irl-kit/SPARC-VQA-Raw

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}
}