| --- |
| 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. |
|
|
