Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
63
datasets available to search
ShareScore release 0.9.0
Dataset results
63 results for “fashion”
Model Zoo: A Dataset of Diverse Populations of Neural Network Models - Fashion-MNIST
<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from Fashion-MNIST. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p>This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "fmnist_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models.</p> <p>For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.</p>
Clothing Dataset for Second-Hand Fashion
<h1>Second-Hand Fashion Dataset</h1> <h2>Update Sep. 19th, 2024</h2> <p>- Some problematic and duplicate images have been removed from version 2.<br>- All "gold dataset" data from `station1` and `station3` has been moved to a single `test100` folder.<br>- JSON errors have been fixed - all JSON files should be parsed correctly now.</p> <p>The new dataset has <strong>31,638</strong> items (+ about 100 items in `test100` folder) instead of the 31,997 items in version 2.</p> <h2>Overview</h2> <p>The dataset originates from projects focused on the sorting of used clothes within a sorting facility. The primary objective is to classify each garment into one of several categories to determine its ultimate destination: reuse, reuse outside Sweden (export), recycling, repair, remake, or thermal waste. </p> <p>The dataset has <strong>31,638</strong> clothing items, a massive update from the 3,000 items in version 1. The dataset collection started under the Vinnova funded project "AI for resource-efficient circular fashion" in Spring, 2022 and involves collaboration among three institutions: RISE Research Institutes of Sweden AB, Wargön Innovation AB, and Myrorna AB. The dataset has received further support through the EU project, CISUTAC (cisutac.eu).</p> <h2>Project page </h2> <p>- Webpage: <a title="Project: Second-hand Fashion" href="https://fnauman.github.io/second-hand-fashion/">second-hand-fashion</a><br>- Contact: farrukh.nauman@ri.se</p> <h2>Dataset Details</h2> <p>- The dataset contains 31,638 clothing items, each with a unique item ID in a datetime format. The items are divided into three stations: `station1`, `station2`, and `station3`. The `station1` and `station2` folders contain images and annotations from Wargön Innovation AB, while the `station3` folder contains data from Myrorna AB. Each clothing item has three images and a JSON file containing annotations. </p> <p>- Three images are provided for each clothing item:<br> 1. Front view.<br> 2. Back view.<br> 3. Brand label close-up. About 4000-5000 brand images are missing because of privacy concerns: people's hands, faces, etc. Some clothing items did not have a brand label to begin with.</p> <p>- Image resolutions are primarily in two sizes: `1280x720` and `1920x1080`. The background of the images is a table that used a measuring tape prior to January 2023, but later images have a square grid pattern with each square measuring `10x10` cm. </p> <p>- Each JSON file contains a list of annotations, some of which require nuanced interpretation (see `labels.py` for the options):<br> - `usage`: Arguably the most critical label, usage indicates the garment's intended pathway. Options include 'Reuse,' 'Repair,' 'Remake,' 'Recycle,' 'Export' (reuse outside Sweden), and 'Energy recovery' (thermal waste). About 99% of the garments fall into the 'Reuse,' 'Export,' or 'Recycle' categories.<br> - `trend`: This field refers to the general style of the garment, not a time-dependent trend as in some other datasets (e.g., Visuelle 2.0). It might be more accurately labeled as 'style.'<br> - `material`: Material annotations are mostly based on the readings from a Near Infrared (NIR) scanner and in some cases from the garment's brand label.<br> - Damage-related attributes include:<br> - `condition` (1-5 scale, 5 being the best)<br> - `pilling` (1-5 scale, 5 meaning no pilling)<br> - `stains`, `holes`, `smell` (each with options 'None,' 'Minor,' 'Major'). <br> <br> Note: 'holes' and 'smell' were introduced after November 17th, 2022, and stains previously only had 'Yes'/'No' options. For `station1` and `station2`, we introduced additional damage location labels to assist in damage detection: </p> <pre><code> "damageimage": "back", "damageloc": "bottom left", "damage": "stain ", "damage2image": "front", "damage2loc": "None", "damage2": "", "damage3image": "back", "damage3loc": "bottom right", "damage3": "stain"</code></pre> <p> Taken from `labels_2024_04_05_08_47_35.json` file. Additionally, we annotated a few hundred images with bounding box annotations that we aim to release at a later date.<br> - `comments`: The comments field is mostly empty, but sometimes contains important information about the garment, such as a detailed text description of the damage. </p> <p>- Whenever possible, ISO standards have been followed to define these attributes on a 1-5 scale (e.g., `pilling`).</p> <p>- Gold dataset: 100 garments were annotated multiple times by different annotators for **annotator agreement comparisons**. These 100 garments are placed inside a separate folder `test100`.</p> <p>- The data has been annotated by a group of expert second-hand sorters at Wargön Innovation AB and Myrorna AB. </p> <p>- Some attributes, such as `price`, should be considered with caution. Many distinct pricing models exist in the second-hand industry:<br> - Price by weight<br> - Price by brand and demand (similar to first-hand fashion)<br> - Generic pricing at a fixed value (e.g., 1 Euro or 10 SEK)<br> <br> Wargön Innovation AB does not set the prices in practice and their prices are suggestive only (`station1` and `station2`). Myrorna AB (`station3`), in contrast, does resale and sets the prices. </p> <h3>Comments</h3> <p>- We received feedback on our version 1 that some images were too blurry or had poor lighting. The image quality has slightly improved, but largely remains similar to release 1. <br>- Some users did not prefer a `tar.gz` format that we uploaded in version 1 of the dataset. We have now switched to `.zip` for convenience.<br>- Extra care was taken not to leak personal information. This is why you will not see any entries for `annotator` attribute in the JSON files in <em>station1/sep2023</em> since people used their real names. Since then, we used internally assigned IDs. <br>- Many brand images contained people's hands, faces, or other personal information. We have removed about 4000-5000 brand images for privacy reasons. <br>- Please <strong>inform us immediately</strong> if you find any personal information revelations in the dataset:<br> - Farrukh Nauman (RISE AB): `farrukh.nauman@ri.se`, <br> - Susanne Eriksson (Wargön Innovation AB): `susanne.eriksson@wargoninnovation.se`, <br> - Gabriella Engstrom (Wargön Innovation AB): `gabriella.engstrom@wargoninnovation.se`.</p> <p>We went through 100k images <strong>four</strong> times to ensure no personal information is leaked, but we are human and can make mistakes.</p> <h2>Partners</h2> <p>The data collection for this dataset has been carried out in collaboration with the following partners:</p> <p>1. <a href="https://www.ri.se/en"><strong>RISE Research Institutes of Sweden AB</strong></a>: RISE is a leading research institute dedicated to advancing innovation and sustainability across various sectors, including fashion and textiles.</p> <p>2. <a href="https://wargoninnovation.se/en/"><strong>Wargön Innovation AB</strong></a>: Wargön Innovation is an expert in sustainable and circular fashion solutions, contributing valuable insights and expertise to the dataset creation.</p> <p>3. <a href="https://www.myrorna.se/"><strong>Myrorna AB</strong></a>: Myrorna is Sweden's oldest chain of stores for collecting clothes and furnishings that can be reused. </p> <h2>License</h2> <p>CC-BY 4.0. Please refer to the LICENSE file for more details. </p> <h2>Acknowledgments</h2> <p>This dataset was made possible through the collaborative efforts of RISE Research Institutes of Sweden AB, Wargön Innovation AB, and Myrorna AB, with funding from Vinnova and support from the EU project CISUTAC. We extend our gratitude to all the expert second-hand sorters and annotators who contributed their expertise to this project.</p>
PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) of the research entitled "Development of Competences for the Fashion Designer: a Scope Review
<p>PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist: recommended items to address in a systematic review protocol and Check list CAPSI - Critical analysis of the articles related to the specific objective: map the current themes that permeate the competencies of fashion design professionals through a scoping review.</p>
Dataset: Lulu's Fashion Lounge Holdings, Inc. (LVLU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
GLAMI-1M: A Multilingual Image-Text Fashion Dataset
<p>We introduce GLAMI-1M: the largest multilingual image-text classification dataset and benchmark. The dataset contains images of fashion products with item descriptions, each in 1 of 13 languages. Categorization into 191 classes has high-quality annotations: all 100k images in the test set and 75% of the 1M training set were human-labeled. The paper presents baselines for image-text classification showing that the dataset presents a challenging fine-grained classification problem: The best scoring EmbraceNet model using both visual and textual features achieves 69.7% accuracy. Experiments with a modified Imagen model show the dataset is also suitable for image generation conditioned on text. The dataset, source code and model checkpoints are published at: https://github.com/glami/glami-1m.</p>
GLAMI-1M: A Multilingual Image-Text Fashion Dataset - 800px
<p>We introduce GLAMI-1M: the largest multilingual image-text classification dataset and benchmark. The dataset contains images of fashion products with item descriptions, each in 1 of 13 languages. Categorization into 191 classes has high-quality annotations: all 100k images in the test set and 75% of the 1M training set were human-labeled. The paper presents baselines for image-text classification showing that the dataset presents a challenging fine-grained classification problem: The best scoring EmbraceNet model using both visual and textual features achieves 69.7% accuracy. Experiments with a modified Imagen model show the dataset is also suitable for image generation conditioned on text. The dataset, source code and model checkpoints are published at: https://github.com/glami/glami-1m</p>
Figure 3 in Taxonomic practice, creativity and fashion: what's in a spider name?
Figure 3. Temporal trends in the way taxonomists assign etymology by region. Coloured lines are predicted values and shaded surfaces represent 95% confidence intervals.
Figure 2 in Taxonomic practice, creativity and fashion: what's in a spider name?
Figure 2. Most frequent spider epithets. Only epithets shared by more than 30 species across the database are shown. Colour-coding refers to the most frequent use of each name (e.g. similis is most often used in a morphological sense, but in a few instances it was also used as a reference to the similarity in the geographic distribution of one species to another).
Figure 1 in Taxonomic practice, creativity and fashion: what's in a spider name?
Figure 1. Temporal variations in the way taxonomists assign spiders etymologies. A, breakdown of the total number of etymologies by category. B, temporal variations in the number of etymologies (annual sum by etymology type) between 1757 and 2019. C, plot of smooth terms for the temporal variations in the relative proportion of different types of etymologies, according to a generalized additive model. D, temporal variations in the proportional use of etymologies. In C and D, coloured lines and shaded surfaces are, respectively, the predicted trend and 95% confidence interval for each level of the factor type, according to a generalized additive model. Exact model estimates are given in the Supporting Information, Table S1.
Figure 4 in Taxonomic practice, creativity and fashion: what's in a spider name?
Figure 4. Spatial variations in the way taxonomists assign spiders etymologies. Pie charts display the proportions of etymology types by continent. Estimates (± standard errors) are based on Bernoulli generalized linear models testing for differences in the proportion of different etymology types by continent. Significant differences (P <0.001) from the baseline (Europe) are highlighted with colours (positive = turquoise; negative = red). Exact model estimates are given in the Supporting Information, Tables S2–S7.
Street Fashion Style (SFS) dataset
<p>The Street Fashion Style (SFS) dataset is a new street photos dataset collected from Chictopia, where a total of 293,105 user posts are crawled. In each post, a user usually publishes the photograph of her/his worn outfit along with associated tags. Generally, these tags include current season, the suitable occasion, fashion style, the detailed garment information (e.g. category, color and brand), the geographical and year information.</p> <p>The dataset can be applied, but not limited to the following research areas:</p> <ul> <li>multi-task learning</li> <li>feature embedding learning</li> <li>fashion-related classification</li> <li>fashion trends analysis</li> </ul> <p>Please cite the following paper if you use the SFS dataset in your work (papers, articles, reports, books, software, etc):</p> <ul> <li>X. Gu, Y. Wong, P. Peng, L. Shou, G. Chen, M. Kankanhalli<br> <strong>Understanding Fashion Trends from Street Photos via Neighbor-Constrained Embedding Learning</strong><br> <em>ACM Multimedia</em>, 2017.<br> http://doi.org/10.1145/3123266.3123441</li> </ul> <p>After downloading all parts (images-*), extract using: cat images-* | tar zx</p> <p>Alternate download mirror - https://pan.baidu.com/s/1nvA1IPz</p>
Ethical and Sustainable Fashion
<p>During the Sabai Webinar Series 18, hosted by the Shwetaungthagathu Reform Initiative Centre (SRIc), experts, including Tin Shine Aung (SRIc Director), Shar Thae Hoy (Founder of CALM), and Theint Theint Myat (Founder of Handmake By Theint), explored the essence of ethical and sustainable fashion. They highlighted fair wages, safe workspaces, and eco-friendly practices as its core values. Discussions included innovative solutions like recycled fabrics and biodegradable materials, emphasizing consumer power to drive industry change by choosing sustainable and durable fashion.</p>
Datasets related to the paper " Inception Models for Fashion Image Captioning: An Extensive Study on Multiple Datasets"
<p>This collection contains three datasets in HDF5 format: FashionCap, ReducedInFashAI, ReducedFACAD.</p>
Dataset Questionnaire Social Media Marketing Activities, Brand Loyalty, Brand Trust, Brand Equity, and Industry Fashion In Indonesia
<p>The following dataset is a dataset from a study that investigated Social Media Marketing Activities, Brand Loyalty, Brand Trust, and, Brand Equity in the context of fashion industry in Indonesia.</p>
Cas9-induced large deletions and small indels are controlled in a convergent fashion
<p>Demultiplexed reads assembled from 150PE Illumina using Pear with following settings: pear-0.9.10-bin-64 -f read1.fq -r read2.fq -n 20 -p 0.01. Unassembled PE reads (estimated at 0.1% of all reads) and reads that failed demux not included. 960 files total, corresponding to 96 wells of the library (95 control or knock-out mES clones and one well intentionally left empty) times two biological replicates times five test gRNAs (non-targetting control g33, chromosome X targetting g15, g48 and gU48 and autosome targetting g148). mES cells are derived from CAST x BL6 cross. Non-targeting control g33 fastq files are not locus-demultiplexed ie would map to g15, g48 and g148 loci. </p>
Speed Art Museum: Fashioning Madame Adélaïde
This is a digitized section of the "Isabelle de Borchgrave: Fashioning Art from Paper" exhibit at Louisville, Kentucky's Speed Art Museum. "[The Exhibit] features the life-size, trompe l'œil paper costumes of Belgian artist Isabelle de Borchgrave (born 1946). Following a visit to the Costume Institute at the Metropolitan Museum in 1994, de Borchgrave began working in the new medium, creating trompe l'œilpaper works in what eventually would become four major paper fashion collections." - Speed Art Museum website This model was created using photogrammetry. The photos were taken June 20, 2021. This model is incomplete and represents only a proof of concept, since access to the exhibit was limited. A more comprehensive scan of the exhibit would result in a much, much higher quality 3D model. Institutions looking to digitize their collections should consider photogrammetry services, a safe, non-invasive, and cost-effective way to digitize historic artifacts. Learn more at www.johnclere.com. Source: Objaverse 1.0 / Sketchfab
old fashion house
great model take it Source: Objaverse 1.0 / Sketchfab
Ethical and Sustainable Fashion
<p>During the Sabai Webinar Series 18, hosted by the Shwetaungthagathu Reform Initiative Centre (SRIc), experts, including Tin Shine Aung (SRIc Director), Shar Thae Hoy (Founder of CALM), and Theint Theint Myat (Founder of Handmake By Theint), explored the essence of ethical and sustainable fashion. They highlighted fair wages, safe workspaces, and eco-friendly practices as its core values. Discussions included innovative solutions like recycled fabrics and biodegradable materials, emphasizing consumer power to drive industry change by choosing sustainable and durable fashion.</p>
Dataset fashion
<p>The data contains consumer preferences and habits related to the purchase of second-hand clothes and clothes made from bio-based materials.</p>
Fluorination increases the electron affinity of phenoxy in a predictable fashion
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.