Datasets:
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license: cc-by-4.0
pretty_name: SOCO-LVLM
viewer: false
task_categories:
- visual-question-answering
tags:
- computer-vision
- multimodal
- object-correspondence
- synthetic-data
---
# SOCO-LVLM
SOCO-LVLM provides multiple-choice semantic object correspondence evaluation data for
LVLMs. This is the SOCO-LVLM v1 release, derived from SOCOv1. The original SOCO
correspondence benchmark is available in
the [GenIntelLab/SOCO](https://huggingface.co/datasets/GenIntelLab/SOCO) dataset repository.
## Repository Layout
```text
GenIntelLab/SOCO-LVLM
SOCO_LVLM/
soco_lvlm_img.tsv
soco_lvlm_imgtxt.tsv
soco_lvlm_txt.tsv
README.md
```
## Variants
- `soco_lvlm_img.tsv`: image-input evaluation variant (approximately 3.24 GB).
- `soco_lvlm_imgtxt.tsv`: image-and-text evaluation variant (approximately 3.24 GB).
- `soco_lvlm_txt.tsv`: text-input evaluation variant (approximately 1.63 GB).
Each TSV uses the columns `question`, `image`, `image_path`, `answer`, `index`, `g_index`,
`qid`, `category`, `A`, `B`, `C`, and `D`.
## Download
Install the Hub client:
```bash
pip install -U huggingface_hub
```
Download all three variants:
```bash
hf download GenIntelLab/SOCO-LVLM --repo-type dataset --local-dir SOCO-LVLM
```
Download only one variant in Python:
```python
from huggingface_hub import hf_hub_download
path = hf_hub_download(
repo_id="GenIntelLab/SOCO-LVLM",
repo_type="dataset",
filename="SOCO_LVLM/soco_lvlm_img.tsv",
)
```
Replace the filename with `soco_lvlm_imgtxt.tsv` or `soco_lvlm_txt.tsv` to select a
different evaluation variant.
## Citation
```bibtex
@misc{duenkel2026soco,
title = {SOCO: Benchmarking Semantic Object Correspondence in Vision Foundation Models},
author = {D{\"u}nkel, Olaf and Sunagad, Basavaraj and Wang, Haoran and
Hoffmann, David T. and Theobalt, Christian and Kortylewski, Adam},
year = {2026},
eprint = {2605.31597},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2605.31597}
}
```
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