code/docs/RELEASING.md

Preparing the existing self-contained KITTI-C2L release

The dataset already exists at https://huggingface.co/datasets/mcocheteux/KITTI-C2L-Dataset with the self-contained upload already in place. Its pinned audited revision is recorded in release/v1/hub_manifest.json. The upload has 44 training and 6 test asset shards (51.94 GB of sensor assets), 19,608 training and 2,792 test frames, plus the four frozen metadata tables.

Preparation must retain that upload and its frames configuration. The source checkout holds only the lightweight metadata, so its README has local preview YAML. Use release/v1/HF_DATASET_CARD.md as the production Hub README. The optional metadata-only archive is an alternative download, not a replacement for the full uploaded dataset.

What is verified

• All 50 shard schemas and frame-identity/source-mapping columns were read at the pinned revision using authenticated ranged reads: complete, unique coverage of all 22,400 expected frames and correct split assignments.
• Hub LFS SHA-256 values for the four metadata files match the exact snapshot used in our UniCal experiments. Each metadata row and its seeded transforms also pass the local numerical audit.
• The smallest train and test shards were downloaded completely. Their SHA-256 values match the Hub declarations. All 149 contained image/scan pairs are byte-identical to local KITTI Raw and decode correctly.
• The other shard payloads were not downloaded in full. Their recorded hashes are Hub declarations, not independently recomputed local checksums. The audit scope is explicit in hub_manifest.json and hub_payload_verification.json.

Stage a reviewable full-repository update

uv sync --python 3.11 --extra dev --extra release --locked
uv run ruff check .
uv run pytest
uv run kitti-c2l validate --root . --kitti-calib-root /path/to/kitti_raw
uv run python scripts/prepare_release.py --profile full-update \
  --output dist/kitti-c2l-full-update-v1.0.0
uv build

This validates the local metadata and recorded full-upload audit, then stages an allowlisted update package. Its README is the full production card, including metadata, frames and windows. release-profile.json records the target repository, base revision and the requirement to retain existing asset shards. It explicitly specifies no remote deletions. The tar.gz is roughly 16 MB because the existing 52 GB upload is reused; it is not a standalone full dataset download. No upload or visibility change occurs.

For an independent lightweight download:

uv run python scripts/prepare_release.py --profile metadata \
  --output dist/kitti-c2l-metadata-v1.0.0
uv run python scripts/check_release_loading.py --root dist/kitti-c2l-metadata-v1.0.0

Do not upload the lightweight README over the full dataset card. Neither archive includes local KITTI files, gallery payloads, credentials or scratch directories. Source/wheel distributions also exclude sensor images and dataset payloads. Both staging modes refuse to overwrite an existing output.

Rerun the remote audit

Use Hugging Face CLI authentication with an account authorized to read the dataset if access is restricted; Safari login alone does not authenticate a separate workspace.

uv run hf auth login
uv run python scripts/audit_hub_release.py \
  --revision dfe6154248a52886fcf35851161cbe07a4373d2c \
  --output scratch/hub-audit.json

This reads schemas and non-payload columns and does not download the complete 52 GB sensor corpus. The pinned revision makes the inventory reproducible. To reproduce the payload spot check (about 361 MB of downloads, cached on repeat):

uv run python scripts/check_hub_payloads.py --raw-root /path/to/kitti_raw \
  --output scratch/payload-audit.json

Do not describe these two shards as a full sensor-payload audit.

Baseline package

scripts/prepare_baselines.py creates the separate model package as described in BASELINES.md. Three exported checkpoints have been re-evaluated across all 2,792 native test frames each, with Hub access disabled, reproducing the original results. Model weights and source snapshots have their own licenses; no training images or point clouds are included in that package.

Publication handoff

1. Review the full dataset card, licenses, citation, frozen geometry convention, split policy and measured limitations. Choose the real release date; set date-released in CITATION.cff and remove candidate/private wording when publication actually happens. No DOI or publication date is invented here.
2. Confirm that the current Hub revision still matches the recorded base or review subsequent changes. Upload only the reviewed update-package files, preserving all existing data/frames_*.parquet shards and docs assets. Never synchronize with deletion from this metadata-only local checkout.
3. Check all three Hub configurations and the viewer before announcing a release. Pin the resulting revision and recheck metadata hashes, shard counts and joins.
4. Change visibility only when explicitly authorized. Recheck the public page and loading flow without authentication; record the final public revision.
5. Publish the MIT loader/generator source and attach the wheel or publish its package so linked installation instructions are accessible. Publish optional baseline weights separately with their source and upstream notices.

Official references: Hugging Face manual configuration, KITTI licensing, KITTI access policy.

Changes to geometry, targets, frame selection or split semantics require a new version and updated evidence. The sensor assets can remain byte-identical when only calibration metadata is revised; do not re-upload them unnecessarily.