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, and production data-drift monitoring.
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
Production data drift
Production data-drift monitoring is available as an optional observer around anomaly detection.
See the Production Data Drift guide for setup, reference generation, metrics, dashboard, and troubleshooting.
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 |
| 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.
Metadata
Release files for anomavision 3.6.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| anomavision-3.6.1.tar.gz | 8.2 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| anomavision-3.6.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 8.4 MB
Release files / anomavision-3.6.1.tar.gz
| Download URL | anomavision-3.6.1.tar.gz |
|---|---|
| Size | 8.2 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / anomavision-3.6.1-py3-none-any.whl
| Download URL | anomavision-3.6.1-py3-none-any.whl |
|---|---|
| Size | 140.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
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PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
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