SynGallery-1024: A Synthetic Gallery of Real Paintings for Instance-Level Artwork Recognition

The 1024×1024 high-resolution edition of patryk-bartkowiak/SynGallery.

A synthetic dataset for instance-level artwork recognition: 4,898 real paintings (MET Open Access) hung in a procedurally randomized 3D art-gallery scene, each rendered from 5 camera viewpoints at 1024×1024 — 24,490 synthetic RGB images paired with their source photos and museum metadata (title, artist, date, medium, …). The environment is randomized per scene — wall/floor/roof textures, lighting, frame molding and color, optional protective glass, placard, painting scale, and camera-pose jitter.

Rendered headlessly with Blender 5.1.2 / Cycles (June 2026); scene variety is driven by Scene-Time-seeded Geometry Nodes, so every painting gets an independently randomized room.

Relationship to the 512² SynGallery. Both datasets share the same index ↔ painting mapping (row N is the same MET artwork in both) and identical schema. They are separate render runs, so the randomized scene around a given painting (lighting, frame, glass, camera jitter, …) differs between the two — they are paired by artwork, not pixel-identical upscales.

Schema

A single train split, 4,898 rows — one row per scene, bundling everything about that scene: the MET source photo, the five camera views rendered from it, and the artwork's museum metadata.

column	type	meaning
index	int32	scene index 0..4897; stable across all SynGallery variants — same index ⇔ same source painting
met_object_id	int32	MET museum object id of the source painting
source_image	Image	the MET source photo itself (web-size JPEG)
image_30 … image_150	Image ×5	the 1024×1024 RGBA PNG renders from cameras 30, 60, 90, 120, 150
title	string	artwork title (MET Open Access)
artist / artist_bio	string	artist display name / bio (may be empty)
object_date	string	display date, e.g. ca. 1580–85
object_begin_date / object_end_date	int32	machine-readable date range (null if unknown)
medium	string	e.g. Oil on copper
classification	string	MET classification, e.g. Paintings
culture / department	string	culture (may be empty) / MET curatorial department
met_url	string	the object's page at metmuseum.org
+ 42 more MET columns	string / bool	every remaining MetObjects.csv field as its own column: object_number, is_highlight / is_timeline_work / is_public_domain (bool), gallery_number, accession_year, object_name, period, dynasty, reign, portfolio, constituent_id, the artist_* details (role, nationality, dates, gender, ULAN/Wikidata URLs, …), dimensions, credit_line, geography (city … river), rights_and_reproduction, object_wikidata_url, metadata_date, repository, tags + tag URLs. Sparse fields are empty strings / nulls
camera_location_30 … _150	float32[3] ×5	each camera's world position at render time (after per-datapoint jitter)
camera_rotation_euler_30 … _150	float32[3] ×5	each camera's world rotation (radians) at render time

Images are embedded bytes only; a render is identified by (index, camera) and the source painting by met_object_id. Rows are ordered by index. There is no held-out split — any row-level split is automatically painting-level, since all 5 views live in the same row.

Scene-randomization factors (wall/floor/roof textures, lighting, frame variant/color, glass presence, painting scale, placard) are baked into the renders but not individually labeled.

Provenance & licensing

Source paintings are photographs of artworks from the MET Open Access collection (CC0). One representative photo per MET object (paintings_train_images_unique.json manifest, consumed in order). Museum metadata is joined from the official MET Open Access MetObjects.csv (also CC0) by object id — the complete record, every field as its own column.

The dataset (the rendered images and their packaging) is released under CC BY 4.0 — please attribute the dataset and cite the accompanying paper. The 3D scene bakes in third-party PBR textures and HDRI environment maps; those assets remain under their respective licenses and are distributed here only as non-extractable, baked pixels.

Citation

A paper describing SynGallery is in preparation. Citation details (BibTeX) will be added here once available.

Acknowledgments

This dataset was created in collaboration with ArtiCollect.