---
title: illustrated_ads
canonical_url: "https://www.modelscope.cn/datasets/biglam/illustrated_ads"
md_url: "https://www.modelscope.cn/datasets/biglam/illustrated_ads.md"
repository: biglam/illustrated_ads
last_updated: 2026-10-10
license: "[cc0-1.0]"
storage_size: "46 MB"
downloads: 35
stars: 0
---

# illustrated_ads

> illustrated_ads - biglam 在 ModelScope 开源的数据集。19th Century US Newspaper Adverts — illustrated or text-only

biglam/illustrated_ads 是 ModelScope 魔搭社区上的数据集，存储大小 46 MB，采用 [cc0-1.0] 许可。

- **Repository**: biglam/illustrated_ads
- **License**: [cc0-1.0]
- **Storage size**: 46 MB
- **Downloads**: 35
- **Stars**: 0
- **Last updated**: 2026-10-10

Source: https://www.modelscope.cn/datasets/biglam/illustrated_ads

---

# 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 
                   with 'illustrated' or 'non illustrated' labels}},
  month        = oct,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {0.0.1},
  doi          = {10.5281/zenodo.5838410},
  url          = {https://doi.org/10.5281/zenodo.5838410}}
```