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---
dataset_info:
features:
- name: image_name
dtype: string
- name: image
dtype: image
- name: labelme
dtype: string
- name: obb
dtype: string
splits:
- name: train
num_bytes: 1854583534
num_examples: 5154
- name: validation
num_bytes: 446502719
num_examples: 1288
configs:
- config_name: default
data_files:
- split: train
path: data/train.parquet
- split: validation
path: data/validation.parquet
license: cc-by-4.0
task_categories:
- object-detection
language:
- km
size_categories:
- 1K<n<10K
---
# Graph Dataset: Image, LabelMe, and OBB
This dataset contains graph/chart images paired with LabelMe JSON polygon
annotations and OBB text annotations. Rows are reproducibly divided into
80% training and 20% validation splits.
## Dataset Summary
| Metric | Count |
| --- | ---: |
| Matched image/LabelMe/OBB rows | 6442 |
| Train rows | 5154 |
| Validation rows | 1288 |
| Source image files | 6442 |
| Source LabelMe JSON files | 6442 |
| Source OBB text files | 6442 |
| Train row groups | 52 |
| Validation row groups | 13 |
| Combined parquet size | 2194.49 MB |
## Columns
| Column | Type | Description |
| --- | --- | --- |
| `image_name` | string | Source filename without extension |
| `image` | image | Image decoded by Hugging Face Datasets |
| `labelme` | string | LabelMe annotation serialized as JSON |
| `obb` | string | OBB annotation text from the matching `.txt` file |
## Load the Dataset
```python
import json
from datasets import load_dataset
dataset = load_dataset(
"parquet",
data_files={
"train": "data/train.parquet",
"validation": "data/validation.parquet",
},
)
row = dataset["train"][0]
image = row["image"]
labelme = json.loads(row["labelme"])
obb_lines = row["obb"].splitlines()
```
The parquet file is written in bounded row groups by `create_hf_parquet_dataset.py`
to avoid constructing the complete image dataset in memory during export.
**Last updated:** 2026-05-25