---
pretty_name: V-Zero 5K
license: other
task_categories:
- visual-question-answering
size_categories:
- 1K<n<10K
tags:
- multimodal
- reinforcement-learning
- on-policy-distillation
- v-zero
---

# V-Zero 5K

This repository contains 5,013 multimodal training examples for V-Zero.
The training image columns are `images`, `teacher_images`, `teacher_neg_images`,
and the ablation column `teacher_random_images`.
The published Parquet intentionally omits `data_source`, `ability`,
`reward_model`, and `extra_info`.

All published image paths are relative to the repository root. Run
`materialize_paths.py` after downloading to create a parquet containing
machine-local absolute paths for the V-Zero training launcher.

## Download

Official endpoint:

```bash
hf download hao05/v-zero-5k --repo-type dataset --local-dir ./v-zero-5k
```

Mainland China download mirror:

```bash
HF_ENDPOINT=https://hf-mirror.com \
  hf download hao05/v-zero-5k --repo-type dataset --local-dir ./v-zero-5k
```

Extract and verify the image shards:

```bash
python ./v-zero-5k/extract_image_shards.py --root ./v-zero-5k
```

The 16 TAR shards are uncompressed because the contained JPEG/PNG files are
already compressed. Keeping TAR uncompressed avoids recompression overhead and
supports deterministic SHA-256 verification.

Materialize local paths:

```bash
python ./v-zero-5k/materialize_paths.py \
  --input ./v-zero-5k/data/train.parquet \
  --output ./v-zero-5k/data/train.local.parquet \
  --root ./v-zero-5k
```

## Source composition

| Source | Examples |
| --- | ---: |
| `chartqa` | 314 |
| `docvqa` | 437 |
| `gqa_global` | 14 |
| `gqa_relation` | 320 |
| `perception` | 3,297 |
| `reasonseg` | 93 |
| `tallyqa` | 277 |
| `textvqa` | 261 |

## License and responsible release

This is a mixed-source dataset. Users are responsible for checking the
redistribution terms, attribution requirements, generated-annotation terms,
privacy constraints, and source citations before reuse.