https://github.com/st-tech/zozo-shift15m
SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts
https://github.com/st-tech/zozo-shift15m
covariate-shift cvpr cvpr2023 dataset dataset-shifts datasets deep-learning distributional-shift fashion fill-in-the-blank fill-in-the-n-blank machine-learning research set-matching target-shift
Last synced: 2 months ago
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SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts
- Host: GitHub
- URL: https://github.com/st-tech/zozo-shift15m
- Owner: st-tech
- License: other
- Created: 2021-06-17T08:17:10.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2023-10-18T02:27:14.000Z (over 2 years ago)
- Last Synced: 2025-12-17T01:30:06.602Z (6 months ago)
- Topics: covariate-shift, cvpr, cvpr2023, dataset, dataset-shifts, datasets, deep-learning, distributional-shift, fashion, fill-in-the-blank, fill-in-the-n-blank, machine-learning, research, set-matching, target-shift
- Language: Python
- Homepage:
- Size: 10.8 MB
- Stars: 175
- Watchers: 63
- Forks: 16
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE.CC
- Citation: CITATION.cff
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# SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts
- [[arXiv]](https://arxiv.org/abs/2108.12992)
- [[CVPRW2023]](https://openaccess.thecvf.com/content/CVPR2023W/CVFAD/papers/Kimura_SHIFT15M_Fashion-Specific_Dataset_for_Set-to-Set_Matching_With_Several_Distribution_Shifts_CVPRW_2023_paper.pdf)
- accepted at CVPR2023 workshop on [CVFAD](https://sites.google.com/view/cvfad2023/home?authuser=0) as an oral paper (acceptance rate = 18.5%)
Set-to-set matching is the problem of matching two different sets of items based on some criteria. Especially when each item in the set is high-dimensional, such as an image, set-to-set matching is treated as one of the applied problems to be solved by utilizing neural networks. Most machine learning-based set-to-set matching generally assumes that the training and test data follow the same distribution. However, such assumptions are often violated in real-world machine learning problems. In this paper, we propose SHIFT15M, a dataset that can be used to properly evaluate set-to-set matching models in situations where the distribution of data changes between training and testing. Some benchmark experiments show that the performance of naive methods drops due to the effects of the distribution shift. In addition, we provide software to handle the SHIFT15M dataset in a very simple way. The URL for the software will appear after this manuscript is published.
We provide the [Datasheet for SHIFT15M](./DATASHEET.md).
This datasheet is based on the [Datasheets for Datasets](https://arxiv.org/abs/1803.09010) [1] template.
| System | Python 3.6 | Python 3.7 | Python 3.8 |
| :---------------: | :------------------------------------------------------------------: | :------------------------------------------------------------------: | :------------------------------------------------------------------: |
| Linux CPU | 
| | |
| Linux GPU | | | |
| Windows CPU / GPU | | | |
| 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
```bash
$ pip install shift15m
```
### From source
```bash
$ 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:
```python
from shift15m.datasets import NumLikesRegression
dataset = NumLikesRegression(root="./data", download=True)
(x_train, y_train), (x_test, y_test) = dataset.load_dataset(target_shift=True)
```
### Download directly by using download scripts
Please download the dataset as follows:
```bash
$ bash scripts/download_all.sh
```
## Tasks
The following tasks are now available:
| Tasks | Task type | Shift type | # of input dim | # of output dim |
| ---------------------------------------------------------------------------------------------------------------------- | ------------------- | ----------------------------- | ------------------- | --------------- |
| [NumLikesRegression](https://github.com/st-tech/zozo-shift15m/tree/main/benchmarks#regression-for-the-number-of-likes) | regression | target shift | (N, 25) | (N, 1) |
| [SumPricesRegression](https://github.com/st-tech/zozo-shift15m/tree/main/benchmarks#regression-for-the-sum-of-prices) | regression | covariate shift, target shift | (N, 1) | (N, 1) |
| ItemPriceRegression | regression | target shift | (N, 4096) | (N, 1) |
| [ItemCategoryClassification](https://github.com/st-tech/zozo-shift15m/tree/main/benchmarks/item_category_prediction) | classification | target shift | (N, 4096) | (N, 7) |
| [Set2SetMatching](https://github.com/st-tech/zozo-shift15m/tree/main/benchmarks/set_matching) | 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](./benchmarks).
## 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",
"tags": "tag_a, tag_b, tag_c, ..."
}
```
## Contributing
To learn more about making a contribution to SHIFT15M, please see the following materials:
- [Developers Guide](./DEVELOPMENT.md)
- [Task Proposal Guide](./TASK_PROPOSAL.md)
- [Benchmark Proposal Guide](./BENCHMARK.md)
## License
The dataset itself is provided under a [CC BY-NC 4.0 license](./LICENSE.CC).
On the other hand, the software in this repository is provided under the [MIT license](./LICENSE.MIT).
## Dataset metadata
The following table is necessary for this dataset to be indexed by search engines such as [Google Dataset Search](https://datasetsearch.research.google.com/).
property
value
name
SHIFT15M Dataset
alternateName
SHIFT15M
alternateName
shift15m-dataset
url
https://github.com/st-tech/zozo-shift15m
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
https://github.com/st-tech/zozo-shift15m/blob/main/LICENSE.CC
## Errata
- 01/08/2022, added tags info ([#187](https://github.com/st-tech/zozo-shift15m/issues/187))
## Papers using this dataset
- Papadopoulos, Stefanos I., et al. "Multimodal Quasi-AutoRegression: Forecasting the visual popularity of new fashion products." arXiv preprint arXiv:2204.04014 (2022).
- Papadopoulos, Stefanos, et al. Fashion Trend Analysis and Prediction Model. 1, Zenodo, 2021, doi:10.5281/zenodo.5795089.
## References
- [1] Gebru, Timnit, et al. "Datasheets for datasets." arXiv preprint arXiv:1803.09010 (2018).