AnomaVision

Simple visual anomaly detection from normal images.

AnomaVision is a production-oriented computer vision toolkit for detecting defects and unusual patterns from normal images.

Supported methods:

• PaDiM
• PatchCore
• EfficientAD

It supports training, evaluation, threshold calibration, ONNX/OpenVINO/TensorRT export where supported, KV260/XModel deployment, production data-drift monitoring, and non-invasive deployment validation.

🚀 Quick start

📦 Install

git clone https://github.com/DeepKnowledge1/AnomaVision.git
cd AnomaVision
uv venv --python 3.11 .venv
source .venv/bin/activate        # Windows: .venv\\Scripts\\Activate.ps1
uv sync --extra cpu

🧠 Train

anomavision train --config config.yml

🔍 Detect

anomavision detect --config config.yml --img_path ./dataset/bottle/test

📤 Export

anomavision export --config config.yml --format onnx

---

🛡️ Deployment validation

Validate exported models before deployment without changing the anomaly-detection algorithm.

It checks model integrity, inference, performance, backend compatibility, and output consistency against a reference model.

anomavision validate `
  --model model.onnx `
  --reference-model model.pt `
  --config config.yml

🛡️ Deployment validation

Before moving an exported model into production, AnomaVision can validate the deployment artifact without changing the underlying anomaly-detection algorithm.

It checks model integrity, inference, performance, supported backend availability, and—when a reference model is supplied—output consistency.

Quick example

anomavision validate `
  --model distributions\\padim\\bottle\\anomav_exp\\model.onnx `
  --config config.yml `
  --runs 20

Typical output includes:

AnomaVision Deployment Validation
────────────────────────────────────
Model: distributions\padim\bottle\anomav_exp\model.onnx
✓ File Exists
✓ Onnx Valid
✓ Onnxruntime Inference
✓ Static Input Shape
Latency: 11.020 ms
FPS:     90.75
....

Supported deployment artifacts include PyTorch, TorchScript, ONNX, TensorRT, OpenVINO, Hailo HEF, and Vitis AI/KV260 XModel.

Note: backend availability means the required runtime components are installed. It does not by itself prove that a specific model has been tested on physical target hardware.

See the Deployment Validation guide for all options, consistency validation, JSON output, backend details, and recommended production usage.

📡 Production data drift

Production data-drift monitoring helps detect changes in the data seen by an anomaly detector after deployment.

It compares production representations against a trusted normal reference population using a rolling window. It can report metrics such as PSI, mean shift, standard-deviation shift, cosine shift, and drift score, with the result available through a dashboard and JSON status file.

The monitor is an observer only: it does not change anomaly scores or localization. Drift is an early-warning signal that should be investigated for causes such as lighting, camera position, preprocessing, or product changes.

Quick example

anomavision detect `
  --config config.yml `
  --model model.onnx `
  --enable-drift-monitoring `
  --drift-reference .\\drift\\reference_embeddings.npy

See the Production Data Drift guide for reference generation, metrics, dashboard, configuration, and troubleshooting.

---

📊 Benchmarks

AnomaVision includes a reproducible benchmark workflow for comparing anomaly-detection performance and runtime characteristics.

The benchmark reports Image AUROC, Pixel AUROC, latency, P95 latency, FPS, model/artifact size, and memory usage. The current comparison script uses a shared evaluation contract so results can be reproduced consistently.

Quick example

Run a single class:

python scripts\\benchmarks\\compare_with_anomalib.py `
  --dataset_path D:\\01-DATA `
  --class_name bottle `
  --algorithms padim `
  --device cpu

Run all available MVTec classes:

python scripts\\benchmarks\\compare_with_anomalib.py `
  --dataset_path D:\\01-DATA `
  --all_classes `
  --algorithms padim `
  --device cpu

With --all_classes, the script automatically discovers supported MVTec class directories present under the dataset path and runs the same AnomaVision-vs-Anomalib comparison for each available class. --class_name and --all_classes are mutually exclusive. Results are written separately under benchmark_results/<algorithm>/<class_name>/.

Historical benchmark results are available for MVTec AD and VisA, including per-class results and visual comparisons. These results are retained for reference; the corrected benchmark should be rerun before making current performance claims.

See the Benchmark guide for the methodology, commands, metrics, and detailed results.

---

⚡ KV260 support

AnomaVision supports a Vitis AI workflow for PaDiM and PatchCore on the AMD/Xilinx Kria KV260. XModel compilation has been validated in the Vitis AI environment; final on-device validation requires the physical hardware.

📚 Documentation

Topic	Guide
Quick start	docs/quickstart.md
Installation	docs/installation.md
CLI / configuration	docs/cli.md, docs/config.md
Python API	docs/api.md
Deployment validation	docs/deployment_validation.md
Production data drift	docs/anomaly_detection_production_data_drift.md
KV260 / XModel	docs/kv260_xmodel.md
Production deployment	docs/production_deployment.md
Benchmarks	docs/benchmark.md
Troubleshooting	docs/troubleshooting.md

📄 License

AnomaVision is released under the MIT License. See LICENSE.