---
license: apache-2.0
task_categories:
  - video-text-to-text
  - visual-question-answering
  - image-text-to-text
language:
  - en
tags:
  - llava
  - multimodal
  - video
  - spatial-reasoning
size_categories:
  - 10M<n<100M
configs:
  - config_name: viewer_caption_30s
    data_files:
      - split: preview
        path: viewer/caption_30s.parquet
  - config_name: viewer_caption_60s
    data_files:
      - split: preview
        path: viewer/caption_60s.parquet
  - config_name: viewer_spatial
    data_files:
      - split: preview
        path: viewer/spatial.parquet
---

# LLaVA-OneVision-2-Data

Training data for the LLaVA-OneVision-2 multimodal model family, covering large-scale video and spatial reasoning corpora used in mid-training.

## Dataset Composition

| Subset | Format | Description |
|---|---|---|
| `mid_training_video/60s_rest/` | WebDataset (`.tar`) | 10,809 shards of ~60s video clips |
| `mid_training_video/caption_v0/split_30s.jsonl` | JSONL | Captions for 30-second video clips |
| `mid_training_video/caption_v0/split_60s.jsonl` | JSONL | Captions for 60-second video clips |
| `spatial/` | WebDataset (`.tar`) | 84 shards of spatial reasoning data (refcoco, visual genome, pointing, 3D, etc.) |

## Preview Configs

The three `viewer_*` configs above expose small Parquet samples so the Hugging Face Dataset Viewer can render the data directly in the browser:

- **`viewer_caption_30s`** — 5 caption samples from 30-second clips
- **`viewer_caption_60s`** — 5 caption samples from 60-second clips
- **`viewer_spatial`** — 10 spatial-reasoning samples with embedded thumbnail images, mixed across tasks (refcoco, visual genome, pointing, ca1m, osd, crosspoint, erqa, roborefer)

These previews are intended for **schema inspection only**. For training, use the full `mid_training_video/` and `spatial/` shards.

## Loading Examples

### Stream the full spatial WebDataset
```python
from datasets import load_dataset

ds = load_dataset(
    "mvp-lab/LLaVA-OneVision-2-Data",
    data_files="spatial/train_*.tar",
    split="train",
    streaming=True,
)
for sample in ds:
    print(sample)
    break
```

### Load a viewer preview
```python
from datasets import load_dataset

ds = load_dataset("mvp-lab/LLaVA-OneVision-2-Data", "viewer_spatial", split="preview")
print(ds[0])
```

## Schema

### Caption (`viewer_caption_30s`, `viewer_caption_60s`)
- `images_source: List[str]` — relative paths to source video files
- `messages: List[{role, content}]` — chat-format caption prompt and assistant response
- `preview: str` — flattened text preview

### Spatial (`viewer_spatial`)
- `id: str` — sample id
- `task: str` — source task name (e.g. `refcoco`, `visual_genome`, `pointing`)
- `image: Image` — first referenced image, embedded as JPEG bytes (downscaled to max edge 1024)
- `image_paths: List[str]` — original relative image paths in the full dataset
- `messages: List[{role, content}]` — chat-format Q/A
- `preview: str` — flattened text preview

## License

Apache 2.0