ECCV Artifact Evaluation
SkillAI & modelsUse when packaging code, models, and data around an ECCV paper, the anonymous review-time archive under the trailing supplement deadline, the do-not-cite-your-own-repo rule, and the June-to-September post-acceptance runway for a public release aligned with the ECVA and Springer copies of the paper.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
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Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the ECCV Artifact Evaluation skill
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in ECCV-Skills/skills/eccv-artifact-evaluation/SKILL.md and read by ahel’s review.
Use this for artifact strategy at ECCV, which has no badge-granting artifact track: artifacts live in two phases with opposite rules. Before decisions, code is a sealed, anonymous supplement; after the June decision, it becomes a public release with a ten-week runway before the September conference makes the paper visible to the whole field.
Phase 1 — sealed archive (review time)
- Upload with the supplement by the trailing deadline (March 12 in 2026, one week after the paper).
- The submission text must not link to the authors' public repository even if it already exists — ECCV 2026 policy treated that as an anonymity break, while arXiv posting itself was fine.
- Assume one reviewer opens the archive for ten minutes. Optimize for that reader: a README that maps each main-table row to a command, pinned dependencies, and one tiny demo input that runs on CPU.
- Scrub the usual vision-stack leaks: wandb entity names in configs, dataset paths containing lab or cluster names, git history, notebook metadata, license headers with author names, model cards naming the group.
What vision reviewers actually probe
| Artifact | Probe | Failure they report |
|---|---|---|
| Inference code + checkpoint | Run the demo image | Output does not resemble the paper's qualitative figures |
| Training configs | Diff against the paper's Sec. 4 numbers | Config hyperparameters contradict the text |
| Evaluation script | Check the metric implementation | Nonstandard IoU/FID variant silently inflates scores |
| Data tooling | Check preprocessing + splits | Test-set contamination via cropping or dedup choices |
The metric-implementation row is the highest-yield check to run on yourself: vision metrics have venue-standard reference implementations, and a rebuttal cannot repair a nonstandard one discovered in review.
Phase 2 — the ten-week release runway
The 2026 calendar gave accepted authors June 17 (decisions) to roughly mid-August (ECVA/Springer open-access copies appear ~4 weeks pre-conference) to ship the release the paper's readers will find:
- Week 1–2: de-anonymize the sealed archive; make the repo the camera-ready URL points to, not a placeholder.
- Week 3–5: release checkpoints with hashes, licences chosen deliberately (the Springer copyright covers the paper, not your code or weights — pick code and model licences yourself).
- Week 6–8: project page linking arXiv, the upcoming ECVA page, data cards, and a results table that matches the camera-ready exactly.
- Week 9–10: freeze; conference week issues should be answered from a stable main branch, because Malmö attendees will clone it during the poster session.
Dataset and benchmark releases
If the contribution is a dataset or benchmark, plan hosting that outlives the conference cycle: institutional or community storage rather than a personal drive link, versioned splits, a datasheet, and licence terms compatible with the source imagery. A benchmark whose test server or download link dies before the next even-year ECCV is a citation dead end.
# minimal release self-check before announcing
git clone <public-url> fresh && cd fresh
pip install -r requirements.txt # pinned, resolvable
python demo.py --input assets/demo.jpg # CPU-runnable smoke test
python eval.py --split val --ckpt <hash> # reproduces one paper row
Output format
[Phase] sealed-supplement / release-runway / post-conference
[Anonymity scrub] <leaks found in configs, paths, metadata, history>
[Reviewer-probe risks] <metric impl / config-text mismatch / demo gap>
[Release plan] <repo, checkpoints, licences, hosting, freeze date>
[Paper-artifact consistency] <camera-ready row -> reproducing command>
Signals
- GitHub stars
- 1k
- Forks
- 155
- Last commit
- Sep 2026
ahel review
K1binfo
installs-packages
Automated review, not a security audit. Ruleset v1+k2.
Advanced
- Item type
- skill
- Key
eccv-artifact-evaluation- Source
- github.com/brycewang-stanford/awesome-journal-skills
github.com/brycewang-stanford/awesome-journal-skills
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