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End of preview. Expand in Data Studio
Motif Segmentation in Paintings
Segmentation masks and metadata for trees detected in a corpus of paintings, produced with SAM3 as part of research in the golden matrix project at the Center for Humanities Computing Aarhus (chcaa).
Dataset Description
This dataset supports a studies of landscape and tree motifs in 19th-century Northern European paintings. It covers 4,727 paintings drawn from museum and Wikidata sources, with tree instances detected using [SAM3]
For each detected tree, the dataset records a bounding box, a confidence score, and a segmentation mask, alongside painting-level metadata (artist, nationality, date, collection)
Contents
Each row corresponds to one painting and includes:
filename,image— original image filename and raw image bytestitle,artist,Artist-nationality,inception,collection,location,inventory_number,qid,wikidata_url,image_url_raw,image_filename— painting metadataImage-rights— rights/licensing information for the original image, as recorded per paintingoriginal_height,original_width— original image dimensionsboxes,scores,count— detected tree bounding boxes, confidence scores, and countembedding— image embedding vectormask— raw bytes of an.npzfile; load withnp.load(io.BytesIO(row["mask"]))["masks"]to get a boolean array of shape(count, 1, H, W)
the dataset further gives results for:
- trees-with-predictions.parquet - Tree detection predictions associated with the paintings.
- classification/ - Data used for image classification experiments.
- ablation-scores/ - Results from ablation experiments evaluating the influence of visual features on classification.
- heads/ - Trained classification head weights for SigLIP.
The dataset can be used to investigate:
The frequency and visual characteristics of trees in historical landscape paintings.
Relationships between tree motifs, visual composition, and national artistic traditions.
The performance and robustness of computer vision models on art-historical images.
Connections between object-level detection and image-level classification.
License
This dataset (masks, metadata, embeddings, and annotations produced by the chcaa team (Rie Schmidt Eriksen, Louise Brix Hansen & Marta Kipke)) is released under CC-BY-4.0. Please credit authors when reusing this data.
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