19th Century United States Newspaper images predicted as Photographs with labels for "human", "animal", "human-structure" and "landscape"

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] 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 'photographs'. This dataset contains a sample of these images with additional labels indicating if the photograph has one or more of the following labels: "human", "animal", "human-structure" and "landscape" The data is organised as follows: The images themselves can be found in `images.zip` `newspaper-navigator-sample-metadata.csv` contains metadata about each image drawn from the Newspaper Navigator Dataset. `multi_label.csv` contains the labels for the images as a CSV file `annotations.csv` conains the labels for the images with additional metadata This dataset was created for use in an under-review Programming Historian tutorial (http://programminghistorian.github.io/ph-submissions/lessons/computer-vision-deep-learning-pt2) The primary aim of the data was to provide a realistic example dataset for teaching computer vision for working with digitised heritage material. The data is shared here since it may be useful for others. This data documentation is a work in progress and will be updated when the Programming Historian tutorial is released publicly. The metadata CSV file contains the following columns: - filepath - pub_date - page_seq_num - edition_seq_num - batch - lccn - box - score - ocr - place_of_publication - geographic_coverage - name - publisher - url - page_url - month - year - iiif_ur