Datasets:

biglam
/

illustrated_ads

@@ -71,164 +71,48 @@ configs:
     path: data/train-*
 ---
-The Dataset contains images derived from the [Newspaper Navigator](https://news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection (chroniclingamerica.loc.gov/).
-> [The Newspaper Navigator dataset](https://news-navigator.labs.loc.gov/) consists of extracted visual content for 16,358,041 historic newspaper pages in Chronicling America. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the Beyond Words crowdsourcing project. source: https://news-navigator.labs.loc.gov/
-One of these categories is 'advertisements'. This dataset contains a sample of these images with additional labels indicating if the advert is 'illustrated' or 'not illustrated'.
-This dataset was created for use in a [Programming Historian tutorial](http://programminghistorian.github.io/ph-submissions/lessons/computer-vision-deep-learning-pt1). The primary aim of the data was to provide a realistic example dataset for teaching computer vision for working with digitised heritage material.
-# Dataset Card for 19th Century United States Newspaper Advert images with 'illustrated' or 'non illustrated' labels
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-## Dataset Description
-- **Homepage:**
-- **Repository:**[https://doi.org/10.5281/zenodo.5838410](https://doi.org/10.5281/zenodo.5838410)
-- **Paper:**[https://doi.org/10.46430/phen0101](https://doi.org/10.46430/phen0101)
-- **Leaderboard:**
-- **Point of Contact:**
-### Dataset Summary
-The Dataset contains images derived from the [Newspaper Navigator](news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection (chroniclingamerica.loc.gov/).
-> [The Newspaper Navigator dataset](https://news-navigator.labs.loc.gov/) consists of extracted visual content for 16,358,041 historic newspaper pages in Chronicling America. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the Beyond Words crowdsourcing project. source: https://news-navigator.labs.loc.gov/
-One of these categories is 'advertisements. This dataset contains a sample of these images with additional labels indicating if the advert is 'illustrated' or 'not illustrated'.
-This dataset was created for use in a [Programming Historian tutorial](http://programminghistorian.github.io/ph-submissions/lessons/computer-vision-deep-learning-pt1). The primary aim of the data was to provide a realistic example dataset for teaching computer vision for working with digitised heritage material.
-### Supported Tasks and Leaderboards
-- `image-classification`: the primary purpose of this dataset is for classifying historic newspaper images identified as being 'advertisements' into 'illustrated' and 'not-illustrated' categories.
-### Languages
-[More Information Needed]
-## Dataset Structure
-### Data Instances
-An example instance from this dataset
-``` python
-{'file': 'pst_fenske_ver02_data_sn84026497_00280776129_1880042101_0834_002_6_96.jpg',
- 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=388x395 at 0x7F9A72038950>,
- 'label': 0,
- 'pub_date': Timestamp('1880-04-21 00:00:00'),
- 'page_seq_num': 834,
- 'edition_seq_num': 1,
- 'batch': 'pst_fenske_ver02',
- 'lccn': 'sn84026497',
- 'box': [0.649412214756012,
-  0.6045778393745422,
-  0.8002520799636841,
-  0.7152365446090698],
- 'score': 0.9609346985816956,
- 'ocr': "H. II. IIASLKT & SOXN, Dealers in General Merchandise In New Store Room nt HASLET'S COS ITERS, 'JTionoMtii, ln. .Tau'y 1st, 1?0.",
- 'place_of_publication': 'Tionesta, Pa.',
- 'geographic_coverage': "['Pennsylvania--Forest--Tionesta']",
- 'name': 'The Forest Republican. [volume]',
- 'publisher': 'Ed. W. Smiley',
- 'url': 'https://news-navigator.labs.loc.gov/data/pst_fenske_ver02/data/sn84026497/00280776129/1880042101/0834/002_6_96.jpg',
- 'page_url': 'https://chroniclingamerica.loc.gov/data/batches/pst_fenske_ver02/data/sn84026497/00280776129/1880042101/0834.jp2'}
 ```
-### Data Fields
-[More Information Needed]
-### Data Splits
-The dataset contains a single split.
-## Dataset Creation
-### Curation Rationale
-[More Information Needed]
-### Source Data
-#### Initial Data Collection and Normalization
-[More Information Needed]
-#### Who are the source language producers?
-[More Information Needed]
-### Annotations
-#### Annotation process
-A description of the annotation process is outlined in this [GitHub repository](https://github.com/Living-with-machines/nnanno)
-[More Information Needed]
-#### Who are the annotators?
-[More Information Needed]
-### Personal and Sensitive Information
-[More Information Needed]
-## Considerations for Using the Data
-### Social Impact of Dataset
-[More Information Needed]
-### Discussion of Biases
-[More Information Needed]
-### Other Known Limitations
-[More Information Needed]
-## Additional Information
-### Dataset Curators
-[More Information Needed]
-### Licensing Information
-[More Information Needed]
-### Citation Information
-``` bibtex
 @dataset{van_strien_daniel_2021_5838410,
   author       = {van Strien, Daniel},
   title        = {{19th Century United States Newspaper Advert images
@@ -239,12 +123,4 @@ A description of the annotation process is outlined in this [GitHub repository](
   version      = {0.0.1},
   doi          = {10.5281/zenodo.5838410},
   url          = {https://doi.org/10.5281/zenodo.5838410}}
 ```
-[More Information Needed]
-### Contributions
-Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.

     path: data/train-*
 ---
+# 19th Century US Newspaper Adverts — illustrated or text-only
+549 advertisement images cut from digitised United States newspaper pages in the Library of Congress [Chronicling America](https://chroniclingamerica.loc.gov/) collection, each labelled `text-only` or `illustrations`. The adverts were located by [Newspaper Navigator](https://news-navigator.labs.loc.gov/) (LC Labs), which ran an object detection model over 16,358,041 Chronicling America pages to extract visual content; advertisements are one of its categories.
+The sample is even by year, not by title: 61 adverts from each of nine years at five-year intervals, 1860–1900, drawn from 301 newspaper titles across 245 places of publication. Sampling and annotation used [nnanno](https://github.com/Living-with-machines/nnanno).
+It was built as teaching data for the Programming Historian lesson *Computer Vision for the Humanities* ([10.46430/phen0101](https://doi.org/10.46430/phen0101)) — a realistic rather than clean example for teaching image classification on digitised heritage material.
+## Labels
+| label | count |
+|---|---|
+| `text-only` | 376 |
+| `illustrations` | 173 |
+Roughly 2:1, so accuracy is a weak metric here — report per-class scores.
+## Structure
+One `train` split, no train/test division. Each row carries the cropped advert (`image`), the `label`, OCR text for the advert region (`ocr`), the detection box on the source page as relative coordinates (`box`) and its confidence (`score`), plus Chronicling America metadata: `pub_date`, `lccn`, `name`, `publisher`, `place_of_publication`, `geographic_coverage`, `batch`, `page_seq_num`, `edition_seq_num`, `file`, and `url` / `page_url` pointing back to the source crop and full page scan.
+> [!WARNING]
+> The images are single-channel greyscale (PIL mode `L`). Call `.convert("RGB")` before feeding them to a pretrained backbone.
+Two further traps. Every row has `score` ≥ 0.90 (range 0.900–0.998), so adverts the detector found ambiguous never entered the sample — this data is easier than the page collection it came from. And `ocr` is raw Chronicling America OCR of the crop: noisy throughout, and empty for 21 of the 549 rows.
+## Uses and limits
+Binary image classification, transfer-learning walkthroughs, and a small test bed for how the illustrated/text-only split changes across 1860–1900. At 549 rows it is too small to support claims about the 19th-century press: the 301 titles here are the ones Chronicling America has digitised, and that programme selects title by title through state partners.
+## Licence and citation
+Public domain (CC0-1.0), per the [Zenodo deposit](https://doi.org/10.5281/zenodo.5838410); the underlying page scans are US public domain material held by the Library of Congress. The Zenodo record credits Daniel van Strien (British Library) as author and Catherine Bond-Harris as data collector.
+```python
+from datasets import load_dataset
+ds = load_dataset("biglam/illustrated_ads", split="train")  # 549 rows, ~48 MB
+image = ds[0]["image"].convert("RGB")
 ```
+```bibtex
 @dataset{van_strien_daniel_2021_5838410,
   author       = {van Strien, Daniel},
   title        = {{19th Century United States Newspaper Advert images
   version      = {0.0.1},
   doi          = {10.5281/zenodo.5838410},
   url          = {https://doi.org/10.5281/zenodo.5838410}}
 ```