ImageNet

Fei-Fei Li’s team creates a huge database of millions of labelled images. It becomes clear that the quantity and quality of data can matter as much as the algorithm.

Why it is in the museum

Machine learning changes the problem: instead of writing every rule, we provide data and examples. Their gaps and biases can pass into the model too.

What supports this exhibit

Primary conference paper

The creation of ImageNet as a very large hierarchical database of labelled images.

Main source: Deng, J. et al. (2009), CVPR, “ImageNet: A Large-Scale Hierarchical Image Database”.

Open the source

What to keep in mind: Primary source.

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