yolozu 1.0.8

Interface-contract-first evaluation toolkit for mitigating catastrophic forgetting and domain shift via reproducible workflows.

YOLOZU (萬)

Japanese: Readme_jp.md

YOLOZU at a glance

• Framework-agnostic evaluation toolkit for vision models: designed for reproducible continual learning and test-time adaptation under domain shift.
• Training-capable workflows for mitigating catastrophic forgetting: supports training and evaluation workflows based on self-distillation, replay, and parameter-efficient updates (PEFT). These approaches reduce forgetting and make it measurable and comparable across runs, though complete elimination is not guaranteed.
• Support for inference-time adaptation (TTT): allows model parameters to be adjusted during inference, enabling continual adaptation to domain shift in deployment.
• Predictions as the stable interface contract: treats predictions---not models---as the primary contract, making training, continual learning, and inference-time adaptation comparable, restartable, and CI-friendly across frameworks and runtimes.
• Multi-task evaluation support: covers object detection, segmentation, keypoint estimation, monocular depth estimation, and 6DoF pose estimation. Training implementations remain configurable and decoupled, rather than fixed to a specific framework.
• Production-ready deployment path: supports ONNX/ExecuTorch export and execution across PyTorch, ONNX Runtime, TensorRT, and ExecuTorch, with reference inference templates in C++ and Rust.
• Interface-contract-first, AI-first workflow: every experiment emits versioned artifacts that can be automatically compared and regression-tested in CI.

Quickstart (run this first)

bash scripts/smoke.sh

Output artifact: reports/smoke_coco_eval_dry_run.json.

Docs index (start here): docs/README.md.

AI-friendly tool registry (source of truth): tools/manifest.json.

Tool list + args examples: docs/tools_index.md.

Learning features (training / continual learning / TTT / distillation / long-tail recipe PyTorch plugin choices): docs/learning_features.md.

Start here (choose 1 of 4 entry points)

• A: Evaluate from precomputed predictions (no inference deps) — predictions.json → validate → eval.
• B: Train → Export → Eval (RT-DETR scaffold + run interface contract / Run Contract) — run artifacts → ONNX → parity/eval.
• C: Interface contracts (predictions / adapter / TTT protocol) — schemas + adapter interface contract boundary + safe adaptation protocol.
• D: Bench/Parity (TensorRT / latency benchmark) — parity checks + pinned-protocol benchmarks.

All four entry points are documented (with copy-paste commands) in docs/README.md.

CLI note:

• yolozu ... is the pip/package CLI.
• python3 tools/yolozu.py ... is the repo wrapper CLI.
• For equivalent commands, swap only the executable (yolozu ↔ python3 tools/yolozu.py).

Module path note:

• Canonical Python modules live under categorized packages (yolozu/core, yolozu/datasets, yolozu/eval, yolozu/inference, yolozu/predictions, yolozu/training, yolozu/geometry).
• Legacy imports such as from yolozu.dataset import build_manifest remain available via package-level aliasing in yolozu/__init__.py.

Key points

• Bring-your-own inference → stable predictions.json interface contract.
• Validators catch schema drift early.
• Protocol-pinned export_settings makes comparisons reproducible.
• Parity/bench quantify backend drift and performance.
• Tooling stays CPU-friendly by default (GPU optional).
• Apache-2.0-only ops policy is enforced in repo tooling.

Why YOLOZU?

YOLOZU standardizes evaluation around a predictions-first interface contract: run inference anywhere, export predictions.json (+ export_settings), then validate and evaluate with fixed protocols for reproducible comparisons.

Details: docs/yolozu_spec.md.

Install (pip users)

python3 -m pip install yolozu
yolozu --help
yolozu doctor --output -

Optional (CPU) demos:

python3 -m pip install -U 'yolozu[demo]'
yolozu demo overview  # writes demo_output/overview/<utc>/demo_overview_report.json
yolozu demo
yolozu demo instance-seg
yolozu demo keypoints
yolozu demo pose  # chessboard default; use --backend aruco for marker-based pose
yolozu demo pose --backend aruco  # cached sample in demo_output/pose/_samples (delete to regenerate)
yolozu demo pose --backend densefusion  # heavy: CUDA + large downloads
yolozu demo depth  # default: Depth Anything (Transformers); use --compare to run MiDaS/DPT too
yolozu demo train  # downloads ResNet18 weights on first run
yolozu demo continual --compare --markdown

Optional extras and CPU demos: docs/install.md.

CLI completion (bash/zsh):

# bash
eval "$(yolozu completion --shell bash)"
# zsh
eval "$(yolozu completion --shell zsh)"

Real-image multitask finetune smoke (bbox/segmentation/keypoints/depth/pose6d):

# review dataset license/terms before download
python3 scripts/download_coco_instances_tiny.py --out-root data/coco --split val2017 --num-images 8 --seed 0 --force
python3 tools/prepare_real_multitask_fewshot.py --out data/real_multitask_fewshot --train-images 6 --val-images 2 --strict-provenance --force
python3 tools/run_real_multitask_finetune_demo.py --dataset-root data/real_multitask_fewshot --out reports/real_multitask_finetune_demo --device cpu --epochs 1 --max-steps 1 --batch-size 2 --image-size 96 --strict-provenance --force

One-command workflow (prepare + optional tiny COCO auto-download + staged smoke):

python3 tools/run_real_multitask_finetune_demo.py --dataset-root data/real_multitask_fewshot --prepare --download-if-missing --allow-auto-download --accept-dataset-license --download-num-images 8 --out reports/real_multitask_finetune_demo --device cpu --epochs 1 --max-steps 1 --batch-size 2 --image-size 96 --strict-provenance --force

External finetune smoke matrix (YOLOv/MMDetection/Detectron2/RT-DETR, interface contract report):

python3 tools/run_external_finetune_smoke.py --dataset-root data/smoke --split train --output reports/external_finetune_smoke.json

Execute real training for selected frameworks:

python3 tools/run_external_finetune_smoke.py --dataset-root data/smoke --split train --non-dry-framework yolov --non-dry-framework rtdetr --epochs 1 --max-steps 1 --batch-size 2 --image-size 96 --device cpu --require-training-execution --output reports/external_finetune_smoke.exec.json

RT-DETR non-dry path now emits explicit dependency failure metadata when torch is unavailable (failure_code=E_DEP_TORCH_MISSING). MMDetection/Detectron2 non-dry runs with --mmdet-train-script / --detectron2-train-script continue train-path audit even when projection deps are missing, and record projection_error in the report.

Details and external launcher wiring: docs/external_finetune_smoke.md.

Deterministic domain-shift target recipe for TTT:

python3 scripts/prepare_ttt_domain_shift_target.py --dataset-root data/smoke --split val --out reports/domain_shift/smoke_gaussian_blur_s2 --corruption gaussian_blur --severity 2 --seed 2026 --force
python3 tools/export_predictions.py --adapter dummy --dataset reports/domain_shift/smoke_gaussian_blur_s2 --split val --wrap --domain-shift-recipe reports/domain_shift/smoke_gaussian_blur_s2/domain_shift_recipe.json --output reports/pred_shift_target.json

Details: docs/ttt_protocol.md.

Reference adapter regression (RT-DETR, real-image baseline):

python3 tools/run_reference_adapter_regression.py --dataset data/smoke --split val --max-images 2 --profile micro --repro-policy relaxed --runtime-lock requirements-ci.lock --baseline baselines/reference_adapter/rtdetr_pose_smoke_val.json --diff-summary-out reports/reference_adapter_regression.diff_summary.json --topk-examples-dir reports/reference_adapter_regression_topk --topk-examples 3 --output reports/reference_adapter_regression.json

Interface-contract-only hard gate:

python3 tools/run_reference_adapter_regression.py --dataset data/smoke --split val --max-images 2 --profile micro --score-gate-mode off --perf-gate-mode off --runtime-lock requirements-ci.lock --enforce-runtime-lock --enforce-weights-hash --baseline baselines/reference_adapter/rtdetr_pose_smoke_val.json --output reports/reference_adapter_regression_contract.json

Behavior-only warn gate:

python3 tools/run_reference_adapter_regression.py --dataset data/smoke --split val --max-images 2 --profile micro --schema-gate-mode off --consistency-gate-mode off --score-gate-mode warn --perf-gate-mode warn --runtime-lock requirements-ci.lock --enforce-runtime-lock --baseline baselines/reference_adapter/rtdetr_pose_smoke_val.json --output reports/reference_adapter_regression_behavior.json

Source checkout (repo users)

python3 -m pip install -r requirements-test.txt
# optional: mirror CI recommended tier (pinned runtime)
python3 -m pip install -r requirements-ci.lock
python3 -m pip install -e .
python3 tools/yolozu.py --help
python3 -m unittest -q

If you want the optional demo dependencies in a source checkout:

python3 -m pip install -e '.[demo]'

Single-command release automation (no required options):

python3 tools/release.py

Auto bump policy (current X.Y.Z -> next version):

• small change: X.Y.(Z+1) (e.g. 1.1.1+add equivalent)
• medium change: X.(Y+1).0 (e.g. 1.1+a.0 equivalent)
• large change: (X+1).0.0 (e.g. 1+a.0.0 equivalent)

Dry-run preview:

python3 tools/release.py --dry-run --allow-dirty --allow-non-main --output reports/release_report.dry_run.json

MCP settings check (manifest + generated MCP/Actions references):

python3 tools/check_mcp_settings.py --output reports/mcp_settings_check.json

Manual (PDF)

Printable manual source: manual/.

Support / legal

• Contact: [e-mail]
• © 2026 ToppyMicroServices OÜ Full support/legal: docs/support.md.

License

Code in this repository is licensed under the Apache License, Version 2.0. See LICENSE.