--- name: eccv-reproducibility description: Use when hardening the reproducibility story of an ECCV paper — training recipes and schedules readers can re-run, dataset versioning and split provenance, pinned foundation-model dependencies, compute disclosure, and seed/variance honesty for benchmark deltas, sized for the 14-page LNCS body plus supplement. --- # ECCV Reproducibility Use this before the ECCV paper freeze. ECCV publishes through Springer LNCS with no standing mandatory reproducibility checklist across cycles (whether the current cycle adds one: 待核实 against the live author guidelines), so the reproducibility bar is enforced socially: by reviewers who try to match your numbers, and by the two-year gap before you could publish a correction at the same venue. ## The two-year checkability horizon A CVPR paper's errors are challenged within a year; an ECCV paper sits as the venue's latest word on the topic until the *next even year*. Write the paper so a lab starting from only the PDF plus supplement in 2027 can rebuild the result — that is the horizon reviewers implicitly price in. ## Recipe ledger (goes in paper or supplement, never nowhere) | Ingredient | Minimum disclosure | Common ECCV-draft omission | |---|---|---| | Training schedule | Optimizer, LR schedule, epochs/iterations, batch size, augmentations | Augmentation list "standard" with no definition | | Initialization | Pretrained checkpoint identity + source | "ImageNet-pretrained" without which checkpoint | | Data | Dataset version, split definition, filtering rules | Custom val split described only as "held out" | | Evaluation | Metric implementation source, input resolution, TTA on/off | Resolution mismatch between method and baselines | | Compute | GPU type, count, wall-clock, total runs behind the paper | Only the final run's cost reported | ## Foundation-model era pinning Modern ECCV pipelines sit on moving substrates. Pin all of them by exact identity, because "CLIP features" is not reproducible information: ```yaml # pinned-substrate block for the supplement backbone: dinov2-vitl14, weights sha256:, source: vlm: , accessed 2026-02 sam_variant: inference: fp16, single-crop, resolution 518x518 api_models: none # if any API model is used, record date + version string ``` An API-served model that silently updates invalidates comparisons; record access dates and version strings, and prefer frozen open-weight substrates for headline tables. ## Variance honesty on benchmark deltas - A +0.3 mAP or +0.2 mIoU headline delta needs seed evidence: report mean ± std over ≥3 seeds for your method *and* your strongest baseline, or scope the claim down. - State which numbers are your re-runs versus quoted from prior papers — mixed provenance inside one table is a classic silent irreproducibility. - If full re-training is too expensive to repeat, say so and report seeds on the cheapest deciding component (e.g., the head, not the backbone). ## Split the story across the 14 pages and the supplement - Body: enough recipe to judge plausibility — schedule summary, data versions, compute order-of-magnitude. - Supplement: the full ledger, per-experiment configs, the pinned-substrate block, and negative-result notes ("we tried X at lr=1e-3, diverged"). - Code archive: configs as files, not prose; the paper should never be the only serialization of a hyperparameter. ## Honest-failure statement One paragraph reviewers reward at this venue: name the regime where the method breaks (small objects, low light, out-of-distribution categories), with a pointer to a supplement figure showing it. It signals the numbers were probed rather than curated. ## Output format ```text [Repro grade] rebuildable-from-paper / rebuildable-with-code / not-rebuildable [Ledger gaps] [Substrate pinning] exact identity to record> [Variance status] seed evidence present?> [Placement plan] ```