SHIFT15M: multiobjective large-scale fashion dataset with distributional shifts

The following question should be answered:

If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? If so, please provide a description, as well as a link or other access point to the mechanism (if appropriate).

@nocotan I'm planning to prepare image features as we discussed. To be extracted:

• CNN features (2048 dimensional features from the pre-trained Inception-V3 model on ILSVRC2012)

By the way, I was trying to find a properly hand-crafted image feature extractor that involves colors but cannot find available codes. For instance, combining Local Binary Pattern (LBP) and Local Color Contrast (LCC) showed superior performance in a texture classification task described in the following paper compared with other color-based hand-crafted features, but LCC is not in OSS. https://www.researchgate.net/publication/315858786_Hand-Crafted_vs_Learned_Descriptors_for_Color_Texture_Classification

So, here I'm planning not to include a hand-crafted one for the image-based task.

The following question should be answered:

Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? If so, please provide a description. If not, you may skip the remainder of the questions in this section.

Before publishing, we need to determine the license of the repository, e.g.,

• MIT
• Apache
• BSD
• GPL

After researching which license is appropriate, please add the LICENSE to the repository.

Thank you for your great work and dataset opening at first.

Description When I tried to run the item_category_prediction task following the usageitem_category_prediction I got an exception like this:

Environment:

• Python 3.8.8

It will be so helpful if you can give any gracious advice, thank you.

the number of outfits: 2,555,147 the number of images (multiple-counting): 15,218,721 the number of unique images: 2,335,598

Note: maybe shift28M is not the correct name.

The following question should be answered:

How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)?

The following question should be answered:

Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? If so, please provide a description.

The following question should be answered:

Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? For example, is there anything that a future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide a description. Is there anything a future user could do to mitigate these undesirable harms?

The following question should be answered:

Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? If so, please provide a link or other access point to the “raw” data.

The following question should be answered:

Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis)been conducted? If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation.

Bumps setuptools from 65.4.1 to 65.5.1.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Describe the bug In set matching, the numbers of data used are restricted as 30816, 3851, and 3851 for train, val, and test data, respectively; however, when the same years are selected for train_year and test_year, it will be inconsistent.

This bug may cause inappropriate experiments in changing train_year and test_year.

Parent Task

set matching

Model List

Note

It might be required to conduct set matching experiments under the disjoint setting. Here, we perform testing using the items that are not included while training; we call it disjointed.

References

https://arxiv.org/abs/1804.09979

Is your feature request related to a problem? Please describe. We added the tags information for our dataset. Then, it is good to implement the additional data loader with tags information.

Describe the solution you'd like This can be accomplished by adding arguments to an existing data loader.

Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.

Additional context Add any other context or screenshots about the feature request here.