README.md

---
language:
- pt
size_categories:
- 100K<n<1M
task_categories:
- image-to-text
tags:
- image-captioning
- multimodal
- oil-and-gas
- geosciences
- portuguese
configs:
- config_name: images
  data_files:
  - split: train
    path: data/images/train-*.parquet
- config_name: annotations
  data_files:
  - split: train
    path: data/annotations/train-*.parquet
---

# ImREGIS

ImREGIS (Image-REGIS) is a Portuguese multimodal image-text dataset,
automatically extracted from the REGIS collection — a set of technical
documents, theses, and reports from the Oil & Gas (O&G) and Geosciences
domain.

The dataset contains over 439,000 unique images and 581,000
image-text pairs, extracted from more than 20,000 PDF documents,
combining captions (descriptive text placed near the image) and
descriptions (textual references to the image scattered throughout
the document body).

## Structure

The dataset is split into two tables (configs) linked by the `image_id`
column.

A single `doc_id` may have more than one image associated with it
(documents with multiple figures/pages).

### `images`
One row per unique image (deduplicated by `image_id`).

| column | type | description |
|---|---|---|
| `image_id` | string | unique image identifier (join key) |
| `doc_id` | string | source document identifier |
| `width` | int32 | width in pixels |
| `height` | int32 | height in pixels |
| `format` | string | original format |
| `image` | binary (bytes) | raw image content |

### `annotations`
One or more text rows per image.

| column | type | description |
|---|---|---|
| `row_id` | string | unique annotation row identifier |
| `doc_id` | string | source document identifier |
| `image_id` | string | identifier of the associated image (key for `images`) |
| `type` | string | `caption` or `description` |
| `text` | string | text content |
| `lang` | string | language |

## How to load

```python
from datasets import load_dataset

images = load_dataset("Geologi/imregis", "images", split="train")
annotations = load_dataset("Geologi/imregis", "annotations", split="train")
```

Given the dataset size (250GB+), streaming is recommended instead of
loading everything into memory:

```python
images = load_dataset("Geologi/imregis", "images", split="train", streaming=True)
annotations = load_dataset("Geologi/imregis", "annotations", split="train", streaming=True)
```

### Joining images and text

For local use (if it fits in memory/disk), via pandas, always join on
`image_id`:

```python
import pandas as pd

df_images = images.to_pandas()
df_annot = annotations.to_pandas()

df = df_annot.merge(df_images, on="image_id", how="left", suffixes=("", "_img"))
```

For streaming use, perform the join on demand, for example by building an
in-memory index of images (if the `images` table fits) and iterating over
`annotations`:

```python
from datasets import load_dataset

images_index = {
    row["image_id"]: row["image"]
    for row in load_dataset("Geologi/imregis", "images", split="train", streaming=True)
}

for row in load_dataset("Geologi/imregis", "annotations", split="train", streaming=True):
    image_bytes = images_index.get(row["image_id"])
    # use row["text"], row["type"], row["lang"], and image_bytes
```

### Decoding the image bytes

The `image` column contains the raw bytes of the original file. To open
it as an image:

```python
from PIL import Image
import io

img = Image.open(io.BytesIO(images[0]["image"]))
img.show()
```