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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
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