0.0.4 — 2026-05-16

Release Notes

Robustness follow-up to 0.0.3. No new evaluation paradigms or kernel
changes — this release widens the typed-error surface, adds a fuzz
harness, and pins the platform-compat matrix in CI. The cross-paradigm
benchmark page is refreshed against the post-0.0.3 SHA on a single
fingerprint (no more dual-SHA LVIS caveat).

Added

• Typed Python error surface (#249) — four new PyValueError
  subclasses (InvalidAnnotationError, NonFiniteError,
  DimensionMismatchError, InvalidConfigError) with the public
  surface pinned by tests/python/test_error_matrix.py and documented
  at docs/reference/errors.md. Previously these all surfaced as bare
  ValueError; existing except ValueError: catches still match the
  new subclasses.
• Fuzz harness (#249) — tools/fuzz/ cargo-fuzz targets for the
  COCO / manifest / RLE / segmentation parsers (non-workspace crate so
  the nightly cargo-fuzz toolchain stays out of the publishable
  workspace). The vernier_core::fuzz_regressions integration test
  replays minimised crashes on every cargo nextest run; CI's
  slow.yml carries a 120 s/target smoke that builds once and exits.
• Platform-compat matrix (#249) — slow.yml adds a
  py3.10 × py3.13 × py3.14 ladder crossed with numpy / torch combos,
  exercising the BackgroundEvaluator tutorial end-to-end. Catches
  ABI / DLPack regressions that the single-version ci.yml matrix
  doesn't surface.
• bench-histogram Cargo feature (#249) — opt-in (G, D, wall_ns)
  per-call recorder on match_image, off by default and stripped from
  the shipped wheel. Powers the 10× val2017 scaling proof at
  docs/engineering/matching-scaling.md. Gated on
  vernier-core / vernier-ffi / vernier-mask; no production cost.
• Stress-matrix workloads (#249) — 6 named regimes
  (coco-baseline, detr-output, lvis-crowded, open-images-cats,
  satellite-4k, pathology-8k) plus per-axis sweeps in
  bench/workloads/stress_matrix.py; runner at
  bench/runners/stress_runner.py. Catalogue and expected behaviour
  per axis in docs/engineering/stress-matrix.md.
• Memory-under-training-load runner (#249) —
  bench/bench/runners/memory_bench.py (reuses
  bench.harness.rss.RSSSampler); methodology and reading guide at
  docs/engineering/memory-under-training.md.
• Colab smoke notebook (#249) — free-tier platform-check entry
  point; README badge links to it.

Changed

• Tutorial smoke now ingests via the DLPack array path —
  fake_model(image_ids) -> list[Detections] returning numpy arrays
  submitted batch-mode (matches torchvision's detection-API
  convention). Notebook cell-3 stays byte-identical to the .py body
  modulo the module docstring and __main__ guard.
• Bench page refreshed against the AMD EPYC-Milan host on a fresh
  machine fingerprint (37652a58e939). Speedups hold within VPS
  variance; absolute medians shift by ±3% versus the 0.0.3 snapshot.
  The dual-SHA LVIS caveat retires — every section now lives at the
  same SHA / fingerprint. The panoptic and synthetic-semantic cells
  exceeded the 5% relative-IQR gate (chronically noisy on this host —
  PNG decode dominates panoptic wall time, mmseg synthetic sits at the
  noise floor at 200-image scale); flagged inline with * per the
  renderer's existing convention.

Install vernier-cli 0.0.4

Install prebuilt binaries via shell script

curl --proto '=https' --tlsv1.2 -LsSf https://github.com/NoeFontana/vernier/releases/download/v0.0.4/vernier-cli-installer.sh | sh

Install prebuilt binaries via powershell script

powershell -ExecutionPolicy Bypass -c "irm https://github.com/NoeFontana/vernier/releases/download/v0.0.4/vernier-cli-installer.ps1 | iex"

Download vernier-cli 0.0.4

File	Platform	Checksum
vernier-cli-aarch64-apple-darwin.tar.xz	Apple Silicon macOS	checksum
vernier-cli-x86_64-pc-windows-msvc.zip	x64 Windows	checksum
vernier-cli-aarch64-unknown-linux-gnu.tar.xz	ARM64 Linux	checksum
vernier-cli-x86_64-unknown-linux-gnu.tar.xz	x64 Linux	checksum