tigas-metric 0.1.1

Trained Image Generation Authenticity Score - A neural metric for assessing image realism

TIGAS - Trained Image Generation Authenticity Score

TIGAS is a neural network metric for assessing the authenticity and realism of images, designed to distinguish between real/natural images and AI-generated/fake images.

Description

TIGAS provides a continuous score in the range [0, 1]:

• 1.0 — natural/real image
• 0.0 — generated/fake image

Key Features

• Multi-Modal Analysis: combines complementary analysis approaches

  • Perceptual features (multi-scale CNN)
  • Spectral analysis (frequency domain)
  • Statistical consistency (distribution analysis)
  • Local-global coherence

• Fully Differentiable: can be used as

  • Image quality assessment metric
  • Loss function for training generative models
  • Evaluation metric for image generation tasks

• Flexible Deployment:

  • Model-based computation (trained neural network)
  • Component-based computation (without trained model)

Installation

Basic Installation

git clone https://github.com/H1merka/TIGAS.git
cd TIGAS
pip install -r requirements.txt
pip install -e .

With CUDA Support

pip install -r requirements_cuda.txt

Dependencies

Core Dependencies:

• PyTorch >= 2.2.0
• torchvision >= 0.17.0
• NumPy >= 1.24.0
• SciPy >= 1.10.0
• scikit-learn >= 1.3.0
• Pillow >= 10.0.0
• OpenCV >= 4.8.0
• pandas >= 2.0.0

Quick Start

Python API

from tigas import TIGAS, compute_tigas_score
import torch

# Method 1: High-level function
score = compute_tigas_score('image.jpg', checkpoint_path='model.pt')
print(f"TIGAS Score: {score:.4f}")

# Method 2: Object-oriented API
tigas = TIGAS(checkpoint_path='model.pt', img_size=256, device='cuda')
score = tigas('image.jpg')  # Single image
scores = tigas(torch.randn(4, 3, 256, 256))  # Batch
scores = tigas.compute_directory('path/to/images/')  # Directory

# Method 3: Auto-download model from HuggingFace Hub
tigas = TIGAS(auto_download=True)  # Automatically downloads model from Hub
score = tigas('image.jpg')

# Method 4: As a loss function
generated_images = torch.randn(4, 3, 256, 256, requires_grad=True)
scores = tigas(generated_images)
loss = 1.0 - scores.mean()  # Maximize authenticity
loss.backward()

Command Line

# Evaluate single image
python scripts/evaluate.py --image path/to/image.jpg --checkpoint model.pt

# Evaluate directory
python scripts/evaluate.py --image_dir path/to/images/ --checkpoint model.pt --batch_size 32

# With auto-download from HuggingFace Hub
python scripts/evaluate.py --image_dir images/ --auto_download

# With results saving and visualization
python scripts/evaluate.py --image_dir images/ --output results.json --plot

Training

Data Structure

Directory mode:

dataset/
├── real/
│   ├── img1.jpg
│   ├── img2.jpg
│   └── ...
└── fake/
    ├── img1.jpg
    ├── img2.jpg
    └── ...

CSV mode:

dataset/
├── train/
│   ├── images/
│   └── annotations01.csv
├── val/
│   └── ...
└── test/
    └── ...

CSV format: image_path,label (1 — real, 0 — fake)

Dataset Validation (Required Step)

IMPORTANT: Before training, you must validate dataset integrity:

python scripts/validate_dataset.py \
  --dataset_dir /path/to/data \
  --csv_file train.csv \
  --remove_corrupted \
  --update_csv

This will remove corrupted images and update CSV, preventing errors during training.

Running Training

# Fast training (Fast Mode - default, optimized for speed)
python scripts/train_script.py \
  --data_root /path/to/data \
  --epochs 50 \
  --batch_size 16 \
  --img_size 128 \
  --lr 0.0003 \
  --use_amp \
  --output_dir ./checkpoints

# Full training (Full Mode - all branches, higher accuracy)
python scripts/train_script.py \
  --data_root /path/to/data \
  --epochs 50 \
  --batch_size 8 \
  --img_size 256 \
  --lr 0.0001 \
  --use_amp \
  --full_mode \
  --output_dir ./checkpoints

# Training with CSV annotations (recommended)
python scripts/train_script.py \
  --data_root /path/to/data \
  --use_csv \
  --epochs 50 \
  --use_amp

# Resume training from checkpoint (N more epochs)
python scripts/train_script.py \
  --data_root data/ \
  --resume checkpoints/best_model.pt \
  --epochs 10 \
  --lr 0.0001 \
  --reset_lr

# Resume with full LR and scheduler reset
python scripts/train_script.py \
  --data_root data/ \
  --resume checkpoints/best_model.pt \
  --epochs 10 \
  --lr 0.0003 \
  --reset_lr \
  --reset_scheduler

Training Parameters

Parameter	Description	Default
--data_root	Path to data	Required
--epochs	Number of epochs (on resume — N more epochs)	50
--batch_size	Batch size	16
--lr	Learning rate	0.0000125
--use_csv	Use CSV annotations	False
--img_size	Image size	256
--output_dir	Checkpoint directory	./checkpoints
--device	Device (cuda/cpu)	auto
--num_workers	DataLoader workers (0 for Windows)	0
--use_amp	Mixed Precision Training	False
--fast_mode	Fast architecture (optimized)	True
--full_mode	Full architecture (all branches)	False
--resume	Path to checkpoint for resuming	None
--reset_lr	Reset LR on resume	False
--reset_scheduler	Reset scheduler on resume	False

Architecture

TIGASModel

Multi-branch neural network including:

1. Multi-Scale Feature Extractor

   • 4-stage CNN backbone (1/2, 1/4, 1/8, 1/16 resolutions)
   • Preserves high-frequency details for artifact detection
   • EfficientNet-inspired design

2. Spectral Analyzer

   • FFT-based frequency domain analysis
   • Detection of GAN artifacts (checkerboard patterns, unnatural spectra)
   • Radial profile extraction from power spectrum

3. Statistical Moment Estimator

   • Distribution consistency analysis
   • Learnable natural image statistics
   • Moment matching against natural image priors

4. Attention Mechanisms

   • Self-Attention for capturing long-range dependencies
   • Cross-Modal Attention for fusing features from different modalities

5. Adaptive Feature Fusion

   • Learnable weighting of 3 feature streams
   • Combines perceptual, spectral, and statistical features

Project Structure

TIGAS/
├── tigas/                          # Main package
│   ├── __init__.py                # Initialization and exports
│   ├── api.py                     # High-level API (TIGAS class)
│   │
│   ├── models/                    # Neural network architectures
│   │   ├── tigas_model.py        # Main TIGASModel architecture
│   │   ├── feature_extractors.py # Feature extractors
│   │   ├── attention.py          # Attention mechanisms
│   │   ├── layers.py             # Custom layers
│   │   └── constants.py          # Configuration constants
│   │
│   ├── metrics/                   # Metric computation modules
│   │   ├── tigas_metric.py       # Main metric calculator
│   │   └── components.py         # Metric components
│   │
│   ├── data/                      # Data loading and preprocessing
│   │   ├── dataset.py            # Dataset classes
│   │   ├── loaders.py            # DataLoader creation
│   │   └── transforms.py         # Augmentations and transforms
│   │
│   ├── training/                  # Training infrastructure
│   │   ├── trainer.py            # Main trainer class
│   │   ├── losses.py             # Loss functions
│   │   └── optimizers.py         # Optimizers and schedulers
│   │
│   └── utils/                     # Utilities
│       ├── config.py             # Configuration management
│       ├── input_processor.py    # Input data processing
│       └── visualization.py      # Visualization
│
├── scripts/                       # Executable scripts
│   ├── evaluate.py              # Evaluation/inference script
│   ├── example_usage.py          # Usage examples
│   └── train_script.py           # Training script
│
├── setup.py                     # Package configuration
├── requirements.txt             # Dependencies
├── requirements_cuda.txt        # CUDA dependencies
└── LICENSE                      # MIT License

Usage Examples

1. Basic Usage

from tigas import TIGAS

tigas = TIGAS(checkpoint_path='model.pt')
score = tigas('test_image.jpg')
print(f"Authenticity score: {score:.4f}")

import torch

tigas = TIGAS(checkpoint_path='model.pt') image = torch.randn(1, 3, 256, 256) outputs = tigas(image, return_features=True)

score = outputs['score'] features = outputs['features'] print(f"Score: {score.item():.4f}") print(f"Available features: {list(features.keys())}") print(f"Fused features shape: {features['fused'] from tigas import TIGAS import torch

tigas = TIGAS(checkpoint_path='model.pt', device='cuda') images = torch.randn(8, 3, 256, 256) scores = tigas(images) print(f"Mean score: {scores.mean():.4f}")

### 3. Directory Processing

```python
from tigas import TIGAS

tigas = TIGAS(checkpoint_path='model.pt')

# Get scores for all images
results = tigas.compute_directory(
    'path/to/images/',
    return_paths=True,
    batch_size=32
)

for img_path, score in results.items():
    print(f"{img_path}: {score:.4f}")
print(f"Feature dimension: {features.shape}")

4. Using as Loss Function

from tigas import TIGAS

tigas = TIGAS(checkpoint_path='model.pt')

# In generator training loop
generated_images = generator(noise)
authenticity_score = tigas(generated_images)
loss = 1.0 - authenticity_score.mean()
loss.backward()

5. Component-Based Metric

from tigas.metrics import TIGASMetric

metric = TIGASMetric(use_model=False)
score = metric.compute(image_tensor)

Image Requirements

• Formats: JPG, JPEG, PNG, BMP
• Resolution: default 256x256 (configurable)
• Images are automatically resized if needed
• Normalized to [-1, 1] range

Training Features

• Mixed Precision Training: accelerated training with AMP
• Gradient Accumulation: for larger effective batch sizes
• Learning Rate Scheduling: cosine annealing, warmup
• Early Stopping: automatic stopping on overfitting
• TensorBoard Logging: training process visualization
• Checkpoint Management: model saving and loading

License

This project is distributed under the MIT License. See LICENSE for details.

Authors

• Dmitrij Morgenshtern

Links

• GitHub Repository