Datasets:
metadata
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
SOCOv1
SOCOv1 is a structured object correspondence dataset with rendered object images, per-view keypoint annotations, pair files, and metadata.
The dataset is distributed as an unpacked folder tree on the Hugging Face Hub. Use huggingface_hub.snapshot_download to download the full dataset or selected subfolders.
Folder Structure
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/: rendered object images, organized by category.KeypointAnnotations/: per-view keypoint annotations, organized by category.PairAnnotations/intra/: intra-category image-pair files.PairAnnotations/cross/: cross-category image-pair files.PairAnnotations/trainsplits/: train/test split pair files.Metadata/: keypoint taxonomy and filename mapping.
This release contains 100 categories, 4,000 images, 4,000 keypoint annotation files, and 60,001 pair annotation files.
Download
Install the Hub client:
pip install -U huggingface_hub
Download the full dataset:
from huggingface_hub import snapshot_download
path = snapshot_download(
repo_id="GenIntelLab/SOCO",
repo_type="dataset",
local_dir="SOCOv1",
token=True, # required while the dataset is private
)
Download only selected folders:
from huggingface_hub import snapshot_download
path = snapshot_download(
repo_id="GenIntelLab/SOCO",
repo_type="dataset",
local_dir="SOCOv1_images_and_metadata",
allow_patterns=["Images/**", "Metadata/**"],
token=True,
)
Citation
Citation information will be added with the public release.