rashidrao/AD_MVTec

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:

@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:

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

Loading a Checkpoint

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

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)

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

@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