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
Code release for SLIP Self-supervision meets Language-Image Pre-training

SLIP: Self-supervision meets Language-Image Pre-training What you can find in this repo: Pre-trained models (with ViT-Small, Base, Large) and code to

Meta Research 621 Dec 31, 2022
This is project is the implementation of the DeepShift: Towards Multiplication-Less Neural Networks paper

DeepShift This is project is the implementation of the DeepShift: Towards Multiplication-Less Neural Networks paper, that aims to replace multiplicati

Mostafa Elhoushi 88 Dec 23, 2022
PFFDTD is an open-source FDTD simulator for 3D room acoustics

PFFDTD is an open-source FDTD simulator for 3D room acoustics

Brian Hamilton 34 Nov 24, 2022
Code for the paper "Can Active Learning Preemptively Mitigate Fairness Issues?" presented at RAI 2021.

Can Active Learning Preemptively Mitigate Fairness Issues? Code for the paper "Can Active Learning Preemptively Mitigate Fairness Issues?" presented a

ElementAI 7 Aug 12, 2022
PyTorch framework for Deep Learning research and development.

Accelerated DL & RL PyTorch framework for Deep Learning research and development. It was developed with a focus on reproducibility, fast experimentati

Catalyst-Team 29 Jul 13, 2022
NHL 94 AI contests

nhl94-ai The end goals of this project is to: Train Models that play NHL 94 Support AI vs AI contests in NHL 94 Provide an improved AI opponent for NH

Mathieu Poliquin 2 Dec 06, 2021
Price-Prediction-For-a-Dream-Home - A machine learning based linear regression trained model for house price prediction.

Price-Prediction-For-a-Dream-Home ROADMAP TO THIS LINEAR REGRESSION BASED HOUSE PRICE PREDICTION PREDICTION MODEL Import all the dependencies of the p

DIKSHA DESWAL 1 Dec 29, 2021
CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)

CrossNorm (CN) and SelfNorm (SN) (Accepted at ICCV 2021) This is the official PyTorch implementation of our CNSN paper, in which we propose CrossNorm

100 Dec 28, 2022
Implementation of the paper All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

SemCo The official pytorch implementation of the paper All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

42 Nov 14, 2022
Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks

This is the code associated with the paper Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks, published at CVPR 2020.

Thomas Roddick 219 Dec 20, 2022
AISTATS 2019: Confidence-based Graph Convolutional Networks for Semi-Supervised Learning

Confidence-based Graph Convolutional Networks for Semi-Supervised Learning Source code for AISTATS 2019 paper: Confidence-based Graph Convolutional Ne

MALL Lab (IISc) 56 Dec 03, 2022
Python script that takes an Impulse response .wav and a input .wav to demonstrate audio convolution.

convolver Python script that takes an Impulse response .wav and a input .wav to demonstrate audio convolution. Created by Sean Higley

Sean Higley 1 Feb 23, 2022
pytorch implementation of fast-neural-style

fast-neural-style 🌇 🚀 NOTICE: This codebase is no longer maintained, please use the codebase from pytorch examples repository available at pytorch/e

Abhishek Kadian 405 Dec 15, 2022
Code for the paper titled "Prabhupadavani: A Code-mixed Speech Translation Data for 25 languages"

Prabhupadavani: A Code-mixed Speech Translation Data for 25 languages Code for the paper titled "Prabhupadavani: A Code-mixed Speech Translation Data

Ayush Daksh 12 Dec 01, 2022
Awesome Long-Tailed Learning

Awesome Long-Tailed Learning This repo pays specially attention to the long-tailed distribution, where labels follow a long-tailed or power-law distri

Stomach_ache 284 Jan 06, 2023
OOD Dataset Curator and Benchmark for AI-aided Drug Discovery

🔥 DrugOOD 🔥 : OOD Dataset Curator and Benchmark for AI Aided Drug Discovery This is the official implementation of the DrugOOD project, this is the

108 Dec 17, 2022
Point Cloud Registration Network

PCRNet: Point Cloud Registration Network using PointNet Encoding Source Code Author: Vinit Sarode and Xueqian Li Paper | Website | Video | Pytorch Imp

ViNiT SaRoDe 59 Nov 19, 2022
Code for KDD'20 "Generative Pre-Training of Graph Neural Networks"

GPT-GNN: Generative Pre-Training of Graph Neural Networks GPT-GNN is a pre-training framework to initialize GNNs by generative pre-training. It can be

Ziniu Hu 346 Dec 19, 2022
[ACL-IJCNLP 2021] "EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets"

EarlyBERT This is the official implementation for the paper in ACL-IJCNLP 2021 "EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets" by

VITA 13 May 11, 2022
a curated list of docker-compose files prepared for testing data engineering tools, databases and open source libraries.

data-services A repository for storing various Data Engineering docker-compose files in one place. How to use it ? Set the required settings in .env f

BigData.IR 525 Dec 03, 2022