README.md

metadata

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.

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

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:

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:

// 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:

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.

@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.