Upload TIGAS model checkpoint (2025-12-21 19:32:48)

LICENSE

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+MIT License
+
+Copyright (c) 2025 Morgenshtern Dmitrij
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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 ---
 license: mit
+language:
+  - en
+library_name: pytorch
+tags:
+  - image-classification
+  - computer-vision
+  - ai-detection
+  - deepfake-detection
+  - pytorch
+  - image-quality
+datasets:
+  - custom
+metrics:
+  - accuracy
+pipeline_tag: image-classification
+model-index:
+  - name: TIGAS
+    results:
+      - task:
+          type: image-classification
+          name: AI-Generated Image Detection
+        metrics:
+          - type: accuracy
+            value: 0.656
+            name: Validation Accuracy
+          - type: loss
+            value: 0.308
+            name: Validation Loss
 ---
+
+# TIGAS - Trained Image Generation Authenticity Score
+
+<div align="center">
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
+[![PyTorch](https://img.shields.io/badge/PyTorch-2.0+-red.svg)](https://pytorch.org/)
+
+**Neural network metric for detecting AI-generated images**
+
+[GitHub Repository](https://github.com/H1merka/TIGAS) • [Documentation](https://github.com/H1merka/TIGAS/blob/main/README_eng.md)
+
+</div>
+
+## Model Description
+
+TIGAS (Trained Image Generation Authenticity Score) is a multi-branch neural network designed to distinguish between real/natural images and AI-generated/fake images. It provides a continuous score in the range [0, 1]:
+
+- **1.0** — Natural/Real image
+- **0.0** — AI-Generated/Fake image
+
+### Architecture
+
+The model uses a **Full Mode** architecture with three complementary analysis branches:
+
+1. **Perceptual Features** — Multi-scale CNN extracting visual patterns at 1/2, 1/4, 1/8, 1/16 resolutions
+2. **Spectral Analysis** — FFT-based frequency domain analysis for detecting GAN artifacts
+3. **Statistical Consistency** — Distribution analysis and moment estimation
+4. **Cross-Modal Attention** — Fuses features from all branches for final prediction
+
+### Model Specifications
+
+| Property | Value |
+|----------|-------|
+| **Parameters** | ~18.9M |
+| **Input Size** | 256×256 RGB |
+| **Output** | Single score [0, 1] |
+| **Architecture** | TIGASModel (Full Mode) |
+| **File Size** | ~217 MB |
+
+## Training Details
+
+### Dataset
+
+- **Training samples**: 128,776 images
+- **Validation samples**: 14,167 images
+- **Test samples**: 14,126 images
+- **Total**: 157,069 images
+- **Class balance**: ~46% real, ~54% fake
+
+### Training Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Epochs | 3 |
+| Batch Size | 8 |
+| Image Size | 256×256 |
+| Learning Rate | 1e-4 (with warmup) |
+| Optimizer | AdamW |
+| Scheduler | Cosine Annealing |
+| Mixed Precision | Enabled (AMP) |
+| Hardware | NVIDIA RTX 3050 Ti (4GB VRAM) |
+
+### Training Results
+
+| Epoch | Train Loss | Val Loss | Val Accuracy |
+|-------|------------|----------|--------------|
+| 0 | 0.4115 | 0.3262 | 61.46% |
+| 1 | 0.3707 | 0.3099 | 65.09% |
+| 2 | 0.3506 | **0.3079** | **65.55%** |
+
+**Note**: This is an early checkpoint after 3 epochs of training. The model is still learning and accuracy will improve with more training epochs (recommended: 30-50 epochs for production use).
+
+## Usage
+
+### Installation
+
+```bash
+pip install torch torchvision
+pip install huggingface-hub
+
+# Clone the TIGAS repository
+git clone https://github.com/H1merka/TIGAS.git
+cd TIGAS
+pip install -e .
+```
+
+### Quick Start
+
+```python
+from tigas import TIGAS
+
+# Initialize with auto-download from HuggingFace Hub
+tigas = TIGAS(auto_download=True)
+
+# Evaluate single image
+score = tigas('path/to/image.jpg')
+print(f"Authenticity Score: {score:.4f}")
+
+# Interpretation
+if score > 0.7:
+    print("Likely REAL (High Confidence)")
+elif score > 0.5:
+    print("Probably REAL (Medium Confidence)")
+elif score > 0.3:
+    print("Probably FAKE (Medium Confidence)")
+else:
+    print("Likely FAKE (High Confidence)")
+```
+
+### Batch Processing
+
+```python
+import torch
+from tigas import TIGAS
+
+tigas = TIGAS(auto_download=True, device='cuda')
+
+# Process batch of images
+images = torch.randn(8, 3, 256, 256)  # [B, C, H, W]
+scores = tigas(images)
+print(f"Mean score: {scores.mean():.4f}")
+```
+
+### Directory Processing
+
+```python
+from tigas import TIGAS
+
+tigas = TIGAS(auto_download=True)
+
+# Evaluate all images in directory
+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}")
+```
+
+### As a Differentiable Loss Function
+
+```python
+from tigas import TIGAS
+
+tigas = TIGAS(auto_download=True)
+
+# In generator training loop
+generated_images = generator(noise)
+authenticity_score = tigas(generated_images)
+
+# Maximize authenticity (make images look more real)
+loss = 1.0 - authenticity_score.mean()
+loss.backward()
+```
+
+### Command Line
+
+```bash
+# Single image evaluation
+python scripts/evaluate.py --image test.jpg --auto_download
+
+# Directory evaluation
+python scripts/evaluate.py --image_dir images/ --auto_download --batch_size 32
+
+# Save results
+python scripts/evaluate.py --image_dir images/ --output results.json --plot
+```
+
+## Loading the Model Manually
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+# Download checkpoint
+checkpoint_path = hf_hub_download(
+    repo_id="H1merka/TIGAS",
+    filename="best_model.pt"
+)
+
+# Load checkpoint
+checkpoint = torch.load(checkpoint_path, map_location='cpu')
+
+# Access model weights
+model_state_dict = checkpoint['model_state_dict']
+epoch = checkpoint['epoch']
+best_val_loss = checkpoint['best_val_loss']
+
+print(f"Loaded model from epoch {epoch}")
+print(f"Best validation loss: {best_val_loss:.4f}")
+```
+
+## Checkpoint Contents
+
+The checkpoint file (`best_model.pt`) contains:
+
+| Key | Description |
+|-----|-------------|
+| `model_state_dict` | Model weights |
+| `optimizer_state_dict` | Optimizer state (for resume training) |
+| `scheduler_state_dict` | LR scheduler state |
+| `scaler_state_dict` | AMP GradScaler state |
+| `epoch` | Training epoch number |
+| `global_step` | Global training step |
+| `best_val_loss` | Best validation loss achieved |
+| `train_history` | Training loss history |
+| `val_history` | Validation metrics history |
+
+## Limitations
+
+- **Early Training Stage**: This checkpoint is from early training (3 epochs). For production use, train for 30-50+ epochs.
+- **Dataset Bias**: Performance may vary on images from generators not represented in the training set.
+- **Resolution Dependency**: Best results at 256×256. Other resolutions are automatically resized.
+- **Adversarial Robustness**: Not specifically hardened against adversarial attacks.
+
+## Intended Use
+
+### Primary Use Cases
+
+- Detecting AI-generated images in content moderation
+- Evaluating quality of generative models
+- Research on image authenticity
+- Integration as a loss function for training more realistic generators
+
+### Out-of-Scope Use
+
+- Legal evidence without human verification
+- Sole basis for content removal decisions
+- Real-time processing of high-volume streams (without optimization)
+
+## Citation
+
+```bibtex
+@software{tigas2025,
+  title = {TIGAS: Trained Image Generation Authenticity Score},
+  author = {Morgenshtern, Dmitrij},
+  year = {2025},
+  url = {https://github.com/H1merka/TIGAS},
+  license = {MIT}
+}
+```
+
+## License
+
+This model is released under the [MIT License](LICENSE).
+
+## Contact
+
+- **GitHub**: [H1merka/TIGAS](https://github.com/H1merka/TIGAS)
+- **Issues**: [GitHub Issues](https://github.com/H1merka/TIGAS/issues)

best_model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c263c630853cbcc15a2158f72439d3dcd20fbae6b008898bf8dd51be4a5f1ed8
+size 227239163

config.json

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+{
+  "model_type": "tigas",
+  "architectures": ["TIGASModel"],
+  "task_type": "image-classification",
+  "framework": "pytorch",
+  "model_config": {
+    "img_size": 256,
+    "in_channels": 3,
+    "feature_dim": 256,
+    "base_channels": 32,
+    "num_scales": 4,
+    "fast_mode": false
+  },
+  "training_config": {
+    "epochs_trained": 3,
+    "batch_size": 8,
+    "learning_rate": 0.0001,
+    "optimizer": "adamw",
+    "scheduler": "cosine",
+    "mixed_precision": true,
+    "warmup_epochs": 5
+  },
+  "dataset_info": {
+    "train_samples": 128776,
+    "val_samples": 14167,
+    "test_samples": 14126,
+    "total_samples": 157069,
+    "real_ratio": 0.458,
+    "fake_ratio": 0.542
+  },
+  "metrics": {
+    "best_val_loss": 0.3079,
+    "best_val_accuracy": 0.6555,
+    "final_train_loss": 0.3506
+  },
+  "input_spec": {
+    "type": "image",
+    "channels": 3,
+    "height": 256,
+    "width": 256,
+    "normalization": {
+      "mean": [0.5, 0.5, 0.5],
+      "std": [0.5, 0.5, 0.5],
+      "range": [-1, 1]
+    }
+  },
+  "output_spec": {
+    "type": "score",
+    "range": [0, 1],
+    "interpretation": {
+      "1.0": "real/natural image",
+      "0.0": "fake/generated image"
+    }
+  },
+  "checkpoint_info": {
+    "format": "pytorch",
+    "keys": [
+      "model_state_dict",
+      "optimizer_state_dict", 
+      "scheduler_state_dict",
+      "scaler_state_dict",
+      "epoch",
+      "global_step",
+      "best_val_loss",
+      "train_history",
+      "val_history"
+    ]
+  },
+  "version": "0.1.0",
+  "library_name": "tigas",
+  "github_repo": "https://github.com/H1merka/TIGAS"
+}