---
license: apache-2.0
tags:
- anomaly-detection
- deep-svdd
- computer-vision
- pytorch
datasets:
- cifar10
- cifar100
metrics:
- accuracy
- precision
- recall
- f1
library_name: pytorch
---

# Deep SVDD Anomaly Detection Model

A Deep Support Vector Data Description (Deep SVDD) model trained for anomaly detection on natural images.

## Model Description

This model uses a ResNet-based encoder to learn a hypersphere representation of normal data. Images are classified as anomalies based on their distance from the center of this hypersphere.

**Training Data:**
- CIFAR-10 (50,000 images)
- CIFAR-100 (50,000 images)  
- STL-10 (100,000 images)

**Architecture:**
- ResNet-based encoder with residual blocks
- Latent dimension: 512
- Input size: 128x128x3

## Performance

Evaluated on CIFAR-10 (normal) vs MNIST (anomaly):

| Metric | Value |
|--------|-------|
| Accuracy | 87.00% |
| Precision | 80.33% |
| Recall | 98.00% |
| F1 Score | 88.29% |

**Anomaly Score Separation:** 6.15x (anomalies score ~6x higher than normal images)

## Usage

### Quick Start

```python
from model import DeepSVDDAnomalyDetector

# Load model
detector = DeepSVDDAnomalyDetector.from_pretrained('.')

# Predict on image
score, is_anomaly = detector.predict('test.jpg')
print(f"Anomaly Score: {score:.6f}")
print(f"Is Anomaly: {is_anomaly}")
```

### Download from Hugging Face

```python
from huggingface_hub import snapshot_download

# Download model
model_path = snapshot_download(repo_id="ash12321/deep-svdd-anomaly-detection")

# Load
detector = DeepSVDDAnomalyDetector.from_pretrained(model_path)
```

### Threshold Options

The model supports three threshold presets:

```python
# Optimal F1 (default, recommended)
detector.set_threshold('optimal')  # threshold = 0.001618

# 95th percentile (balanced)
detector.set_threshold('95th')     # threshold = 0.008501

# 99th percentile (conservative, fewer false positives)
detector.set_threshold('99th')     # threshold = 0.015922
```

**Threshold Comparison:**

| Threshold | Accuracy | Precision | Recall | Use Case |
|-----------|----------|-----------|--------|----------|
| Optimal (0.0016) | 87% | 80% | 98% | **Recommended** - Best F1 |
| 95th (0.0085) | 75% | 95% | 53% | Few false alarms |
| 99th (0.0159) | 68% | 100% | 35% | Zero false alarms |

## Training Details

- **Framework:** PyTorch 2.9.1+cu128
- **Precision:** bfloat16 mixed precision
- **Optimizer:** Fused AdamW
- **Hardware:** NVIDIA H200
- **Epochs:** 50
- **Batch Size:** 1536

## Model Files

- `deepsvdd_model.pth` - Model weights and hypersphere parameters
- `thresholds.pkl` - All threshold configurations
- `thresholds.json` - Thresholds in JSON format
- `config.json` - Model configuration
- `model.py` - Inference code
- `requirements.txt` - Python dependencies

## Citation

```bibtex
@misc{deep-svdd-anomaly-detection,
  title={Deep SVDD Anomaly Detection Model},
  author={ash12321},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/ash12321/deep-svdd-anomaly-detection}
}
```

## License

Apache 2.0

## Limitations

- Trained on natural images (CIFAR-10/100, STL-10)
- Best suited for detecting distribution shift in natural images
- May not generalize well to very different domains
- Requires RGB images, resized to 128x128

## Intended Use

**Primary Use:** Anomaly detection in natural image datasets

**Good for:**
- Quality control in image datasets
- Detecting out-of-distribution samples
- Filtering unusual/corrupted images
- Content moderation

**Not recommended for:**
- Critical safety systems without human review
- Domains very different from natural images