This content originally appeared on DEV Community and was authored by Masanari KIMURA
Hi, everyone.
We present a novel dataset aimed at properly evaluating machine learning models under distributional shifts.
Our SHIFT15M dataset has several good properties:
- Multiobjective. Each instance in the dataset has several numerical values that can be used as target variables.
- Large-scale. The SHIFT15M dataset consists of 15million fashion images.
- Coverage of types of dataset shifts. SHIFT15M contains multiple dataset shift problem settings (e.g., covariate shift or target shift). SHIFT15M also enables the performance evaluation of the model under various magnitudes of dataset shifts by switching the magnitude.
In addition, we provide software to handle SHIFT15M in a very simple way.
If you are interested feel free to check out:
Arxiv: https://arxiv.org/abs/2108.12992
GitHub: https://github.com/st-tech/zozo-shift15m
This content originally appeared on DEV Community and was authored by Masanari KIMURA
Masanari KIMURA | Sciencx (2021-09-10T01:34:05+00:00) SHIFT15M: Multiobjective Large-scale Fashion Dataset with Distributional Shifts. Retrieved from https://www.scien.cx/2021/09/10/shift15m-multiobjective-large-scale-fashion-dataset-with-distributional-shifts/
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