Datasets:

diuogenes
/

Capturechart

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Capturechart: CharXiv standard 32 conditions

This release pairs 1,000 CharXiv validation figures across the same 32 image conditions: the electronic source, one clean physical baseline, and 30 selected capture perturbations. It includes 5,000 unchanged official questions and 1,151 visual-evidence questions linked to the original reasoning tasks.

Load the data

from datasets import load_dataset

repo = "diuogenes/Capturechart"
images = load_dataset(repo, "images_jpeg_q85", split="validation", streaming=True)
sample = next(iter(images))
sample["image"].save("example.png")
questions = load_dataset(repo, "original_qa", split="validation")
chart_questions = questions.filter(lambda q: q["chart_id"] == sample["chart_id"])

Use token=True for a private repository after hf auth login. For a reproducible evaluation, also pass revision="<release commit>" to every load_dataset call.

Configuration	Rows	Contents
`images_jpeg_q85`	32,000	Unchanged electronic originals + native-resolution Q85 / 4:4:4 derivatives
`original_qa`	5,000	Official questions, prompts, answers, and question metadata
`visual_evidence`	1,151	Visual-evidence questions and parent IDs; no reference answers
`parents`	1,000	Reasoning-task classification and child-question membership

All configurations use the upstream validation split. No new train/test split is introduced. Join questions to images with chart_id, select a profile, and keep qa_id/question_id plus view_id in evaluation records. Images are stored once per encoding and view, rather than repeated for every question. This distribution contains the JPEG encoding of the standard views. subplot_loc_json preserves the official mixed list/string/null field as JSON text. question preserves the official prompt; raw_question preserves the question alone. For visual evidence, model_question contains the frozen model-facing prompt.

Image versions and pairing

JPEG distribution. Canonical original PNGs are retained in the local full release and are not hosted in this repository. This is a separately versioned input encoding, not a byte-exact substitute for canonical original-image evaluation.

images_jpeg_q85 uses the already-frozen JPEG derivatives; JPEG is lossy and is a separately versioned input protocol. No image is resized or recoded during this export. sha256 identifies the embedded bytes; original_sha256 links a derivative to its canonical original. Each image has a stable view_id = chart_id + "__" + profile. Image bytes are embedded in Parquet.

The ordered conditions, physical mechanisms, selected intensities, original references, matched/shared control distinction and answerability gates are preserved in metadata/profile_standard.json and metadata/profile_catalog.csv.

Visual-evidence design and evaluation

The full release maps all 1,000 original reasoning tasks: 173 are decomposed into 1,151 visual-evidence questions, 810 remain direct-visual tasks, and 17 retain their ambiguity/evidence status. There is no three-question cap. At all 32 conditions the full release yields 160,000 original-question records and 36,832 visual-evidence records; these are input counts, not completed model results.

Visual-evidence questions were produced through Codex image inspection and authorized reference facts. human_approved=false is retained; the frozen release is not a claim of completed human approval. The standard condition list does not certify that every degraded image preserves answerability. Use the recorded per-condition gates.

metadata/evaluation_protocol.json preserves the frozen EvalKit transport, answer/judge prompts and decoding settings with portable model identifiers. Official original-question evaluation uses the native CharXiv adapter and scorer. VE scoring uses the per-question reference units/tolerances; it is not a uniform 10% rule. The private VE reference answers, tolerances and composition solutions are excluded from this package. The question-only VE configuration cannot reproduce VE scores without those private references. Official CharXiv answers are included in original_qa.

Source and license

Derived from princeton-nlp/CharXiv and its validation figures and questions. Please cite CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs. The upstream dataset declares CC BY-SA 4.0; this derivative data release retains CC BY-SA 4.0. Changes comprise synthetic capture views, separately marked JPEG derivatives, visual-evidence questions, portable metadata and storage layout. Original figure credits and rights remain with their respective creators.

This package contains the CharXiv component only. release.json records export counts, input hashes and validation evidence. checksums.sha256 covers release files.

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Paper for diuogenes/Capturechart

CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs

Paper • 2406.18521 • Published Jun 26, 2024 • 31