maujim/vsr-sample-500
VSR Sample 500 This repository is a derivative sample of the Visual Spatial Reasoning (VSR) dataset. It contains 500 records and 483 unique COCO images in one train split. It is not the complete VSR corpus and is not a replacement for the upstream dataset. The records were sampled without replacement from the upstream random-train split with deterministic seed 20260905. The sample preserves the source fields and values; the image field points to the bundled local file at… See the full description on the dataset page: https://huggingface.co/datasets/maujim/vsr-sample-500.
VSR Sample 500
This repository is a derivative sample of the Visual Spatial Reasoning (VSR) dataset. It contains 500 records and 483 unique COCO images in one train split. It is not the complete VSR corpus and is not a replacement for the upstream dataset.
The records were sampled without replacement from the upstream random-train split with deterministic seed 20260905. The sample preserves the source fields and values; the image field points to the bundled local file at images/<basename>.jpg. image_link retains the corresponding COCO URL.
Offline viewer
Download the entire repository and keep its folders together, including the images/ directory. Then double-click index.html to open the viewer locally—no installation, local server, or internet connection is needed after download. Browse records with Previous/Next or the keyboard arrow keys, filter and search by label or relation, jump to a random record, and inspect captions, truth labels, image filenames, and metadata. You can also copy a record's JSON for reuse.
Schema
The data/train.jsonl file has exactly 500 records with these features:
Loading
From a local checkout of this repository:
from datasets import load_dataset
dataset = load_dataset("./vsr-sample-500", data_files={"train": "data/train.jsonl"})
print(dataset["train"][0])The JSONL paths are relative to this repository, so the bundled images are available when the dataset is loaded locally. For a published Hub copy, use its dataset identifier instead of the local path; the train configuration maps to data/train.jsonl.
Provenance
VSR is a benchmark of caption-image pairs for judging whether a caption correctly describes a spatial relation in an image. This sample was prepared from the upstream random train split on 2026-09-05 using the deterministic seed stated above. See the upstream VSR repository for the original data, split definitions, implementation, and provenance.
Citation
If you use the original VSR work, please cite:
@article{Liu2022VisualSR,
title={Visual Spatial Reasoning},
author={Fangyu Liu and Guy Edward Toh Emerson and Nigel Collier},
journal={Transactions of the Association for Computational Linguistics},
year={2023},
}Limitations
This is a small, intentionally sampled subset and may not represent the full distribution of VSR relations, captions, labels, or annotators. It contains only the random-train source split and should not be used to infer performance on VSR's other splits or on broader visual reasoning tasks. The labels represent the VSR caption-verification task and do not establish general image or object-recognition capability.
Licensing and image attribution
The VSR repository's code and dataset materials are released under the Apache-2.0 license, which is the license declared for this derivative repository's dataset-card materials. The bundled images originate from the COCO dataset; image rights, terms, and attribution obligations are governed by COCO and the applicable image creators' rights, not by this sample's Apache-2.0 declaration. Review the upstream VSR and COCO terms before redistributing or using the images.
