README.md

metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': 1930s
            '1': 1940s
            '2': 1950s
            '3': 1960s
            '4': 1970s
  splits:
    - name: train
      num_bytes: 221261063
      num_examples: 1325
  download_size: 222265856
  dataset_size: 221261063
pretty_name: Dating Historical Color Images
task_categories:
  - image-classification
tags:
  - 'history '
  - lam
  - photography
size_categories:
  - 1K<n<10K

Dataset Card for Dating historical color images

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: http://graphics.cs.cmu.edu/projects/historicalColor/
• Repository:
• Paper: https://doi.org/10.1007/978-3-642-33783-3_36
• Leaderboard:
• Point of Contact:

Dataset Summary

We introduce the task of automatically estimating the age of historical color photographs. We suggest features which attempt to capture temporally discriminative information based on the evolution of color imaging processes over time and evaluate the performance of both these novel features and existing features commonly utilized in other problem domains on a novel historical image data set. For the challenging classification task of sorting historical color images into the decade during which they were photographed, we demonstrate significantly greater accuracy than that shown by untrained humans on the same data set. Additionally, we apply the concept of data-driven camera response function estimation to historical color imagery, demonstrating its relevance to both the age estimation task and the popular application of imitating the appearance of vintage color photography.

Supported Tasks and Leaderboards

This dataset is intended to train image classification or regression models to predict the time period in which color photographs were taken. This task could be approached either as a classification task or could be approached as an image regression task.

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

There is a single training split since the original dataset doesn't define a train-test split.

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

Beginning with a collection of approximately 230,000 Flickr images taken prior to 1980, we perform automated removal of monochromatic images. The remaining images are manually inspected to remove non-photographic content (e.g. scans of vintage artwork) and any remaining monochromatic images. Finally, a random subsampling and decimation is performed to create a dataset containing an equal number of historical color images for each decade (1,375 images total).

Annotations

Annotation process

Annotations are based on metadata available on Flickr.

Who are the annotators?

It appears that annotations are sourced via Flickr, so the annotators are presumed to be those uploading the images to Flickr. This will include individuals as well as cultural heritage institutions.

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

[More Information Needed]

Contributions

Thanks to @davanstrien for adding this dataset.