---
license: mit
language:
- en
tags:
- anomaly-detection
- computer-vision
- vae-gan
- industrial-ai
- mvtec
- pytorch
library_name: pytorch
pipeline_tag: image-classification
datasets:
- mvtec-ad
---

# VAE-GAN Checkpoints for MVTec Anomaly Detection

This repository contains pre-trained VAE-GAN model checkpoints for visual anomaly detection on the MVTec AD dataset.

## Overview

The models were trained in a one-class anomaly detection setting using only normal training images. During inference, each input image is reconstructed by the VAE-GAN model, and anomaly scores are computed from the reconstruction error between the input and reconstructed image.

These checkpoints are useful for:

- Reconstruction-based anomaly detection
- Threshold selection experiments
- Multi-point threshold evaluation
- Anomaly localization
- Explainable anomaly detection
- Baseline comparison with AE, VAE, PatchCore, PaDiM, and other methods

## Dataset

The checkpoints are trained on MVTec AD object and texture categories.

Reference:

```bibtex
@article{bergmann2021mvtec,
  title={The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection},
  author={Bergmann, Paul and Batzner, Kilian and Fauser, Michael and Sattlegger, David and Steger, Carsten},
  journal={International Journal of Computer Vision},
  year={2021}
}
```

## Available Checkpoints

| Category | Checkpoint |
|-----------|-----------|
| Bottle | model_bottle_64.pt |
| Cable | model_cable_64.pt |
| Capsule | model_capsule_64.pt |
| Carpet | model_carpet_64.pt |
| Grid | model_grid_64.pt |
| Hazelnut | model_hazelnut_64.pt |
| Leather | model_leather_64.pt |
| Metal Nut | model_metal_nut_64.pt |
| Pill | model_pill_64.pt |
| Screw | model_screw_64.pt |
| Tile | model_tile_64.pt |
| Toothbrush | model_toothbrush_64.pt |
| Transistor | model_transistor_64.pt |
| Wood | model_wood_64.pt |
| Zipper | model_zipper_64.pt |

## Model Details

| Property | Value |
|-----------|-----------|
| Model | VAE-GAN |
| Training Setting | One-Class Learning |
| Training Data | Normal Samples Only |
| Input Size | 128 × 128 × 3 |
| Latent Dimension | 64 |
| Framework | PyTorch |

## Checkpoint Structure

Each checkpoint contains:

```python
{
    "encoder_state_dict": ...,
    "decoder_state_dict": ...,
    "discriminator_state_dict": ...
}
```

## Loading a Checkpoint

```python
import torch

checkpoint = torch.load(
    "model_bottle_64.pt",
    map_location="cpu",
    weights_only=False
)

encoder.load_state_dict(checkpoint["encoder_state_dict"])
decoder.load_state_dict(checkpoint["decoder_state_dict"])
discriminator.load_state_dict(checkpoint["discriminator_state_dict"])

encoder.eval()
decoder.eval()
discriminator.eval()
```

## Example Anomaly Score

```python
with torch.no_grad():
    mu, logvar = encoder(image)
    z = reparameterize(mu, logvar)
    reconstruction = decoder(z)

anomaly_map = torch.abs(image - reconstruction).mean(dim=1)
anomaly_score = anomaly_map.mean().item()
```

## Device Support

The checkpoints can be loaded on:

- CPU
- NVIDIA CUDA GPUs
- Apple Silicon (MPS)

```python
import torch

if torch.cuda.is_available():
    device = "cuda"
elif torch.backends.mps.is_available():
    device = "mps"
else:
    device = "cpu"
```

## Intended Use

This repository is intended for research on:

- Visual Anomaly Detection
- Reconstruction-Based Anomaly Scoring
- Threshold Calibration
- Multi-Point Thresholding
- Explainable Anomaly Detection
- Industrial Inspection Systems

## Limitations

- Models are trained on resized 128×128 images.
- Performance depends on preprocessing, anomaly score design, and threshold selection.
- These checkpoints are intended for research purposes and should be validated before deployment in industrial or safety-critical environments.

## Citation

```bibtex
@misc{rao2026vaeganmvtec,
  title={VAE-GAN Checkpoints for MVTec Anomaly Detection},
  author={Rao, Rashid},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/rashidrao/AnomalyDetection}
}
```

## Author

Rashid Rao  
Industrial PhD Researcher  
University of Turin, Italy  

Research Areas:

- Explainable AI (XAI)
- Visual Anomaly Detection
- Trustworthy AI
- Industrial AI Systems