SHIFT15M: multiobjective large-scale fashion dataset with distributional shifts

Related tags

Deep Learning machine-learning research deep-learning fashion dataset datasets covariate-shift fill-in-the-blank dataset-shifts distributional-shift target-shift set-matching fill-in-the-n-blank
Overview

License: MIT Python GitHub code size in bytes Downloads GitHub Workflow Status PyPI version GitHub issues GitHub commit activity GitHub last commit arXiv

[arXiv]

The main motivation of the SHIFT15M project is to provide a dataset that contains natural dataset shifts collected from a web service IQON, which was actually in operation for a decade. In addition, the SHIFT15M dataset has several types of dataset shifts, allowing us to evaluate the robustness of the model to different types of shifts (e.g., covariate shift and target shift).

We provide the Datasheet for SHIFT15M. This datasheet is based on the Datasheets for Datasets [1] template.

System Python 3.6 Python 3.7 Python 3.8
Linux CPU
Linux GPU
Windows CPU / GPU Status Currently Unavailable Status Currently Unavailable Status Currently Unavailable
Mac OS CPU

SHIFT15M is a large-scale dataset based on approximately 15 million items accumulated by the fashion search service IQON.

Installation

From PyPi

$ pip install shift15m

From source

$ git clone https://github.com/st-tech/zozo-shift15m.git
$ cd zozo-shift15m
$ poetry build
$ pip install dist/shift15m-xxxx-py3-none-any.whl

Download SHIFT15M dataset

Use Dataset class

You can download SHIFT15M dataset as follows:

from shift15.datasets import NumLikesRegression

dataset = NumLikesRegression(root="./data", download=True)

Download directly by using download scripts

Please download the dataset as follows:

$ bash scripts/download_all.sh

To avoid downloading the test dataset for set matching (80GB), which is not required in training, you can use the following script.

$ bash scripts/download_all_wo_set_testdata.sh

Tasks

The following tasks are now available:

Tasks Task type Shift type # of input dim # of output dim
NumLikesRegression regression target shift (N, 25) (N, 1)
SumPricesRegression regression covariate shift, target shift (N, 1) (N, 1)
ItemPriceRegression regression target shift (N, 4096) (N, 1)
ItemCategoryClassification classification target shift (N, 4096) (N, 7)
Set2SetMatching set-to-set matching covariate shift (N, 4096)x(M, 4096) (1)

Benchmarks

As templates for numerical experiments on the SHIFT15M dataset, we have published experimental results for each task with several models.

Original Dataset Structure

The original dataset is maintained in json format, and a row consists of the following:

{
  "user":{"user_id":"xxxx", "fav_brand_ids":"xxxx,xx,..."},
  "like_num":"xx",
  "set_id":"xxx",
  "items":[
    {"price":"xxxx","item_id":"xxxxxx","category_id1":"xx","category_id2":"xxxxx"},
    ...
  ],
  "publish_date":"yyyy-mm-dd"
}

Contributing

To learn more about making a contribution to SHIFT15M, please see the following materials:

License

The dataset itself is provided under a CC BY-NC 4.0 license. On the other hand, the software in this repository is provided under the MIT license.

Dataset metadata

The following table is necessary for this dataset to be indexed by search engines such as Google Dataset Search.

property value
name SHIFT15M Dataset
alternateName SHIFT15M
alternateName shift15m-dataset
url
sameAs https://github.com/st-tech/zozo-shift15m
description SHIFT15M is a multi-objective, multi-domain dataset which includes multiple dataset shifts.
provider
property value
name ZOZO Research
sameAs https://ja.wikipedia.org/wiki/ZOZO
license
property value
name CC BY-NC 4.0
url

Citation

@misc{Kimura_SHIFT15M_Multiobjective_LargeScale_2021,
author = {Kimura, Masanari and Nakamura, Takuma and Saito, Yuki},
month = {8},
title = {SHIFT15M: Multiobjective Large-Scale Fashion Dataset with Distributional Shifts},
year = {2021}
}

Errata

No errata are currently available.

References

  • [1] Gebru, Timnit, et al. "Datasheets for datasets." arXiv preprint arXiv:1803.09010 (2018).
Comments
Releases(v0.2.0)

  • v0.2.0(Sep 20, 2022)

    • add tags info as follows:
    {
  "user":{"user_id":"xxxx", "fav_brand_ids":"xxxx,xx,..."},
  "like_num":"xx",
  "set_id":"xxx",
  "items":[
    {"price":"xxxx","item_id":"xxxxxx","category_id1":"xx","category_id2":"xxxxx"},
    ...
  ],
  "publish_date":"yyyy-mm-dd",
  "tags": "tag_a, tag_b, tag_c, ..."
}
    • add superset matching benchmark
    • fix a label creation bug on set matching with multiple splits
    Source code(tar.gz)
    Source code(zip)

  • v.0.1.2(Nov 24, 2021)

  • v.0.1.1(Sep 6, 2021)

Owner
ZOZO, Inc.
ZOZO, Inc.
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