File size: 2,626 Bytes
81d60fa d57fffa 81d60fa d57fffa 81d60fa d57fffa 251f04d d57fffa 251f04d d57fffa 81d60fa 251f04d 81d60fa 251f04d 07aa0c9 81d60fa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | ---
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](https://huggingface.co/datasets/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:
```python
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:
```bash
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
```bibtex
@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}
}
``` |