pyimgano 0.6.30

Enterprise-Grade Visual Anomaly Detection Toolkit with 120+ registry models and 80+ operations

pyimgano

Production-oriented visual anomaly detection (image-level + pixel-level) for industrial inspection.

pyimgano focuses on the practical parts that matter in production:

• Unified model registry (120+ registered model entry points; native implementations + optional backends + aliases)
• Reproducible CLI runs (workbench + reports + per-image JSONL)
• Deploy-friendly inference (pyimgano-infer → JSONL; optional defect masks + connected-component regions)
• Industrial IO (numpy-first, explicit image formats, high-resolution tiling)
• Benchmarking & metrics (image-level + pixel-level; AUROC/AP/AUPRO/SegF1, etc.)
• Data & preprocessing (dataset helpers + preprocessing/augmentation utilities)

Translations: 中文 · 日本語 · 한국어

Contents

• Installation
• Quickstart (CLI)
• Quickstart (Python)
• Synthetic anomaly generation
• Models & discovery
• Industrial outputs (defects)
• Optional dependencies
• Weights & cache policy
• Docs
• Contributing
• License
• Citation

Installation

pip install pyimgano

Note: pip install pyimgano works after publishing to PyPI. Until then, install from source:

git clone https://github.com/skygazer42/pyimgano.git
cd pyimgano
pip install -e ".[dev]"

For the release workflow, see docs/PUBLISHING.md.

Quickstart (CLI)

Fastest offline sanity check

Run a tiny end-to-end demo (creates a minimal custom dataset + runs a suite + exports tables):

pyimgano-demo

Tip: use pyimgano-demo --help to customize suite/sweep/output.

Optional: sanity-check your environment and which baselines would be skipped due to missing extras:

pyimgano-doctor --suite industrial-v4

Optional: run inference + defects export on the demo dataset produced by pyimgano-demo:

pyimgano-infer \
  --model-preset industrial-template-ncc-map \
  --train-dir ./_demo_custom_dataset/train/normal \
  --input ./_demo_custom_dataset/test \
  --defects-preset industrial-defects-fp40 \
  --save-jsonl ./_demo_results.jsonl \
  --save-masks ./_demo_masks

Train (workbench) → export infer_config.json

Start from the provided template and edit dataset paths:

pyimgano-train \
  --config examples/configs/industrial_adapt_defects_fp40.json \
  --export-infer-config

This writes a run directory (under runs/ by default) containing:

• artifacts/infer_config.json (model + threshold + postprocess + defects config)
• report.json and per_image.jsonl (auditable run artifacts)

Optional (deploy bundle): copy a single directory to servers/containers:

pyimgano-train --config cfg.json --export-deploy-bundle

Inference → JSONL (+ optional defect masks/regions)

pyimgano-infer \
  --infer-config /path/to/run_dir/artifacts/infer_config.json \
  --input /path/to/images \
  --defects \
  --save-masks /tmp/pyimgano_masks \
  --save-jsonl /tmp/pyimgano_results.jsonl

Guides:

• docs/WORKBENCH.md (train/export flow)
• docs/CLI_REFERENCE.md (all flags + JSONL schema)
• docs/INDUSTRIAL_INFERENCE.md (tiling + defects + ROI notes)
• docs/FALSE_POSITIVE_DEBUGGING.md (overlays + FP tuning loop)

One-off inference (no workbench)

For quick experiments you can run pyimgano-infer directly from a registered model name:

pyimgano-infer \
  --model vision_patchcore \
  --preset industrial-balanced \
  --device cuda \
  --train-dir /path/to/normal/train_images \
  --calibration-quantile 0.995 \
  --input /path/to/images \
  --include-maps

Notes:

• Pass extra constructor kwargs via --model-kwargs '{"backbone":"wide_resnet50","coreset_sampling_ratio":0.1}'.
• --defects requires anomaly maps. If you don’t pass a fixed --pixel-threshold, provide --train-dir so the default normal_pixel_quantile strategy can calibrate one from normal pixels.
• High-resolution tiling works best with detectors tagged numpy,pixel_map:
  • add --tile-size 512 --tile-stride 384 (see docs/INDUSTRIAL_INFERENCE.md).

Algorithm selection: baseline suites (+ optional small sweeps)

For industrial algorithm selection, pyimgano-benchmark can run curated baseline suites and write a single aggregated report + leaderboard tables.

Discover suites / sweep profiles:

pyimgano-benchmark --list-suites
pyimgano-benchmark --suite-info industrial-v4 --json

pyimgano-benchmark --list-sweeps
pyimgano-benchmark --sweep-info industrial-template-small --json

Run a suite (and export leaderboard.* / skipped.* tables):

pyimgano-benchmark \
  --dataset mvtec \
  --root /path/to/mvtec_ad \
  --category bottle \
  --suite industrial-v4 \
  --device cpu \
  --no-pretrained \
  --suite-export both \
  --output-dir /tmp/pyimgano_suite_run

Optional: run a small grid search (bounded variants per baseline):

pyimgano-benchmark \
  --dataset mvtec \
  --root /path/to/mvtec_ad \
  --category bottle \
  --suite industrial-v4 \
  --suite-sweep industrial-template-small \
  --suite-sweep-max-variants 1 \
  --suite-export csv \
  --output-dir /tmp/pyimgano_suite_sweep_run

Notes:

• Some suite entries are optional and are skipped if extras are missing (with actionable hints like pip install 'pyimgano[skimage]' or pip install 'pyimgano[torch]').
• See docs/CLI_REFERENCE.md for suite output layout and all flags.

Quickstart (Python)

Create a detector from the registry

from pyimgano.models import create_model

detector = create_model(
    "vision_patchcore",
    device="cuda",         # or "cpu"
    contamination=0.1,     # used for threshold defaults when applicable
)

detector.fit(train_paths)                 # normal/reference images
scores = detector.decision_function(test_paths)

Classical baseline (CPU-friendly, no pixel maps)

Classical detectors usually expect a feature_extractor that turns images into 2D features:

from pyimgano.models import create_model
from pyimgano.utils import ImagePreprocessor

extractor = ImagePreprocessor(resize=(224, 224), output_tensor=False)
detector = create_model("vision_ecod", feature_extractor=extractor, contamination=0.1, n_jobs=-1)
detector.fit(train_paths)
scores = detector.decision_function(test_paths)

Numpy-first industrial inference

If you already have decoded frames/images in memory, prefer the explicit IO helpers:

import numpy as np

from pyimgano.inference import calibrate_threshold, infer
from pyimgano.inputs import ImageFormat

train_frames = [np.zeros((64, 64, 3), dtype=np.uint8) for _ in range(8)]
detector.fit(train_frames)
calibrate_threshold(detector, train_frames, input_format=ImageFormat.RGB_U8_HWC, quantile=0.995)

results = infer(
    detector,
    train_frames[:2],
    input_format=ImageFormat.RGB_U8_HWC,
    include_maps=True,
)
print(results[0].score, results[0].label)

Guide: docs/INDUSTRIAL_INFERENCE.md

Synthetic anomaly generation

When real defects are scarce, pyimgano can generate small, controlled synthetic anomalies for smoke tests / robustness checks / pixel-mask pipelines.

CLI: generate a tiny dataset + masks + JSONL manifest:

pyimgano-synthesize \
  --in-dir /path/to/normal_images \
  --out-root ./out_synth_dataset \
  --category synthetic_demo \
  --presets scratch stain tape edge_wear \
  --roi-mask /path/to/roi_mask.png \
  --blend alpha \
  --alpha 0.9 \
  --n-train 50 \
  --n-test-normal 20 \
  --n-test-anomaly 20 \
  --seed 0

Guide: docs/SYNTHETIC_ANOMALY_GENERATION.md

Models & discovery

pyimgano ships 120+ registered model entry points (native implementations + optional backend wrappers + aliases). Some models require optional extras (see below).

Discover models from the CLI:

pyimgano-benchmark --list-models
pyimgano-benchmark --list-models --tags vision,deep
pyimgano-benchmark --model-info vision_patchcore --json

Recommended baselines (practical)

If you’re doing industrial inspection with anomaly maps / defect localization, start here:

Goal	Start with	Notes
Strong pixel localization baseline	vision_patchcore	numpy,pixel_map; great default for MVTec/VisA-style data
Robustness to “noisy normal”	vision_softpatch	numpy,pixel_map; filters outlier patches in the memory bank
Lightweight pixel baseline	vision_padim / vision_spade	numpy,pixel_map; simpler + often easier to tune
Few-shot / small normal set	vision_anomalydino	numpy,pixel_map; may download DINOv2 weights on first run
CPU-only / precomputed features	vision_ecod / vision_copod	fast, parameter-free; typically score-only (no pixel maps)
You already train in anomalib	vision_*_anomalib / vision_anomalib_checkpoint	requires pyimgano[anomalib]; wraps trained checkpoints for evaluation/inference

For a longer discussion, see docs/ALGORITHM_SELECTION_GUIDE.md.

Tags & capabilities

Every registry entry has tags + metadata. Useful tags include:

• classical vs deep
• pixel_map → model exposes anomaly maps (--include-maps, --defects, pixel metrics)
• numpy → model can score RGB uint8 numpy images (needed for tiling + robustness corruptions)

To inspect a model’s constructor signature and supported kwargs:

pyimgano-benchmark --model-info vision_patchcore
pyimgano-benchmark --model-info vision_patchcore --json

References:

• docs/MODEL_INDEX.md (auto-generated model index)
• docs/ALGORITHM_SELECTION_GUIDE.md (how to pick a baseline)
• docs/SOTA_ALGORITHMS.md and docs/DEEP_LEARNING_MODELS.md (deeper dives)

Industrial outputs (defects)

When --defects is enabled, pyimgano-infer derives defect structures from the anomaly map:

• binary defect mask (optional artifact output)
• connected-component regions (area / bbox / per-region score)
• optional ROI gating and binary morphology (open/close/fill holes)

This is intended for downstream inspection systems that need more than a single anomaly score.

Docs:

• docs/CLI_REFERENCE.md (JSONL schema + examples)
• docs/INDUSTRIAL_INFERENCE.md (defects + ROI configuration)
• docs/FALSE_POSITIVE_DEBUGGING.md (how to tune down false positives)

Optional dependencies

pyimgano is usable with the default dependencies, but some models/backends require extras:

pip install "pyimgano[torch]"       # torch + torchvision (deep backends, TorchScript export)
pip install "pyimgano[onnx]"        # onnx + onnxruntime (+ onnxscript for torch.onnx.export)
pip install "pyimgano[openvino]"    # OpenVINO runtime (deployment)
pip install "pyimgano[skimage]"     # scikit-image baselines (SSIM/phase-corr/HOG/LBP/Gabor, etc.)
pip install "pyimgano[numba]"       # numba-accelerated baselines
pip install "pyimgano[viz]"         # matplotlib/seaborn plots
pip install "pyimgano[diffusion]"   # diffusion-based methods
pip install "pyimgano[clip]"        # OpenCLIP backends
pip install "pyimgano[faiss]"       # faster kNN for memory-bank methods
pip install "pyimgano[anomalib]"    # anomalib checkpoint wrappers (inference-first)
pip install "pyimgano[backends]"    # clip + faiss + anomalib
pip install "pyimgano[all]"         # everything (dev/docs/viz + torch/onnx/openvino + backends/diffusion)

See also: docs/OPTIONAL_DEPENDENCIES.md (extras map + recommended combos).

Weights & cache policy

• pyimgano does not ship model weights inside the wheel.
• When models download weights (torchvision / OpenCLIP / HuggingFace), weights are cached on disk by upstream libraries.
• Common cache env vars you may want to set on servers/containers:
  • TORCH_HOME
  • HF_HOME / TRANSFORMERS_CACHE
  • XDG_CACHE_HOME

Docs

Start here:

• docs/QUICKSTART.md
• docs/WORKBENCH.md
• docs/CLI_REFERENCE.md

Production/industrial:

• docs/INDUSTRIAL_INFERENCE.md (numpy-first IO, tiling, postprocess, defects export)
• docs/ROBUSTNESS_BENCHMARK.md (drift corruptions + evaluation)
• docs/MANIFEST_DATASET.md (JSONL manifest datasets)

Reference:

• docs/MODEL_INDEX.md
• docs/PREPROCESSING.md
• docs/EVALUATION_AND_BENCHMARK.md
• docs/PUBLISHING.md (PyPI release flow)

Contributing

See CONTRIBUTING.md.

License

MIT. See LICENSE.

Citation

@software{pyimgano2026,
  author = {PyImgAno Contributors},
  title = {pyimgano: Production-oriented Visual Anomaly Detection},
  year = {2026},
  url = {https://github.com/skygazer42/pyimgano}
}