pipeline integrity
Manifest generation and validation

📝 Configuration Best Practices

For Mobile Deployment

Input Size: 256x256 is optimal for real-time performance
Quantization: Always use float16 quantization
Compute Units: Use ALL or CPU_AND_NE
Model Size: Target <10MB for quick loading

For High Quality

Input Size: 512x512 or higher
Quantization: Optional (sacrifices size for quality)
Training Epochs: 10-20 epochs
Style Weight: Experiment with 1e4 to 1e6

For Fast Training

Batch Size: Maximize based on GPU memory
Learning Rate: Start with 1e-3, use scheduler
Validation: Use smaller validation set
Checkpointing: Save every 2-5 epochs

🤝 Contributing

Contributions are welcome! Please:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-style)
Commit your changes (git commit -m 'Add amazing style')
Push to the branch (git push origin feature/amazing-style)
Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Third-Party Attributions

COCO Dataset: CC BY 4.0
VGG Weights: CC BY
Style Transfer Architecture: Based on Johnson et al. (2016), MIT License

See LICENSE for complete attribution details.

🔗 Resources

Papers

Perceptual Losses for Real-Time Style Transfer (Johnson et al., 2016)
A Neural Algorithm of Artistic Style (Gatys et al., 2015)

Documentation

Related Projects

🐛 Troubleshooting

Common Issues

Out of Memory During Training:

# Reduce batch size
python train.py --batch-size 2 ...

# Reduce image size
python train.py --image-size 128 ...

Slow Training:

# Use more workers
python train.py --num-workers 8 ...

# Enable GPU
python train.py --device cuda ...

Core ML Conversion Errors:

# Make sure coremltools is up to date
pip install --upgrade coremltools

# Try different compute units
python convert_coreml.py --compute-units CPU_ONLY ...

Getting Help

Open an issue
Check existing discussions
Review FAQ

📧 Contact

For questions, feature requests, or collaboration:

GitHub Issues: Create an issue
Email: your.email@example.com

Made with ❤️ for the AR/ML community

⭐ Star this repo if you find it useful!

Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
.github/workflows		.github/workflows
conversion		conversion
datasets		datasets
manifest		manifest
models		models
output/starry-night		output/starry-night
style-transfer-models		style-transfer-models
tests		tests
training		training
venv-py311		venv-py311
.gitignore		.gitignore
LICENSE		LICENSE
QUICKSTART.md		QUICKSTART.md
README.md		README.md
TRAINING_COMPLETE.md		TRAINING_COMPLETE.md
requirements.txt		requirements.txt

License

kolvian/room-visualize-ml

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