SHIFT15M: multiobjective large-scale fashion dataset with distributional shifts

Related tags

Deep Learningmachine-learningresearchdeep-learningfashiondatasetdatasetscovariate-shiftfill-in-the-blankdataset-shiftsdistributional-shifttarget-shiftset-matchingfill-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.
GitHub Repository
A comprehensive and up-to-date developer education platform for Urbit.

curriculum A comprehensive and up-to-date developer education platform for Urbit. This project organizes developer capabilities into a hierarchy of co

Sigilante 36 Oct 04, 2022
This is the official code for the paper "Ad2Attack: Adaptive Adversarial Attack for Real-Time UAV Tracking".

Ad^2Attack：Adaptive Adversarial Attack on Real-Time UAV Tracking Demo video 📹 Our video on bilibili demonstrates the test results of Ad^2Attack on se

Intelligent Vision for Robotics in Complex Environment 10 Nov 07, 2022
LAnguage Model Analysis

LAMA: LAnguage Model Analysis LAMA is a probe for analyzing the factual and commonsense knowledge contained in pretrained language models. The dataset

Meta Research 960 Jan 08, 2023
Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'

Filtration Curves for Graph Representation This repository provides the code from the KDD'21 paper Filtration Curves for Graph Representation. Depende

Machine Learning and Computational Biology Lab 16 Oct 16, 2022
MlTr: Multi-label Classification with Transformer

MlTr: Multi-label Classification with Transformer This is official implement of "MlTr: Multi-label Classification with Transformer". Abstract The task

程星 38 Nov 08, 2022
A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization

University1652-Baseline [Paper] [Slide] [Explore Drone-view Data] [Explore Satellite-view Data] [Explore Street-view Data] [Video Sample] [中文介绍] This

Zhedong Zheng 335 Jan 06, 2023
My coursework for Machine Learning (2021 Spring) at National Taiwan University (NTU)

Machine Learning 2021 Machine Learning (NTU EE 5184, Spring 2021) Instructor: Hung-yi Lee Course Website : (https://speech.ee.ntu.edu.tw/~hylee/ml/202

100 Dec 26, 2022
[ICME 2021 Oral] CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning

CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning This repository is the official PyTorch implementation of CORE-Text, a

Jingyang Lin 18 Aug 11, 2022
YouRefIt: Embodied Reference Understanding with Language and Gesture

YouRefIt: Embodied Reference Understanding with Language and Gesture YouRefIt: Embodied Reference Understanding with Language and Gesture by Yixin Che

16 Jul 11, 2022
Java and SHACL code commented in the paper "Towards compliance checking in reified I/O logic via SHACL" submitted to ICAIL 2021

shRIOL The subfolder shRIOL contains Java files to execute the SHACL files on the OWL ontology. To compile the Java files: "javac -cp ./src/;./lib/* -

1 Dec 06, 2022
Clustering with variational Bayes and population Monte Carlo

pypmc pypmc is a python package focusing on adaptive importance sampling. It can be used for integration and sampling from a user-defined target densi

45 Feb 06, 2022
[AAAI22] Reliable Propagation-Correction Modulation for Video Object Segmentation

Reliable Propagation-Correction Modulation for Video Object Segmentation (AAAI22) Preview version paper of this work is available at: https://arxiv.or

Xiaohao Xu 70 Dec 04, 2022
Source code for our paper "Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash"

Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash Abstract: Apple recently revealed its deep perceptual hashing system NeuralHash to

<a href=[email protected]"> 11 Dec 03, 2022
Another pytorch implementation of FCN (Fully Convolutional Networks)

FCN-pytorch-easiest Trying to be the easiest FCN pytorch implementation and just in a get and use fashion Here I use a handbag semantic segmentation f

Y. Dong 158 Dec 21, 2022
An official PyTorch Implementation of Boundary-aware Self-supervised Learning for Video Scene Segmentation (BaSSL)

An official PyTorch Implementation of Boundary-aware Self-supervised Learning for Video Scene Segmentation (BaSSL)

Kakao Brain 72 Dec 28, 2022
TensorFlow 101: Introduction to Deep Learning for Python Within TensorFlow

TensorFlow 101: Introduction to Deep Learning I have worked all my life in Machine Learning, and I've never seen one algorithm knock over its benchmar

Sefik Ilkin Serengil 896 Jan 04, 2023
A simple, fast, and efficient object detector without FPN

You Only Look One-level Feature (YOLOF), CVPR2021 A simple, fast, and efficient object detector without FPN. This repo provides an implementation for

789 Jan 09, 2023
An Unpaired Sketch-to-Photo Translation Model

Unpaired-Sketch-to-Photo-Translation We have released our code at https://github.com/rt219/Unsupervised-Sketch-to-Photo-Synthesis This project is the

38 Oct 28, 2022
Code for Towards Streaming Perception (ECCV 2020) :car:

sAP — Code for Towards Streaming Perception ECCV Best Paper Honorable Mention Award Feb 2021: Announcing the Streaming Perception Challenge (CVPR 2021

Martin Li 85 Dec 22, 2022
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning

Mammoth - An Extendible (General) Continual Learning Framework for Pytorch NEWS STAY TUNED: We are working on an update of this repository to include

AImageLab 277 Dec 28, 2022