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license: cc-by-4.0
pretty_name: SOCOv1
viewer: false
task_categories:
- image-to-image
tags:
- computer-vision
- keypoint-detection
- object-correspondence
- synthetic-data
size_categories:
- 10K<n<100K
---
# SOCO
SOCO is a semanti object correspondence dataset with images, per-view keypoint annotations, pair annotations that for evaluation of visual features.
Evaluation code is available at the [OmniProbe repo](https://github.com/GenIntel/OmniProbe).
## Repository Layout
```text
GenIntelLab/SOCO
Images.zip # -> Images/<category>/*.JPEG
KeypointAnnotations.zip # -> KeypointAnnotations/<category>/*.json
PairAnnotations.zip # -> PairAnnotations/{intra,cross,trainsplits/{train,test}}/<category>/*.json
Metadata/
filename_mapping.json
keypoint_taxonomy.json
README.md
```
After unzipping, the dataset tree is:
```text
SOCOv1/
Images/<category>/*.JPEG
KeypointAnnotations/<category>/*.json
PairAnnotations/
intra/<category>/*.json
cross/<category>/*.json
trainsplits/
train/<category>/*.json
test/<category>/*.json
Metadata/
filename_mapping.json
keypoint_taxonomy.json
```
## Contents
- `Images.zip`: rendered object images, organized by category.
- `KeypointAnnotations.zip`: per-view keypoint annotations, organized by category.
- `PairAnnotations.zip`: image-pair files for intra-category (`intra`), cross-category (`cross`), and the predefined train/test splits (`trainsplits`).
- `Metadata/`: keypoint taxonomy and filename mapping (shipped unzipped).
This release contains 100 categories, 4,000 images (40 per category), 4,000 keypoint annotation files, and 60,000 pair annotation files (20,000 intra-category, 20,000 cross-category, and a 10,000 / 10,000 intra-category train / test split).
## Download
Install the Hub client:
```bash
pip install -U huggingface_hub
```
Download the repository and unpack the archives:
```bash
huggingface-cli download GenIntelLab/SOCO --repo-type dataset --local-dir SOCOv1
cd SOCOv1 && for z in *.zip; do unzip -q "$z" && rm "$z"; done
```
Equivalently in Python:
```python
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="GenIntelLab/SOCO",
repo_type="dataset",
local_dir="SOCOv1",
)
# then unzip Images.zip, KeypointAnnotations.zip, PairAnnotations.zip inside SOCOv1/
```
## Using the data
After extraction the folder layout maps directly onto a dataset-root path: point your
loader at the `SOCOv1/` directory and read images from `Images/<category>/` and pairs from
the relevant `PairAnnotations/` subfolder. For evaluation, use `PairAnnotations/intra`
(within-category pairs). For training and evaluating a probe, use the predefined
`PairAnnotations/trainsplits/train` and `PairAnnotations/trainsplits/test` splits. Each
pair file references two views and their corresponding keypoints. Per-view keypoints are
also available under `KeypointAnnotations/<category>/`.
## Citation
```
@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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