| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - image-to-text |
| - text-to-image |
| - visual-question-answering |
| language: |
| - en |
| tags: |
| - image-captioning |
| - vision-language-model |
| - continual-pretraining |
| - multimodal |
| - captions-only |
| size_categories: |
| - 100K<n<1M |
| pretty_name: ZoomCap-450K |
| configs: |
| - config_name: pii_free |
| data_files: pii_free/*.parquet |
| papers: |
| - arXiv:2610.21320 |
| --- |
| |
| # ZoomCap-450K |
|
|
| **Dense image captions produced by the ZoomCap evidence-routing workflow, as a |
| drop-in caption replacement for |
| [ScaleCap-450k](https://huggingface.co/datasets/long-xing1/ScaleCap-450k).** |
|
|
| Code, verification tooling and the published evidence reports: |
| **https://github.com/MasterHow/ZoomCap-450K** (Apache-2.0). |
|
|
| This repository contains **captions only — no images**. Each row carries the |
| image key under which the picture is published upstream, so the corpus can be |
| joined to ScaleCap-450k (or to any local copy of the ShareGPT4V / LAION pools) |
| without redistributing a single picture. |
|
|
| The row schema is intentionally **identical to ScaleCap-450k** (`images` + |
| `conversations`, including its literal `<image>` human turn), so a ScaleCap |
| training recipe runs against ZoomCap-450K with no loader changes. |
|
|
| ## Why |
|
|
| The companion paper |
| *ZoomCap: Evidence-Routed Visual Zooming for Recoverable Visual Information |
| Transfer* treats fine-grained captioning as **recoverable visual information |
| transfer**: a teacher acquires missing evidence under a finite observation |
| budget (conditional knowledge retrieval, OCR-sensitive reading, coverage-gap |
| recovery, adaptive visual zooming with grounded geometry) and converts it into |
| reusable supervision. Headline results, measured on this corpus and its |
| evaluations: |
|
|
| | Result | ZoomCap | baseline | |
| | --- | --- | --- | |
| | FactCycle transferred claims / image | **52.4** | 31.5 (ScaleCap) | |
| | Human tie-adjusted preference vs. a 10× larger flagship teacher | **81.9%** | — | |
| | 11-benchmark CPT average vs. strongest competing 450K corpus | **+1.31 … +2.38** | — | |
| | Production document/OCR monitor suite (`Ling-Tiny-VL-v3`, 60B tokens) | **+1.4** (+0.90 platform-wide) | — | |
| | Medical pre-training, 5 VQA benchmarks (2.64M images) | **+3.04** | +1.76 (same-image single-pass captions) | |
| | Captioning cost per image | **$0.0077** | 3.6× more expensive per image, 5.9× more expensive per transferred claim for the ScaleCap recipe | |
| | Total characters vs. ScaleCap-450K | **1.45×** | — | |
|
|
| Releasing the corpus is what makes the equal-scale CPT comparison in the paper |
| checkable. |
|
|
| ## Layout |
|
|
| ``` |
| pii_free/ train-00000 … train-00008.parquet 447,285 rows 536 MB |
| sample/ zoomcap_450k_sample.jsonl 1,000 rows (readable preview) |
| stats.json per-build counters, QC flags, PII census tallies |
| release_manifest.json sha256 + byte size of all 31 published files |
| verification/PII_CENSUS.md every personal-information category, measured verbatim |
| verification/JOIN_VERIFICATION.md schema + row-count + loadable checks |
| verification/CORPUS_STATS.md the paper's corpus-statistics columns, recomputed |
| verification/PII_FREE_CHECK.md independent re-scan of `pii_free`: 0 detections |
| LEAK_SCAN.md result of the pre-publication internal-identifier scan |
| ``` |
|
|
| * `pii_free` — the corpus minus 3,687 degenerate outputs and minus the 3,991 |
| rows whose source image shows a third-party contact string or credential. |
| Rows are dropped, never edited, so every caption here is byte-identical to |
| the version the teacher produced. |
|
|
| > **Scope of this public release.** Published: `pii_free`. Withheld: `full`, `clean`. The withheld configurations keep every caption byte-identical to the corpus the paper reports (the degenerate output tail and rows transcribing contact strings visible in the source image). Use `pii_free` for training; access to the withheld configurations requires a separate data-release approval — open an issue on the code repository to ask. |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("MasterHow/ZoomCap-450K", "pii_free", split="train") # 447,285 rows |
| # configs: "pii_free" (447,285 rows) |
| print(ds[0]) |
| # {'images': ['laion_filter_image/7156c3a92b9fdf307f8734b11cd4d98b' |
| # '70317652a937fb9622a24256048b910b.jpg'], |
| # 'conversations': [{'from': 'human', 'value': '<image>'}, |
| # {'from': 'gpt', 'value': 'This dynamic, close-up action shot |
| # captures three Liverpool FC players celebrating a goal …'}]} |
| ``` |
|
|
| ### Captions + images (join against ScaleCap-450k) |
|
|
| Images are not redistributed here; they live in ScaleCap-450k under exactly the |
| same key. Index the keys once, then stream the upstream images past them — the |
| index holds ~440K short strings (~40 MB), and captions stay on disk behind Arrow |
| random access: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| zoom = load_dataset("MasterHow/ZoomCap-450K", "pii_free", split="train") # arrow-backed |
| index = {} |
| for i, row in enumerate(zoom): # one pass over the keys only |
| index.setdefault(row["images"][0], i) # use a list if you want every caption |
| # of a duplicated key |
| |
| n = 0 |
| for row in load_dataset("long-xing1/ScaleCap-450k", "ScaleCap450k", |
| split="train", streaming=True): |
| i = index.get(row["images"][0]) # e.g. 'laion_filter_image/<sha256>.jpg' |
| if i is None: |
| continue |
| caption = zoom[i]["conversations"][1]["value"] |
| n += 1 # (row["images"][0], caption) is a pair |
| print("matched", n) |
| ``` |
|
|
| ScaleCap-450k is ~95 GB, so prefer `streaming=True`. If you already hold the |
| ShareGPT4V or LAION image pools locally, the same key resolves there: |
| `laion_filter_image/<sha256>.jpg` is the LAION-5B image key (sha256 of the source |
| URL, i.e. what `img2dataset` names its files), and `share4v_image/<name>.jpg` is |
| ShareGPT4V's own filename (`000000156836.jpg` COCO, `sa_24327.jpg` SAM, |
| `6e68d8a830ff2821.jpg` CC12M, `Vincent_Cassel.jpg` wikiart). |
|
|
| ### LLaMA-Factory / CPT against ScaleCap |
|
|
| Register both corpora and change nothing else — the fields are the same: |
|
|
| ```json |
| // data/dataset_info.json |
| "zoomcap_450k": {"hf_hub_url": "MasterHow/ZoomCap-450K", "subset": "pii_free", |
| "formatting": "sharegpt", "columns": {"messages": "conversations", |
| "images": "images"}, "tags": {"role_tag": "from", "content_tag": "value", |
| "user_tag": "human", "assistant_tag": "gpt"}}, |
| "scalecap_450k": {"hf_hub_url": "long-xing1/ScaleCap-450k", |
| "formatting": "sharegpt", "columns": {"messages": "conversations", |
| "images": "images"}} |
| ``` |
|
|
| ## Contents |
|
|
| | | `pii_free` | |
| | --- | --- | |
| | rows | 447,285 | |
| | unique image keys | 432,755 | |
| | mean caption characters | 3,966.0 | |
| | `laion_filter_image` rows | 346,618 | |
| | `share4v_image` rows | 100,667 | |
| | median / p99 / max chars | 3,926 / 6,581 / 29,215 | |
|
|
| `full`'s mean of 4,228.6 characters is the number the paper's corpus-statistics |
| table reports, reproduced exactly: caption text is published verbatim. |
|
|
| Word and sentence means are recomputed from the published rows with the script in |
| `verification/CORPUS_STATS.md`; the paper's table quotes 622.8 words and 33.1 |
| sentences from its own internal tokeniser, and the character column (4,228.6) is |
| reproduced to within 0.1 by the sanitised release. |
|
|
| **Duplicate keys are expected.** The upstream pool repeats 8,865 ShareGPT4V |
| images (5,395 keys twice, 1,848 three times, 1,000 four times, 494 five, 110 six, |
| 18 seven times → 14,725 extra rows). Every occurrence was captioned |
| independently, so those rows are *different* captions of the same picture — |
| useful as augmentation, and something to deduplicate if you want strictly one |
| caption per image: |
|
|
| ```python |
| seen = set() |
| ds = ds.filter(lambda r: not (r["images"][0] in seen or seen.add(r["images"][0]))) |
| ``` |
|
|
| ## Processing applied to the internal corpus |
|
|
| Everything below is reproducible from the scripts in the accompanying code |
| repository (`release/build_release.py`, `release/scan_leaks.py`, |
| `release/verify_join.py`, `release/compute_corpus_stats.py`). |
|
|
| 1. **Key sanitising.** Captions were produced against an internal image cache |
| whose absolute paths are not part of the upstream naming. They are rewritten |
| to the upstream form `<source_dir>/<basename>`; the cache's 21 four-hex shard |
| directories are dropped. De-sharding was verified injective on this corpus |
| (no key is reachable from two shard directories), and the build fails hard if |
| that ever changes. |
| 2. **Caption text is published verbatim — no masking.** The strings a PII |
| detector finds in this corpus are text *visible inside the source images* |
| (contact e-mails on business cards, phone numbers on storefront signage, |
| paths in screenshots, social handles in watermarks). Rewriting them would |
| break three things at once: the OCR transcriptions that make this corpus |
| useful, the caption-image consistency that reconstruction-style evaluation |
| measures, and bit-for-bit reproduction of the paper's statistics. Instead we |
| measure and expose them: |
|
|
| | category | occurrences | rows | share of rows | |
| | --- | --- | --- | --- | |
| | social handle (at-sign username) | 3,943 | 1,986 | 0.44% | |
| | e-mail address | 2,705 | 1,978 | 0.43% | |
| | local filesystem path | 136 | 95 | 0.02% | |
| | mainland-China mobile number | 80 | 66 | 0.015% | |
| | in-image credential (password / activation code) | — | 10 | 0.002% | |
| | QQ identifier | 2 | 2 | <0.001% | |
| | national ID / passport / bank card | 0 | 0 | 0% | |
|
|
| Verbatim contexts for every category, plus the exact detection patterns, ship |
| in `verification/PII_CENSUS.md`; `verification/PII_FREE_CHECK.md` is an |
| independent re-scan of the published `pii_free` shards (447,285 rows, 0 |
| detections) that does not reuse the build's own flags. Nothing in this corpus is Ant Group data: |
| the images are the public ShareGPT4V / LAION pools that ScaleCap-450k already |
| publishes, and the captions are our model's descriptions of them. |
| 3. **Row-level exclusion, offered as a config.** Every row matching one of those |
| categories carries a flag (`pii_contact`, `pii_identifier`, |
| `credential_in_image`) and is dropped from `pii_free` — 3,991 rows, 0.88% of |
| the corpus. Exclusion replaces editing, so the remaining rows stay exactly as |
| generated. Textual masking remains available behind |
| `build_release.py --redact-pii` for anyone who needs it; it is off by default |
| precisely because it corrupts the corpus. |
| 4. **Degeneracy QC**, deterministic thresholds: `too_short` < 200 chars (4 rows), |
| `too_long` > 30,000 chars (3,510), `repetition_line` — one line of >= 40 chars |
| repeated more than 5x (3,274), `repetition_word` — one 8-word shingle |
| repeated more than 15x (2,401). Flagged rows: **3,687** removed in `clean`, |
| kept in `full`. |
| 5. **Internal-identifier scan** of every published file — internal mount points, |
| developer home directories, internal corpus/system names, work ids, cloud |
| credentials, private keys: **0 hits**. The same scan over the publishing |
| scripts and this card is also clean (`LEAK_SCAN.md` ships with the release). |
| 6. **Schema check** against the upstream contract (field order, arrow types, |
| literal `<image>` human turn, key grammar, no absolute paths, no empty |
| captions), a `datasets.load_dataset` check that the arrow feature signature |
| equals ScaleCap-450k's, and reconciliation of every shard's row count with |
| `stats.json`. |
|
|
| Residual in-image text is *not* scrubbed: URLs, watermark credits (e.g. |
| `© AFP/Getty Images`) and public-figure names appear where the source image |
| shows them, exactly as in the upstream public captions. |
|
|
| ## Privacy stance in one paragraph |
|
|
| We publish descriptions of images that are already public. Captions can therefore |
| contain what those images show — including contact strings of third parties who |
| never interacted with us. Rather than edit the text (which would silently corrupt |
| OCR transcriptions and every statistic the paper reports), we (a) publish the full |
| census with verbatim examples, (b) ship `pii_free`, a config with all such rows |
| removed, (c) redistribute no images, and (d) restrict the corpus to |
| non-commercial research use under the upstream licence. If your use case cannot |
| tolerate any third-party contact string, load `pii_free`. |
|
|
| ## Intended use and limitations |
|
|
| * **Intended:** research on vision-language models and text-to-image models — |
| caption-corpus comparisons, continual pre-training, caption→image |
| reconstruction studies. |
| * Captions are **model-generated** (teacher: the Qwen3.5-35B-A3B call used in the |
| paper's ZoomCap workflow). They can contain hallucinated detail, misattributed |
| names and wrong counts; the paper quantifies this rather than hiding it |
| (FactCycle's truthfulness anchor exists precisely to measure it). Verify before |
| relying on any single caption. |
| * Captions are English and highly structured (free-text overview plus |
| `### Background Knowledge` and `### Visual Detail Supplement` sections), which |
| is a property of the workflow, not of the images. |
| * **Not** for medical, legal or diagnostic use, and not for any |
| identification/surveillance of the people who appear in the source images. |
| * The corpus contains descriptions of images that may depict nudity, violence or |
| other disturbing content, inherited from LAION-5B / ShareGPT4V. |
|
|
| ## License and attribution |
|
|
| Captions: **CC BY-NC 4.0**, research use only — the same licence as the upstream |
| corpus this overlays, so no downstream user gains rights beyond what |
| ScaleCap-450k already grants. The accompanying scripts are Apache-2.0. |
|
|
| No images are redistributed. Obtaining them remains the user's responsibility |
| under the upstream terms: ScaleCap-450k (CC BY-NC 4.0), ShareGPT4V-100K, and |
| LAION-5B (which publishes URL metadata only; image copyright stays with the |
| original creators). If you cannot satisfy those terms, do not use this dataset. |
|
|
| ```bibtex |
| @article{shi2026zoomcap, |
| title = {ZoomCap: Evidence-Routed Visual Zooming for Recoverable Visual |
| Information Transfer}, |
| author = {Shi, Hao and Wang, Jiafeng and Fu, Yuzhuo and Zhang, Tao and |
| Bai, Jian and Zhang, Juelu and Cui, Qing and Huang, Chao}, |
| journal = {arXiv preprint arXiv:2610.21320}, |
| year = {2026} |
| } |
| |
| @article{xing2025scalecap, |
| title = {ScaleCap: Inference-Time Scalable Image Captioning via |
| Dual-Modality Debiasing}, |
| author = {Xing, Long and Huang, Qidong and Dong, Xiaoyi and Zhang, Pan and |
| Zang, Yuhang and Cao, Yuhang and Li, Jinsong and Ding, Shuangrui and |
| Zhang, Weiming and Yu, Nenghai and Wang, Jiaqi and Wu, Feng and |
| Lin, Dahua}, |
| journal = {arXiv preprint arXiv:2506.19848}, |
| year = {2025} |
| } |
| |
| @inproceedings{chen2023sharegpt4v, |
| title = {ShareGPT4V: Improving Large Multi-Modal Models with Better Captions}, |
| author = {Chen, Lin and Li, Jisong and Dong, Xiaoyi and Zhang, Pan and |
| He, Conghui and Wang, Jiaqi and Zhao, Feng and Lin, Dahua}, |
| booktitle = {European Conference on Computer Vision (ECCV)}, |
| year = {2024} |
| } |
| |
| @article{schuhmann2021laion, |
| title = {LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs}, |
| author = {Schuhmann, Christoph and others}, |
| journal = {arXiv preprint arXiv:2111.02114}, |
| year = {2021} |
| } |
| ``` |
|
|
| ## Contact |
|
|
| TODO: point at the approved public contact (org e-mail or GitHub issues on the |
| code repository). Do not list personal work accounts. |
|
|
