SHIFT15M was introduced as a benchmark for evaluating set-to-set matching models when the training and test distributions differ. Many machine learning methods assume that training and test data are independently and identically distributed, but this assumption is often violated in real-world applications. In fashion, trends change over time, causing shifts in item appearance, prices, user preferences, and outfit composition.
The dataset supports… See the full description on the dataset page:
https://huggingface.co/datasets/zozonext/shift15m.
