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STRAP: STructured Region Annotations single Pass

STRAP is a large-scale source of structured region supervision for vision-language models. It contains annotations for 2,000,000 web images: 1M from Conceptual Captions 3M (CC3M) and 1M from DataComp-1B. Across both subsets, STRAP describes 10.35 million objects.

Each object connects a normalized bounding box to a basic-level label, naming hierarchy, short description, attributes, visible parts, and categories the object could be mistaken for. Objects cross-reference each other by ID in interactions and occlusion relations; readable text is annotated both on objects and at scene level. This supports region-text representation learning, open-vocabulary recognition, attribute and part supervision, relation modeling, and dense captioning without requiring users to run the annotation model themselves.

All annotations for an image were generated together in one structured pass of the multimodal large language model (MLLM) Qwen3.6-35B-A3B.

For more details, see the dataset website.

If you find the dataset useful, please cite us - an arxiv paper is coming soon :) We would also love to hear about your experience and use cases!

STRAP pipeline: a frozen MLLM sees each image once with a fixed prompt and JSON schema, and returns one structured record per image with image-level fields and one detailed card per object

Images are not included. STRAP contains annotations only, with no image bytes. Obtain images from the original distributions and join them using the provided keys; see Getting the images.

Dataset Summary

	`cc3m`	`datacomp`
Images (rows)	1,000,000	1,000,000
Annotated objects	6,229,195	4,121,158
Objects per image (mean)	6.2	4.1
Image source	CC3M	DataComp-1B
Primary join key	`tsv_index` (row in Google's CC3M TSV)	`uid` (DataComp-1B sample ID)
MLLM annotator	Qwen3.6-35B-A3B	Qwen3.6-35B-A3B

Created and released by the Visual Recognition Group FEE CTU in Prague. Annotations are released under CC BY 4.0.

Quick Start

Stream the dataset to inspect records without first downloading an entire config:

from datasets import load_dataset

cc3m = load_dataset(
    "vrg-prague/STRAP", "cc3m", split="train", streaming=True
)
example = next(iter(cc3m))

Use the datacomp config name for the DataComp subset. Omit streaming=True to download and load a complete config.

Example

The annotations of one image (bounding boxes and basic labels):

STRAP example: a squirrel eating a nut in a snowman teacup, with all annotated bounding boxes

The same annotation as a record (abbreviated: one of its seven objects and two of its four interactions shown, floats rounded; every object carries the full structure):

{
  "tsv_index": 1730823,
  "url": "https://i.pinimg.com/736x/a1/03/88/a1038843468be4725af63cabb2532d4e--secret-squirrel-winter-scenes.jpg",
  "original_caption": "i 'm going nuts over this squirrel",
  "width": 600, "height": 451,
  "cc3m_wds_key": "001779031",
  "image_summary": "A close-up photograph of a squirrel sitting inside a large, blue, snowman-themed teacup, eating a nut against a snowy background.",
  "scene_context": "A close-up outdoor scene with a shallow depth of field, featuring a squirrel in a decorative cup against a blurred snowy background.",
  "objects": [
    {
      "id": 1,
      "basic_label": "squirrel",
      "salience": 0.95,
      "brief_description": "A squirrel sitting inside a teacup, eating a nut.",
      "bounding_box": [0.33, 0.10, 0.85, 0.72],
      "hierarchy": {
        "coarser_levels": [{"label": "mammal", "confidence": 0.99}, {"label": "rodent", "confidence": 0.99}],
        "basic_confidence": 0.99,
        "finer_levels": [{"label": "red squirrel", "confidence": 0.85}],
        "confidence_note": "The reddish fur on the tail and back strongly suggests a red squirrel, though the greyish body is also common in some populations or lighting."
      },
      "intrinsic_parts": ["head", "torso", "left_front_paw", "right_front_paw", "bushy_tail", "whiskers", "ears"],
      "attributes": {
        "colors": ["grey", "brown", "rust-red"],
        "materials": ["fur"],
        "pose_or_state": "sitting, eating",
        "other": ["long whiskers", "large dark eye"]
      }
      // ... visibility, anchor_point, count, confusable_with, context_typicality, ...
    }
    // ... 6 more objects (nut, teacup, saucer, ...)
  ],
  "interactions": [
    {
      "object_ids": [1, 2],
      "kind": "eating",
      "direction": {"from_object_id": 1, "to_object_id": 2, "is_directional": true},
      "note": "The squirrel (1) is holding and eating the nut (2)."
    },
    {
      "object_ids": [1, 3],
      "kind": "sitting_in",
      "direction": {"from_object_id": 1, "to_object_id": 3, "is_directional": true},
      "note": "The squirrel (1) is sitting inside the teacup (3)."
    }
    // ... 2 more interactions
  ]
}

Dataset Structure

STRAP has two configs (cc3m, datacomp), each with one train split stored as 20 parquet shards. One row represents one image.

Row fields

column	type	description
`tsv_index`	int	(cc3m only) stable 0-based row join key into Google's original `Train_GCC-training.tsv`
`uid`	string	(datacomp only) DataComp-1B's canonical sample ID (`md5(text + url)`), matches the `uid` column of the official metadata
`url`	string	original image URL
`original_caption`	string	original web alt-text caption: the caption from the official CC3M TSV / the `text` field of the DataComp-1B metadata, reproduced exactly
`width`, `height`	int	pixel size of the annotated image
`cc3m_wds_key`	string	(cc3m only) `__key__` in the hosted `pixparse/cc3m-wds` mirror (unique per row)
`image_summary`	string	one-sentence summary of the image
`scene_context`	string	scene/setting description
`objects`	list<struct>	annotated objects, see below
`interactions`	list<struct>	`object_ids`, `kind`, `direction{from_object_id, to_object_id, is_directional}`, `note`
`global_text`	list<struct>	readable text not tied to an object: `text`, `location`, `legibility`
`uncertain_items`	list<struct>	`description`, `possible_classes`, `reason`
`annotation_json`	string	the verbatim model output; a lossless superset of the typed columns

Object fields

Each element of objects (a ? after a type marks a nullable field):

field	type	description
`id`	int	object ID, referenced by `interactions`, `occluded_by_id`, `associated_items.object_id`
`basic_label`	string	basic-level category name
`hierarchy`	struct?	`coarser_levels`/`finer_levels` (lists of `{label, confidence}`), `basic_confidence`, `confidence_note`; null for low-salience objects
`salience`	float	0 to 1; ≥ 0.5 is the schema's "salient object" threshold
`brief_description`	string	one-line free-text description
`bounding_box`	[float]×4	normalized `[x0, y0, x1, y1]`
`anchor_point`	[float]×2	normalized representative point
`bounding_description`	string?	textual location ("upper left corner", …)
`count`	int?	instance count for group boxes
`group_id`	int?	shared ID when several rows form one group
`visibility`	string	`fully_visible` / `partially_occluded` / …
`occluded_by_id`	int?	ID of the occluding object
`rendition`	struct?	`{medium, depicts}` when the object depicts something (statue, drawing, screen, …)
`confusable_with`	list	`{label, reason}`: categories the object could be mistaken for, each with a reason
`context_typicality`	struct?	`{is_typical_context, context_note}`
`intrinsic_parts`	list<string>	structural parts (head, wheel, …)
`associated_items`	list	`{item, relation, covers, object_id}`: worn/carried/mounted items
`attributes`	struct	`colors`, `materials`, `pose_or_state`, `size_cue`, `expression`, `markings`, `clothing`, `condition`, `other`
`text_on_object`	list	`{text, location_on_object, legibility}`
`recognition_requires_text`	bool	label depends on reading text
`recognition_requires_context`	bool	label depends on scene context

Nullable nested structs are null when absent (e.g. hierarchy for low-salience objects). annotation_json preserves the raw model record verbatim.

Coordinate convention

bounding_box = [x0, y0, x1, y1] and anchor_point = [x, y], both normalized to [0, 1] relative to the full image. They remain valid at any image resolution. width and height are the pixel dimensions of the image the annotator saw; use them to map a box into that image's pixel coordinates (pixel_box = [x0·W, y0·H, x1·W, y1·H]) and to check whether a downloaded image matches the annotated version.

Getting the images

`cc3m` config

Official CC3M (Google's TSV release): join on tsv_index (0-based row index of Train_GCC-training.tsv) or on url.
Hosted images (pixparse/cc3m-wds): join on cc3m_wds_key (the WebDataset __key__). The easiest way to obtain the images themselves.

`datacomp` config

Official DataComp-1B (metadata): join on uid (the metadata's sample ID), or download images directly from url (e.g. with img2dataset).

Annotation Process

For each image, Qwen3.6-35B-A3B (the Intel int4-mixed AutoRound quantization) received the image, a fixed instruction prompt, and a fixed JSON schema. Source captions were not provided to the annotator. The model produced the image-level and object-level fields together in one response, which keeps boxes, descriptions, attributes, and relations connected through shared object IDs.

Records were checked for successful parsing and schema conformance. This validation guarantees a consistent machine-readable structure, not semantic correctness; labels, descriptions, and localization remain model-generated.

Considerations for Using the Data

Intended Use

STRAP is intended for training and analyzing region-level vision-language representations, including region-text alignment, open-vocabulary recognition, dense captioning, region attributes and parts, object relations, and structured MLLM annotation at scale.

Not Intended For

STRAP is not a substitute for human-verified detection ground truth. In particular, it should not be used as the sole reference for precise localization evaluation, calibrated confidence evaluation, or for making judgments about individuals depicted in the images. It also does not provide an image distribution; users must obtain images under the terms of the source datasets.

Limitations

Annotations are model-generated, so labels, boxes, descriptions, and attributes may contain errors. Bounding boxes are MLLM estimates rather than detector outputs and should be treated as approximate localization. Confidence fields reflect the annotator's self-assessment, not calibrated probabilities. The underlying CC3M and DataComp-1B images are web-scale data and inherit the biases of those collections.

Licensing

Generated annotations (image_summary, scene_context, objects, interactions, global_text, uncertain_items, annotation_json): CC BY 4.0. They were generated with Qwen3.6-35B-A3B, released under Apache 2.0.
Source and join metadata (url, original_caption, width, height, tsv_index, cc3m_wds_key, uid) is redistributed solely to enable joining: CC3M metadata under the Conceptual Captions terms (attribution of the source required), and DataComp-1B metadata under CC BY 4.0.
Images are not distributed here. CC3M images/URLs are subject to the Conceptual Captions terms; DataComp-1B content is subject to its source terms.

Dataset Card Contact

Visual Recognition Group FEE CTU in Prague (group website)

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