# YOLOZU v1.0.0 — announcement + copy/paste templates

YOLOZU is an Apache-2.0, interface-contract-first evaluation harness for detection / segmentation / pose.

## What’s the point (3 lines)

- Run inference anywhere (PyTorch / ONNXRuntime / TensorRT / OpenCV-DNN / C++ / Rust).
- Export only the results into a stable `predictions.json` **predictions interface contract** (+ pinned `export_settings`).
- Validate + score with the same evaluator → apples-to-apples backend comparisons and reproducible reports.

## Quickstart (3 commands)

```bash
python3 -m pip install yolozu==1.0.0
yolozu doctor --output -
bash scripts/smoke.sh
```

Output artifact: `reports/smoke_coco_eval_dry_run.json`.

## Artifacts

- GitHub Release: https://github.com/ToppyMicroServices/YOLOZU/releases/tag/v1.0.0
- PyPI: https://pypi.org/project/yolozu/1.0.0/
- Zenodo (software): https://doi.org/10.5281/zenodo.18744757 (concept DOI: https://doi.org/10.5281/zenodo.18744756)
- Zenodo (manual PDF): https://doi.org/10.5281/zenodo.18744927 (concept DOI: https://doi.org/10.5281/zenodo.18744926)

## “For experts” signals (drop these in posts)

- Safe TTT/TTA protocols (Tent/MIM presets + guardrails)
- LoRA-only / norm-only update scopes
- Hessian/curvature-aware diagnostics
- Continual learning hooks + **run interface contract** (resume + pinned artifacts)

## GitHub Discussions (Announcements) — suggested post (EN)

Title:

> YOLOZU v1.0.0 — predictions interface contract for apples-to-apples eval

Body:

YOLOZU v1.0.0 is out.

- Any-backend inference → `predictions.json` predictions interface contract → one evaluator.
- `export_settings` pins preprocessing/protocol so comparisons stay reproducible.

Quickstart:

```bash
python3 -m pip install yolozu==1.0.0
yolozu doctor --output -
bash scripts/smoke.sh
```

Links:
- PyPI: https://pypi.org/project/yolozu/1.0.0/
- Zenodo (software concept DOI): https://doi.org/10.5281/zenodo.18744756
- Zenodo (manual concept DOI): https://doi.org/10.5281/zenodo.18744926

For people who care about the research knobs: Safe TTT (Tent/MIM), LoRA/norm-only scopes, Hessian diagnostics, and continual learning hooks.

## GitHub Discussions (Announcements) — suggested post (JP)

Title:

> YOLOZU v1.0.0 — `predictions.json` predictions interface contract で公平評価

Body:

YOLOZU v1.0.0 をリリースしました。

- 推論は好きなバックエンドで実行（PyTorch/ORT/TRT/OpenCV/C++など）
- 結果だけを `predictions.json` predictions interface contract に統一
- 同一評価器で採点するので backend 差を apples-to-apples に比較できます（`export_settings` で再現性も担保）

Quickstart:

```bash
python3 -m pip install yolozu==1.0.0
yolozu doctor --output -
bash scripts/smoke.sh
```

Links:
- PyPI: https://pypi.org/project/yolozu/1.0.0/
- Zenodo (software concept DOI): https://doi.org/10.5281/zenodo.18744756
- Zenodo (manual concept DOI): https://doi.org/10.5281/zenodo.18744926

（わかる人向け）Safe TTT（Tent/MIM）、LoRA/norm-only update scope、Hessian診断、継続学習フックも入っています。

## Social short posts

### X (EN, short)

YOLOZU v1.0.0: run inference anywhere → export `predictions.json` predictions interface contract → apples-to-apples eval (+ pinned `export_settings`). Safe TTT (Tent/MIM), LoRA/norm-only scopes, Hessian diagnostics. https://pypi.org/project/yolozu/1.0.0/

### X (JP, short)

YOLOZU v1.0.0: 推論は好きなbackendでOK → `predictions.json` predictions interface contract に統一して同一評価器でapples-to-apples（`export_settings`で再現性）。Safe TTT(Tent/MIM) / LoRA・norm-only / Hessian診断。https://pypi.org/project/yolozu/1.0.0/

### LinkedIn (EN, short)

YOLOZU v1.0.0 is released (Apache-2.0).

Bring-your-own inference (PyTorch/ORT/TRT/OpenCV/C++/Rust) and export only a stable `predictions.json` predictions interface contract. YOLOZU validates and scores that JSON with the same evaluator, so backend differences are measured under identical conditions and stay reproducible via pinned `export_settings`.

PyPI: https://pypi.org/project/yolozu/1.0.0/  
Zenodo (software concept DOI): https://doi.org/10.5281/zenodo.18744756  
Zenodo (manual concept DOI): https://doi.org/10.5281/zenodo.18744926

### Qiita/Zenn (JP, short)

YOLOZU v1.0.0 をリリースしました（Apache-2.0）。

推論はPyTorch/ONNXRuntime/TensorRT/OpenCV/C++など好きな環境で実行し、結果だけを `predictions.json` predictions interface contract に保存します。YOLOZUはそのJSONを同一評価器で採点するので、backend差を同一条件で比較でき、`export_settings` により再現性も担保できます。

- PyPI: https://pypi.org/project/yolozu/1.0.0/
- Zenodo（software concept DOI）: https://doi.org/10.5281/zenodo.18744756
- Zenodo（manual concept DOI）: https://doi.org/10.5281/zenodo.18744926

Quickstart:
```bash
python3 -m pip install yolozu==1.0.0
yolozu doctor --output -
bash scripts/smoke.sh
```

