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Free commercial product · Apache-2.0 code

YOLOZU — Framework-agnostic vision model evaluation toolkit

Bring your own inference. Export once. Evaluate fairly.

YOLOZU is a commercial product from ToppyMicroServices OÜ and is provided free of charge. Its repository code is licensed under Apache-2.0. It validates and fairly evaluates existing vision predictions through a stable predictions.json interface contract instead of tying reports to one training or inference framework.

Start with the docs GitHub repository PyPI package Quickstart
Detection Instance segmentation Keypoints Monocular depth 6DoF pose

Use cases

What YOLOZU is for

YOLOZU is the evaluation lane for teams that already have, or can export, prediction artifacts and need comparable evidence without replacing their training or inference stack.

Fair cross-framework evaluation

Compare outputs from different frameworks or runtimes on the same dataset and pinned metric protocol.

Prediction preflight

Check third-party or custom predictions.json artifacts for expected structure and required fields before scoring.

Repeatable regression evidence

Use explicit export settings and comparable reports to detect metric, preprocessing, or backend drift in review and CI.

Product boundary

Not the best fit

YOLOZU is not a managed training platform, hosted inference service, one-click production deployment system, or guaranteed support/SLA offering. If you only evaluate inside one framework and do not need a stable cross-stack boundary, that framework's native evaluator may be simpler. Training, benchmark, adapter, and research lanes remain secondary and carry their own maturity labels.

Quickstart

Install and run a visible demo

The shortest path checks the environment, runs an instance-segmentation demo, and writes both a JSON report and PNG overlays.

python3 -m pip install -U yolozu
yolozu doctor --explain
yolozu demo instance-seg --run-dir reports/quickstart_instance_seg --progress

Evaluation flow

How the lane works

1. Export Bring predictions from any framework or runtime.
2. Validate Check the predictions interface contract before scoring.
3. Evaluate Run the pinned metric protocol for the task.
4. Compare Review reports, PNG evidence, and parity artifacts.

Resources

Official links

These are the canonical public surfaces for installation, source review, archived releases, and the manual.

Searchable onboarding docs

Follow the 30-minute stable path, search generated commands and schemas, inspect examples, and resolve common failures.

Open docs

Source and issues

Review the Apache-2.0 repository, file issues, and inspect the current README and documentation map.

GitHub Issues

Install package

Install the current Python package from PyPI and use the CLI entrypoint yolozu.

PyPI

Manual and DOI records

Use the archived software and manual records when you need a stable citation trail.

Software DOI Manual DOI

Interface contract

Start with the predictions schema when wiring a new model, runtime, or export adapter into the evaluation lane.

Predictions schema

License

Apache-2.0 boundary

YOLOZU is a commercial ToppyMicroServices product provided free of charge. Its repository code is licensed under Apache-2.0; dependencies, data, and model weights retain their own terms. The primary lane is framework-agnostic prediction validation and evaluation; optional adapters and research paths are labeled separately. Free use does not include a hosted service or guaranteed support/SLA.