docs/data-dictionary.md

Data dictionary

Images and ImageFolder rows

data/test/metadata.jsonl has one row per image. file_name is relative to data/test/; the manifest uses paths relative to the repository root.

Field	Meaning
image_id	Stable string identifier; join key across native files
category	synthetic or trap; both contain generated images
width, height	Original raster dimensions in pixels
plausibility, naturalness	Final ordinal ratings, integers 1–5
no_violation_detected	True only for a usable negative
method_id	Ground-truth resolution method, e.g. GT-004
objects.category_id	List of integer class IDs, 1–97
objects.bbox	Parallel list of normalised [x, y, width, height] boxes

The loader exposes image as a decoded PIL image. Empty object lists are valid negative examples. There is one evaluation test split, no training split. release/image_manifest.jsonl adds the original image SHA-256 and byte count.

IDs and categories are strings; dimensions and class IDs are integers. Boxes contain four floating-point values. Ratings are integers, and no_violation_detected is a boolean. The two objects lists have the same length: elements at the same index describe one instance. category records the source grouping, not the final positive/negative judgement.

Native annotations

annotations/ground_truth.json is an array of records with image_id, kind, gold, base_annotator, source_annotators, adjudicated_by and edit_summary. gold.violations contains use_case_id (U01–U97) and normalised XYWH bbox. It also contains the quality ratings and usable/negative state. The complete resolution provenance is in edit_summary.resolution, including method_id, name and version. kind alone does not identify how gold was obtained.

gold.reported is false for every released final annotation. A usable negative has an empty violation list and no_violation_detected: true; a positive has one or more violations and no_violation_detected: false. Each violation retains its class name. Bounding boxes have positive width and height, with all edges inside the full-image range [0, 1].

agreement_at_gold is an optional historical agreement snapshot, not a new comparison of final labels. base_annotator and adjudicated_by can be null; actor references are opaque pseudonyms, not sortable identities. For rating meanings, use the annotation rubric.

annotations/first_pass_annotations.json is an array joined by image_id. first_pass holds original per-annotator payloads; n_first_pass is their count. Actor IDs remain pseudonyms. Administrative text, personnel timestamps and local replay paths are nulled; free-form notes are emptied. Class labels, box coordinates, ratings, state, record order and provenance identifiers are preserved. The original local files have different hashes because these administrative fields were removed. Source hashes are retained in provenance.

First-pass records also retain their original agreement snapshot and state flags. A reported or unfinished submission must not be inferred to be a clean negative merely because it has no boxes. n_first_pass is the actual number of submissions; do not assume exactly two. Null administrative fields mean that information is not distributed, rather than that the event never occurred.

COCO compatibility

COCO stands for Common Objects in Context, a widely used object-detection annotation format. ground_truth_coco.json is a derived compatibility export:

• images[].id is a deterministic integer starting at 1, in sorted stable-ID order. images[].image_id retains the native string join key.
• images[].file_name is relative to the dataset repository root.
• annotations[].image_id references that integer image ID.
• category_id 1 maps to U01, through 97 mapping to U97.
• bbox is pixel XYWH. Multiply native x/width by image width and native y/height by image height. Coordinates are not rounded.
• area is pixel width × height, and iscrowd is 0. Segmentation is absent.

All images, including negatives with no annotation rows, occur in images. COCO does not carry the full native rating and resolution provenance; use the native file when those fields are required.

Results and provenance

evaluation/dataset/ holds aggregate composition, first-pass agreement and final rating distributions. evaluation/models/synthetic11534/results.json holds valid/common scopes, coverage and per-class metrics; CSV is a summary. Prediction JSONL stores image_id, model_name, latency_ms, parse_status and detections. Each detection uses an integer category_id (1–97), a normalised XYWH bbox and, where available, a score. Retained scores are evidence and do not control evaluation.

release/checksums.sha256 covers every publication file except itself. release/provenance.json identifies the release, licence and original source filenames and hashes. Source hashes identify the original artifacts before administrative fields were removed; the checksum inventory covers the distributed files.

Worked example

Suppose an image is 1,000 pixels wide and 800 pixels high. A native violation with use_case_id: "U03" and bbox: [0.10, 0.20, 0.30, 0.25] is litter with its top-left corner at pixel (100, 160), width 300 and height 200. The equivalent COCO box is [100, 160, 300, 200]; its bottom-right corner is (400, 360). This example is illustrative rather than an additional dataset record.

To trace a real example, take image_id from a loaded row, find the same key in both native annotation arrays, and use release/image_manifest.jsonl to locate its image. Join COCO annotations through the COCO integer images[].id; that integer is not the native string ID. Class integer 3 maps to U03 in every representation.

A class can occur in several boxes on one image. Keep those instances for localisation; deduplicate only the class IDs for image-level classification.