vaas 0.1.8

VAAS (Vision-Attention Anomaly Scoring) is a dual-module vision framework for image anomaly detection and localisation.

VAAS — Vision-Attention Anomaly Scoring

VAAS is a research-driven, inference-first dual-module vision framework for image anomaly detection and localisation.

It integrates global attention-based reasoning with patch-level consistency analysis to produce:

• A continuous hybrid anomaly score (S_H)
• Dense spatial anomaly maps
• Interpretable attention overlays

The output is designed to be spatially grounded, explainable, and suitable for research and prototyping workflows.

This package provides the runtime inference pipeline only.
Pretrained model weights are hosted separately on Hugging Face.

Research Paper | GitHub | Hugging Face Models

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Installation

pip install vaas

Optional dependency: PyTorch

VAAS uses lazy loading for PyTorch.

• Importing vaas does not require PyTorch
• PyTorch is required only when running inference

If PyTorch is not installed, VAAS will raise a clear runtime error when inference is invoked.

Install PyTorch (CPU, CUDA, or ROCm) from: https://pytorch.org/get-started/locally/

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Quick Start

from vaas.inference.pipeline import VAASPipeline
from PIL import Image

pipeline = VAASPipeline.from_pretrained(
    repo_id = "OBA-Research/vaas",
    device="cpu",
    alpha=0.5,
    model_variant = "v1-base-df2023" # v1-medium-df2023 and v1-large-df2023 are other model variants you can use.
  )

image = Image.open("image.jpg").convert("RGB")
result = pipeline(image)

print(result["S_H"])

Output format

{
    "S_F": float,           # global feature anomaly score
    "S_P": float,           # patch consistency score
    "S_H": float,           # hybrid anomaly score
    "anomaly_map": ndarray  # shape (224, 224)
}

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Visualisation

Generate qualitative visual explanations combining:

• Patch-level anomaly heatmaps
• Global attention overlays
• A hybrid anomaly score gauge

pipeline.visualize(
    image="image.jpg",
    save_path="vaas_visualization.png",
    mode="all",
    threshold=0.5,
)

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Model Variants

VAAS models are versioned by architecture, scale, and dataset.

Example:

• v1-base-df2023
• v1-medium-df2023
• 1-large-df2023

Each variant is stored as a separate versioned branch on Hugging Face.

See:

• https://github.com/OBA-Research/VAAS
• https://huggingface.co/OBA-Research/vaas

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Reproducibility

Each published model variant contains structured metadata:

• Architecture version
• Dataset
• Dataset fraction -Runtime defaults
• Backbone configuration

This ensures reproducible loading and transparent model lineage.

Intended Use

VAAS is intended for:

• Image anomaly detection
• Visual integrity assessment
• Explainable inspection of irregular regions
• Research on attention-based anomaly scoring
• Digital forensics research
• Prototyping anomaly-aware vision systems

Supports CPU-only inference and GPU acceleration.

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Limitations

• Trained on a single dataset
• Does not classify anomaly types
• Performance may degrade on out-of-distribution imagery

VAAS should not be used as a standalone decision-making system in high-stakes applications.

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Citation

If you use VAAS in your research, please cite both the software and the associated paper as appropriate.

Software Citation

@software{vaas,
  title        = {VAAS: Vision-Attention Anomaly Scoring},
  author       = {Bamigbade, Opeyemi and Scanlon, Mark and Sheppard, John},
  year         = {2025},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.18064355},
  url          = {https://doi.org/10.5281/zenodo.18064355}
}

Paper Citation

@article{bamigbade2025vaas,
  title={VAAS: Vision-Attention Anomaly Scoring for Image Manipulation Detection in Digital Forensics},
  author={Bamigbade, Opeyemi and Scanlon, Mark and Sheppard, John},
  journal={arXiv preprint arXiv:2512.15512},
  year={2025}
}

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License

MIT License

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Maintainers

OBA-Research

• https://github.com/OBA-Research
• https://huggingface.co/OBA-Research