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README.md

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+---
+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()
+```