Datasets:
license: other
license_name: mixed-upstream-licenses
task_categories:
- object-detection
language:
- en
tags:
- scene-graph-generation
- visual-relationship-detection
- open-vocabulary
- benchmark
- evaluation
pretty_name: OV-SGG-Bench
size_categories:
- 10K<n<100K
OV-SGG-Bench: evaluation packs for open-vocabulary relation prediction
The six evaluation sources of OV-SGG-Bench, the protocol of
RelateAnything (code · paper:
RelateAnything: Real-Time Open-Vocabulary Relation Prediction From Any
Inputs), packed in the memmap format the evaluators read, plus the
negative-set and cell files the scorers need and the training-side artifacts
the shipped recipe names. No images are redistributed: every pack stores
boxes, labels and file names, and the evaluator reads pixels from your copy of
the source dataset (set RA_DATASETS).
The protocol has six axes, chosen so that no single one can be won by matching
a benchmark's annotation prior. The definition, the sources, and the reasons
are in
benchmark/SPEC.md.
| pack | images | role | axis |
|---|---|---|---|
vg150 (test) |
26,404 | in-domain control (100 % vocabulary overlap with VG150-trained baselines) | A1, A3 |
psg (test) |
2,179 | primary transfer, COCO images, 56 predicates | A1, A3, A4, A5 |
indoorvg (test) |
4,403 | domain shift | A1, A3 |
hicodet (test) |
9,490 | 116 external verbs; federated negatives (its train split, 33,415 images, is a training source) |
A1, A2 |
haystack |
11,368 | explicit negatives on SA-1B images (no image overlap with any training set) | A2 |
spatialsense, spatialsense_test, spatialsense_valid |
1,920 test images, 2,758 balanced true/false triples | adversarial spatial understanding | A6 |
Files
| path | contents |
|---|---|
packs/<name>/<split>/ |
meta.json, file_names.json, img_meta.npy, boxes.npy, box_cats.npy, rels.npy |
datamix/haystack_negatives.json, datamix/hicodet_negatives*.json |
explicit negative cells for the A2 precision axis |
datamix/spatialsense_{test,valid}_cells.json |
the balanced true/false cells for A6 |
datamix/indoorvg_holdout.json |
image ids excluded from training (leakage audit) |
datamix/registry.json, vg2coco.json, psg2coco.json |
id registry used by the converters |
detect/yoloworld_ov_psg_test.npz |
the shared YOLO-World detections on PSG test that A4 and A5 score every system on |
text_student_v2_512/student.pt |
the distilled predicate text encoder every released checkpoint was trained with |
datamix_v22/pair_opportunity.npz, datamix_v22/text_space/* |
the soft-supervision and synonym artifacts the training recipe names (train.sh) |
Use
Two commands from a clone of RelateAnything:
pip install -e ".[train,hub]"
python benchmark/setup_data.py # this dataset, the test images (~15 GB), every pack relocated and checked
python benchmark/run_benchmark.py --checkpoint maelic/relsgg-vits16plus
setup_data.py --axes a1 a4 a6 downloads only the images those axes read. The images come
from their sources (VG150, PSG and IndoorVG from maelic/*-coco-format, HICO-DET from
zhimeng/hico_det, Haystack from its authors, SpatialSense from Zenodo); none are
redistributed here. Each pack's meta.json records img_dir as datasets/<source>/<split>,
which setup_data.py re-roots at $RA_DATASETS.
run_benchmark.py takes a Hugging Face model id or any checkpoint of the recipe and prints
OVS-F1 over A1, A2, A4 and A6. A5, the graph-quality axis, needs a VLM judge (vLLM,
Qwen/Qwen3.6-27B) and runs only with --judge; it adds the fifth axis of the paper's
composite. The protocol and the composite are defined in
benchmark/SPEC.md
and docs/evaluation.md.
Licenses
Each pack retains the license of its source: Visual Genome / VG150 and
IndoorVG (CC BY 4.0), PSG (COCO images, CC BY 4.0), HICO-DET (research use),
Haystack (SA-1B research license), SpatialSense (CC BY 4.0). The text student
and the datamix_v22 artifacts are derivatives of Meta DINOv3 (dino.txt) and
follow the DINOv3 license.
Citation
@article{neau2026relateanything,
title = {RelateAnything: Real-Time Open-Vocabulary Relation Prediction From Any Inputs},
author = {Neau, Ma\"elic},
journal = {arXiv preprint arXiv:2609.12552},
eprint = {2609.12552},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2609.12552},
year = {2026}
}