The Clothes Segmentation Dataset is crafted for the e-commerce, fashion, and visual entertainment sectors, incorporating a wide array of internet-collected images with resolutions ranging from 183 x 275 to 3024 x 4032 pixels. This dataset specializes in contour and semantic segmentation, featuring around 30 target categories including clothing items, accessories, and body parts, facilitating detailed analysis and application in fashion technology.

In-shop Clothes Retrieval Benchmark evaluates the performance of in-shop Clothes Retrieval. This is a large subset of DeepFashion, containing large pose and scale variations. It also has large diversities, large quantities, and rich annotations, including:

7,982 number of clothing items; 52,712 number of in-shop clothes images, and ~200,000 cross-pose/scale pairs;

Each image is annotated by bounding box, clothing type and pose type.

The Clothing Segmentation Dataset is designed to propel the capabilities of AI in the fashion industry by providing a comprehensive collection of images for semantic segmentation tasks. This dataset encompasses internet-collected images from various scenarios such as e-commerce platforms, fashion shows, social media, and offline user-generated content. It focuses on enabling precise segmentation of clothing items, including main human parts, clothing pieces, and accessories, to support the development of advanced AI models for automated image analysis and product categorization.

The Pinterest Fashion Compatibility dataset comprises images showcasing fashion products, each annotated with bounding boxes and associated with links directing to the corresponding products. This dataset facilitates the exploration of scene-based complementary product recommendation, aiming to complete the look presented in each scene by recommending compatible fashion items.

Basic Statistics: - Scenes: 47,739 - Products: 38,111 - Scene-Product Pairs: 93,274

Metadata: - Product IDs: Identifiers for the products featured in the images. - Bounding Boxes: Coordinates specifying the location of each product within the image.

Example (fashion.json): The dataset contains JSON entries where each entry associates a product with a scene, along with the bounding box coordinates for the product within the scene. json { "product": "0027e30879ce3d87f82f699f148bff7e", "scene": "cdab9160072dd1800038227960ff6467", "bbox": [ 0.434097, 0.859363, 0.560254, 1.0 ] }

Citation: If you utilize this dataset, please cite the following paper: Title: Complete the Look: Scene-based complementary product recommendation Authors: Wang-Cheng Kang, Eric Kim, Jure Leskovec, Charles Rosenberg, Julian McAuley Published in: CVPR, 2019 Link to paper

Code and Additional Resources: For additional resources, sample code, and instructions on how to collect the product images from Pinterest, you can visit the GitHub repository.

This dataset provides a rich ground for research and development in the domain of fashion-based image recognition, product recommendation, and the exploration of fashion styles and trends through machine learning and computer vision techniques.

Dataset Card for H&M Clothes captions

_Dataset used to train/finetune [Clothes text to image model] Captions are generated by using the 'detail_desc' and 'colour_group_name' or 'perceived_colour_master_name' from kaggle/competitions/h-and-m-personalized-fashion-recommendations. Original images were also obtained from the url (https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/data?select=images)

  For each row the dataset contains image and text… See the full description on the dataset page: https://huggingface.co/datasets/wbensvage/clothes_desc.

The Clothing Keypoints Dataset aims to enhance the precision of fashion-related AI applications by providing a large-scale collection of images for keypoint detection tasks. This dataset includes internet-collected images that span a wide array of scenarios, including e-commerce platforms, fashion shows, social media, and offline user-generated content. It is meticulously annotated to identify keypoints on clothing items, facilitating the development of algorithms for pose estimation, size fitting, style matching, and interactive shopping experiences. The dataset includes classified labels, bounding boxes, and keypoints for 80 different clothing types, making it a comprehensive resource for improving the accuracy and reliability of fashion AI systems.

Clothing Segmentation DataSet

## Overview

Clothing Segmentation DataSet is a dataset for instance segmentation tasks - it contains Ropa annotations for 1,084 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dress Code is a new dataset for image-based virtual try-on composed of image pairs coming from different catalogs of YOOX NET-A-PORTER. The dataset contains more than 50k high resolution model clothing images pairs divided into three different categories (i.e. dresses, upper-body clothes, lower-body clothes).

We introduce the Clothing Attribute Dataset for promoting research in learning visual attributes for objects. The dataset contains 1856 images, with 26 ground truth clothing attributes such as "long-sleeves", "has collar", and "striped pattern". The labels were collected using Amazon Mechanical Turk.

The E-commerce Product Dataset is a comprehensive collection tailored for the e-commerce sector, featuring a wide range of products from 16 main categories including shoes, hats, bags, furniture, digital products, jewelry, and more. With over 200k SKUs, this dataset is equipped with bounding boxes and category tags, making it a pivotal resource for product classification and inventory management.

Clothes

## Overview

Clothes is a dataset for object detection tasks - it contains Clothes annotations for 200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Custom Made Clothes Market size was valued at USD 51.89 Billion in 2023 and is projected to reach USD 131.6 Billion by 2031, growing at a CAGR of 10.9% during the forecast period 2024-2031.

Global Custom Made Clothes Market Drivers

Technological developments include 3D scanning: AI design tools, and online platforms for customization that increase the accessibility of custom apparel.

Growing Disposable Income: Consumers' desire to spend more on high-end, custom clothing is being driven by rising disposable income, especially in developed economies.

Ethical and sustainable design: is becoming more and more popular, with custom-made clothing providing more environmentally friendly options and generating less waste than mass-produced goods.

Influence of Social Media and Celebrities: Influencers and social media platforms encourage customers to choose custom designs by promoting personalized fashion as a status symbol.

MultiModel Clothes

## Overview

MultiModel Clothes is a dataset for object detection tasks - it contains Clothes Pt2b annotations for 4,336 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Fashion-MNIST is a dataset of Zalando's article images consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('fashion_mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/fashion_mnist-3.0.1.png" alt="Visualization" width="500px">

Clothes Labeling

## Overview

Clothes Labeling is a dataset for object detection tasks - it contains Shirt annotations for 5,995 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Fashion-Gen consists of 293,008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by professional stylists. Each item is photographed from a variety of angles.

staturus/clothes dataset hosted on Hugging Face and contributed by the HF Datasets community

Unlock valuable insights with our comprehensive Fashion Dataset from Flipkart. This dataset is meticulously curated, offering detailed information on a wide range of fashion products available on Flipkart.

Whether you're conducting data analysis, enhancing your machine learning models, or performing market research, this dataset is an invaluable resource. It includes product names, descriptions, prices, images, and customer reviews.

Optimize your projects with high-quality, structured data and stay ahead in the competitive fashion industry. Explore the vast collection and leverage the power of data for your research and development needs.

Crawl Feeds ready to download fashion datasets

Laboratory Clothes Market Outlook

The global laboratory clothes market size is projected to witness significant growth from USD 1.8 billion in 2023 to USD 3.1 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 6.5% during the forecast period. This growth can be attributed to the increasing awareness regarding safety and hygiene standards in laboratory settings, advancements in fabric technology, and the rising number of research activities across various industries.

One of the primary growth factors driving the laboratory clothes market is the heightened emphasis on safety and hygiene in laboratory and clinical environments. As infections and contamination risks pose significant threats to personnel, the adoption of protective clothing, including lab coats, scrubs, aprons, and coveralls, has become imperative. Regulations and guidelines from health and safety authorities further compel institutions to ensure their staff are adequately protected, thereby boosting the market demand for laboratory clothes.

Technological advancements in fabric materials and production processes are also playing a pivotal role in the market's expansion. Innovations such as antimicrobial fabrics, fluid-resistant materials, and enhanced durability features have made laboratory clothes more effective and long-lasting. These developments cater to the increasing demand for high-performance protective clothing that can withstand rigorous laboratory conditions, thereby driving market growth.

The growth of research and development activities across various sectors, including pharmaceuticals, biotechnology, and academic institutions, is another significant factor propelling the market. The surge in pharmaceutical research, particularly in light of recent global health challenges, has led to an increased need for protective clothing to ensure the safety of researchers and maintain sterile environments. Similarly, the expansion of academic and industrial research laboratories worldwide contributes to the rising demand for laboratory clothes.

Regionally, North America holds a substantial share of the laboratory clothes market, driven by established healthcare infrastructure, stringent safety regulations, and significant investment in research and development activities. The Asia Pacific region is anticipated to witness the highest growth rate due to the burgeoning biotechnology and pharmaceutical industries, increasing healthcare expenditure, and the rapid growth of academic research institutions.

Product Type Analysis

The laboratory clothes market can be segmented into various product types, including lab coats, scrubs, aprons, coveralls, and other specialized protective clothing. Among these, lab coats are the most commonly used and widely recognized form of laboratory apparel. They provide basic protection against spills and minor chemical exposures, making them a staple in many laboratory settings. The demand for lab coats is further bolstered by their mandatory use in numerous educational and research institutions, as well as healthcare facilities.

Scrubs are another critical segment within the laboratory clothes market, particularly prevalent in medical and clinical environments. Known for their comfort and ease of movement, scrubs are essential for healthcare professionals who require protective yet functional attire. The rising number of healthcare workers and the expansion of healthcare services globally contribute significantly to the demand for scrubs. Moreover, the introduction of antimicrobial and fluid-resistant scrubs has enhanced their protective qualities, making them more appealing to end-users.

Aprons and coveralls are typically used in more hazardous laboratory environments where there is a higher risk of exposure to harmful chemicals, biological agents, or other dangerous substances. These garments provide a higher level of protection compared to lab coats and scrubs, covering more of the body and often incorporating advanced protective features. The necessity for such high-level protection in certain laboratory settings ensures a steady demand for aprons and coveralls within the market.

In addition to the standard categories of laboratory clothes, there are other specialized protective garments designed to meet specific safety requirements. These may include flame-resistant clothing, chemical-resistant suits, and disposable lab wear. The demand for these specialized products is driven by niche applications in industries such as petrochemicals, hazardous materials handling

Custom Made Clothes Market Outlook

The global custom-made clothes market size was valued at approximately USD 35.6 billion in 2023 and is projected to reach USD 56.2 billion by 2032, growing at a CAGR of 5.2% during the forecast period. The growth factor driving this market is the increasing demand for personalized fashion, fueled by the rising disposable income and changing consumer preferences towards unique and high-quality clothing. As consumers become more discerning about their fashion choices, the demand for custom-made clothing continues to rise, creating a vibrant market landscape.

Several factors are contributing to the growth of the custom-made clothes market. Firstly, advancements in technology such as 3D printing, AI-based fashion algorithms, and virtual fitting rooms have revolutionized the way custom clothing is designed, produced, and marketed. These technologies allow for precise measurements, which in turn enhances the fit and comfort of the clothing, making custom-made clothes more appealing to consumers. Secondly, the increasing awareness of sustainable fashion is driving consumers towards custom-made clothes, which often result in less waste compared to mass-produced clothing. Custom-made clothing typically features higher quality materials and craftsmanship, further enhancing its appeal.

Consumer demand for uniqueness in fashion is another significant growth driver. Unlike mass-produced garments, custom-made clothes offer a level of personalization that allows individuals to express their personal style and preferences. This uniqueness is especially appealing to millennials and Gen Z consumers who value individuality and are willing to pay a premium for products that reflect their personal identity. Additionally, the rise of social media has amplified the desire for unique fashion statements, with influencers and celebrities often showcasing custom-made outfits, thereby increasing consumer interest and demand.

E-commerce platforms have also played a crucial role in the growth of the custom-made clothes market. Online stores have made it easier for consumers to access custom-made clothing options, even from the comfort of their homes. The convenience of online shopping, combined with the ability to customize and preview garments before purchase, has significantly boosted the market. Moreover, many online platforms offer user-friendly interfaces and advanced customization tools, making the process of designing custom clothes straightforward and appealing to a broad audience.

The concept of Ready-to-Wear has also influenced the custom-made clothes market. While custom-made garments offer unparalleled personalization and fit, the convenience and immediacy of Ready-to-Wear fashion cannot be overlooked. Many consumers appreciate the ability to purchase high-quality garments off the rack, which are designed to fit a wide range of body types. This has led to a hybrid approach where brands offer semi-customized options, allowing consumers to select from a range of sizes and styles while still enjoying some degree of personalization. This blend of Ready-to-Wear and custom-made options caters to diverse consumer needs, providing flexibility and choice in the fashion market.

Regionally, Asia Pacific holds a significant share of the custom-made clothes market, driven by a large and growing middle-class population with increasing disposable incomes. The region's strong tradition of tailoring and bespoke clothing also contributes to its market dominance. North America and Europe are also prominent markets, characterized by high consumer spending on fashion and a strong inclination towards personalized and high-quality clothing. The Middle East & Africa and Latin America, while currently smaller markets, are expected to see considerable growth due to increasing urbanization and rising disposable incomes.

Product Type Analysis

The custom-made clothes market is segmented by product type into formal wear, casual wear, sportswear, ethnic wear, and others. Formal wear is a significant segment, driven by a high demand for tailored suits, dresses, and business attire. Consumers in this segment are often professionals seeking high-quality garments that provide a perfect fit and a polished appearance. The formal wear segment benefits from the increasing number of corporate events, weddings, and other formal occ