adopd/adopd2026

Project | Code | Model Zoo

ADOPD2026 is a 120,000-page document understanding dataset with original high-resolution images and complementary supervision for text detection, semantic region grounding, instance segmentation, page description, and document-level analysis. It is the public data release accompanying Thinking with Anchors: Grounded and Efficient Document Reasoning.

Unlike a flattened detection export, each row retains independent annotation layers and their provenance boundaries. Human entity polygons are canonical segmentation targets; grouped OCR blocks are canonical text-box targets; VLM annotations add semantic labels to human masks; legacy masks remain available as a separate historical layer.

Dataset Summary

Pages	Splits	Original images	Parquet shards	Release size
120,000	80k train / 20k validation / 20k test	JPEG bytes, no resize or re-encode	74	76.06 GB

Annotation layer	Count	Intended use
Human entity masks	1,222,592	canonical polygon localization and segmentation
Kimi-K2.5 semantic mask annotations	1,132,311	region tagging and semantic filtering
Grouped OCR blocks	1,246,351	text-box detection and grouping
Human-tagged OCR blocks	134,248	semantic text-role and foreground/background analysis
Legacy masks	949,193	independent historical Labelbox layer
Legacy role masks	79,668	historical semantic/visual-role layer

Annotation Layers

The layers are related by page identity, not by positional list index:

• human_annotated_masks contains the canonical human polygon geometry. Its stable mask_index joins an optional semantic vlm_annotation.
• ocr.grouped_blocks contains human-grouped OCR geometry and text. It is the supervision used by released text detection checkpoints.
• ocr.human_tagged_blocks is a second text layer with semantic role and foreground/background labels.
• legacy_masks and legacy_role_masks are independent annotations. They must not be zipped or positionally matched with human_annotated_masks.

Semantic Region Labels

Human masks with a successful VLM annotation use 13 public values:

Background Image, Brand Logo, Chart / Graph, Color Block, Decorative / Pattern Graphic, Dialog Box, Icon, Illustration / Artwork, Line / Divider, Other / Not Target, Photograph, Table, and Text Block / Content.

The model name stored in the release is the public name moonshotai/Kimi-K2.5. Masks without a validated annotation keep their geometry and omit the semantic payload; they are not assigned a guessed class.

Loading The Dataset

Hugging Face Datasets

from datasets import load_dataset

dataset = load_dataset("adopd/adopd2026")
sample = dataset["validation"][0]

image = sample["image"]
human_masks = sample["human_annotated_masks"]
text_blocks = sample["ocr"]["grouped_blocks"]

For quick inspection without downloading all shards:

stream = load_dataset(
    "adopd/adopd2026",
    split="validation",
    streaming=True,
)
sample = next(iter(stream))

Direct Parquet Access

import duckdb
from huggingface_hub import HfFileSystem

duckdb.register_filesystem(HfFileSystem())
duckdb.sql("""
    SELECT sample_id, width, height, language
    FROM 'hf://datasets/adopd/adopd2026/data/validation-*.parquet'
    LIMIT 10
""").show()

Released Training Adapters

The companion ADOPD2026 code repository provides lazy Parquet readers and deterministic COCO, YOLO, and LocateAnything exporters. It also contains training and inference commands for all nine released checkpoints.

Row Schema

Each row represents one document page. Split membership is encoded by the Parquet filename and is not duplicated in the row.

Identity And Image

Field	Type	Description
sample_id	string	Stable SHA-256-style page identifier used across released tasks
image	Image	Original JPEG bytes plus a non-absolute display filename
width, height	int32	Original image dimensions in pixels
image_md5	string	MD5 of the exact embedded JPEG bytes
source_url_sha256	string	Non-reversible hash of the source URL for identity checks
language	string	Primary annotation language

Captions And Global Tags

Field	Description
captions.human	Human-authored page caption when available
captions.gpt4v	GPT-4V page caption when available
captions.blip2_opt_6_7b	Optional BLIP2 OPT-6.7B caption
captions.preferred_source	Which non-empty caption source was selected by metadata: human, gpt4v, or source_text
global_tags.human	Human document-level tags
global_tags.clip	CLIP tag/probability records
global_tags.ram_attributes	RAM-generated visual attributes

OCR

Field	Description
ocr.source_text	Source plain text associated with the page
ocr.paddleocr_v4_raw	Retained PaddleOCR v4 output text
ocr.internvl3_78b_text	Retained InternVL3-78B OCR transcription
ocr.grouped_blocks	Canonical grouped text blocks used for Doc2Box
ocr.human_tagged_blocks	Optional human-tagged text blocks with semantic role and visual layer

Each grouped_blocks item contains block_index, group_index, polygon, bbox_xyxy, text, confidence, and source_member_indices. Each human_tagged_blocks item contains the same stable indices and geometry plus text_role and layer.

Human And Legacy Masks

Field	Description
human_annotated_masks	Canonical list of human entity polygons
legacy_masks	Independent historical mask/label list
legacy_role_masks	Historical masks augmented with coarse semantic/visual role
legacy_mask_summary	Page-level historical primary_label and derived label_family
legacy_annotation_project	Source project name for the legacy layer

Each human mask has mask_index, polygons, source_label, bbox_xyxy, area_fraction, and nullable vlm_annotation. A VLM annotation contains only label, model, and short_reason. Each legacy mask contains polygons, label, bbox_xyxy, and area_fraction; a legacy role mask additionally has role.

Quality, Document Metadata, And Statistics

Field	Description
quality.unsafe_probability	Estimated unsafe-content probability
quality.watermark_probability	Estimated watermark probability
quality.caption_image_similarity	Caption-image similarity score
document_metadata.language_group	Coarse language group
document_metadata.language_detail_group	Fine-grained language group
document_metadata.layout_profile	Coarse page layout profile
document_metadata.text_visual_profile	Text-versus-visual content profile
document_metadata.taxonomy_cluster_id	Stable taxonomy cluster identifier
document_metadata.taxonomy_cluster_name	Human-readable taxonomy cluster
document_metadata.taxonomy_assignment_tag	Assignment tag retained from taxonomy analysis
statistics	Page-level size, object, text, polygon, and complexity features

statistics contains aspect_ratio, megapixels, object_count, bbox_count, polygon_count, vertex_count, ocr_block_count, unique_mask_label_count, small_bbox_ratio, bbox_area_std, text_rich_score, visual_rich_score, bbox_complexity_score, segmentation_complexity_score, and aggregate complexity_score.

Data Integrity

• Images are embedded at original resolution and were not re-encoded for this release.
• Polygon-derived boxes and area fractions were recomputed and audited.
• Split IDs are unique and disjoint across train, validation, and test.
• The full staging audit counted all 120,000 rows and decoded representative first/last rows from every split.
• The Hugging Face viewer was validated against the published Parquet commit.
• Numeric source label IDs, local paths, source URLs, run IDs, retries, prompts, endpoint names, and other annotation-process state are intentionally omitted.

The source contained 1,062 Class-Delete raster-only items without renderable polygon geometry. They are excluded from human_annotated_masks; retained mask_index values can therefore be non-contiguous.

Intended Uses And Limitations

ADOPD2026 supports research in document detection, instance segmentation, visual grounding, semantic region classification, OCR grouping, and grounded reasoning. The semantic mask labels are model-assisted annotations and should not be treated as human ground truth without task-appropriate validation. Legacy layers have different collection semantics and are not interchangeable with canonical human masks.

Document pages may contain personal, copyrighted, or sensitive material from their original sources. Users are responsible for complying with the dataset license and applicable law, evaluating demographic and language coverage, and avoiding deployment decisions based solely on automated annotations.

License

ADOPD2026 is released under CC BY-NC-ND 4.0. It is intended for non-commercial research use. Review the license before redistributing data or derived artifacts.

Citation

@misc{zhu2026thinkingwithanchors,
  title={Thinking with Anchors: Grounded and Efficient Document Reasoning},
  author={Sichen Zhu and Yuchen Zhu and Wenzhuo Xu and Jason Kuen and Wanrong Zhu and Jing Shi and Xuan Shen and Quanyi Wang and Yiwei Wang and Yujun Cai and Bing Shuai and Qin Zhang and Yongxin Chen and Shilong Liu and Molei Tao and Jiuxiang Gu},
  year={2026}
}

@inproceedings{gu2024adopd,
  title={{ADOPD}: A Large-Scale Document Page Decomposition Dataset},
  author={Jiuxiang Gu and Xiangxi Shi and Jason Kuen and Lu Qi and Ruiyi Zhang and Anqi Liu and Ani Nenkova and Tong Sun},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024},
  url={https://openreview.net/forum?id=x1ptaXpOYa}
}