README.md

metadata

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:

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

Mainland China download mirror:

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:

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:

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.