pretty_name: SPARC VQA Raw
tags:
- robotics
- vision-language
- spatial-reasoning
- sparc
SPARC VQA Raw
This repository contains the unfiltered SPARC VQA corpus: 838,211 embedded-image training examples in train.parquet (33.20 GB). Each example contains an image, question, answer, task metadata, source identifier, and annotation metadata including selected_start_score.
Ready-to-train version
For the exact processed SPARC subset used by the released Qwen3.5 models, download train_filtered_t097_mpo700.parquet from irl-kit/SPARC-VQA. It contains 284,909 examples and can be used directly without SPARC postprocessing.
After download, load it as a Parquet dataset:
from datasets import load_dataset
dataset = load_dataset(
"parquet",
data_files="train_filtered_t097_mpo700.parquet",
split="train",
)
Reproduce the release filter
To create the same subset from this unfiltered corpus, download train.parquet and run the included export_sparc_training_subset.py in the same directory:
python export_sparc_training_subset.py \
train.parquet \
train_filtered_t097_mpo700.parquet \
--quality-threshold 0.97 \
--max-per-object 700 \
--blocked-vacant-location gripper
The filter applies selected_start_score >= 0.97, sorts retained records by descending score, retains complete trajectory-subtask groups for the first 700 groups per normalized object phrase, and removes vacant-location prompts referring to a gripper.
For a custom raw-data subset, change any of --quality-threshold, --max-per-object, or --blocked-vacant-location, then load the resulting Parquet with the same load_dataset("parquet", ...) call above. The output preserves the embedded Hugging Face-compatible image column.
Data schema
| Field | Description |
|---|---|
sample_id |
Stable example identifier |
image |
Embedded image bytes and optional path |
question |
User text prompt |
answer |
Supervised assistant answer |
target_type, task_type |
Spatial target and VQA task labels |
source, split, metadata |
Provenance and generation metadata |
ours_adaptive_det_soft_snr_sp8.yaml records the raw SPARC VQA generation configuration. release_mixture.yaml records the SPARC and external-dataset mixture used for each released model.
Citation
@article{blank2026sparc,
title={SPARC: Reliable Spatial Annotations from Robot Demonstrations at Scale},
author={Blank, Nils and others},
journal={arXiv preprint arXiv:2606.13497},
year={2026}
}