Open Call: What can you do with an image dataset?
We’re looking to award three £1000 commissions for the Gallery Media Wall that interrogate and bring new meaning to image datasets.
Data/Set/Match is a series of events, workshops and artists’ commissions at The Photographers’ Gallery which explore the significance of scientific image datasets. Over the course of 12 months, it aims to draw attention to these datasets and explore their creation, influence and uses. At the same time, the programme will connect the image dataset to historical photographic discourses of the archive, truth and power.
The three £1000 commissions for the Media Wall might address questions such as: What do these image collections look like and how can they be comprehended? How are they generated and created? What image of the world do they promote and what do they fail to represent? How could their use be re-imagined?
These artistic commissions could fall into one of three categories:
- the creation of a new or speculative photographic dataset, which can be released to the public and shown on the Media Wall;
- the creation of a display which re-interprets an existing contemporary or historical image dataset (beyond data visualisation); or
- the adaptation of an existing work which responds to or utilises an existing image dataset.
Deadline Wednesday 31 July 2019, 5pm
Some reference and example datasets include:
- Images in the Wild Dataset: http://vis-www.cs.umass.edu/lfw/
- Caylee Pattern dataset: http://homepages.inf.ed.ac.uk/rbf/CEILIDHDATA/
- Epic Kitchens: https://epic-kitchens.github.io/2018
- Diversity in Faces: https://www.research.ibm.com/artificial-intelligence/trusted-ai/diversity-in-faces/
- Image-net: http://www.image-net.org/
- Fruits 360: https://www.kaggle.com/moltean/fruits
- This is the Problem, the Solution, the Past and the Future: http://this-is-the-problem-the-solution-the-past-and-the-future.com/
- The USC-SIPI Image Database: http://sipi.usc.edu/database/database.php
- Caltech Camera Traps: https://beerys.github.io/CaltechCameraTraps/
- Computer Vision Test Images: https://www.cs.cmu.edu/~cil/v-images.html
- Abnormality Detection in Images: https://web.archive.org/web/20180312065423/http://paul.rutgers.edu/~babaks/abnormality_detection.html
- Kaggle’s Dataset search engine: https://www.kaggle.com/datasets
- Wikipedia Article of List of Datasets for machine learning