ash12321/deep-svdd-anomaly-detection

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

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

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:

# 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

@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