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">

nreimers/fashion-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Context

This dataset contains product listings from SSENSE. SSENSE is a multi-brand retailer specializing in the sales of designer fashion and high-end streetwear. The data, extracted from their websites via Python and Beautiful Soup, provides a snapshot of current trends, prices, and offerings in the luxury fashion e-commerce sector.

Content

Each entry in the dataset contains the following information:
Brand: The fashion brand or designer of the product.
Description: A brief description of the product, highlighting key features.
Price_USD: The retail price of the product in US dollars.
Type: Indicates the target gender for the product, classified as 'men' or 'women'.

Inspiration

Trend Analysis in Luxury Fashion: Investigate current trends in luxury fashion, including popular brands, product types, and pricing.
Gender-Based Market Insights: Explore differences in product offerings and pricing strategies between men's and women's fashion.
Brand and Price Segmentation: Analyze how different brands are positioned within SSENSE's portfolio in terms of pricing and target audience.

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.

The Clothing Pattern Classification Dataset is specifically designed to address the needs of the fashion industry, focusing on the classification of various clothing patterns. This dataset gathers internet-collected images that showcase clothing from different scenarios such as e-commerce platforms, fashion shows, social media, and offline user-generated content. It aims to facilitate the development of AI models that can accurately recognize and classify over 30 common clothing patterns, enhancing online shopping experiences and supporting trend analysis.

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.

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.

ZARA UK Fashion Dataset offers an extensive collection of fashion product data from ZARA's UK online store, providing a detailed overview of available items. This dataset is valuable for analyzing the European fashion retail market, particularly in the UK, and includes fields such as product titles, URLs, SKUs, MPNs, brands, prices, currency, images, breadcrumbs, country, availability, unique IDs, and timestamps for when the data was scraped.

Key Features:

• Product Details: Includes title, URL, SKU (Stock Keeping Unit), MPN (Manufacturer Part Number), and brand for each product, helping to uniquely identify and differentiate items.
• Pricing Information: Features the price of each product along with the currency used (GBP) to understand the pricing strategies of ZARA in the UK market.
• Visual Data: High-quality images of each product, essential for visual merchandising analysis and online consumer behavior studies.
• Categorical Information: Breadcrumbs data provide context on the product's placement within ZARA's website structure, helping to analyze navigation and product hierarchy.
• Geographical Focus: Specific to the UK market, making it relevant for studies on British fashion retail and consumer trends.
• Availability Status: Includes real-time availability data, which is crucial for understanding stock levels, popular products, and restocking practices.
• Unique Identifiers: Each product is tagged with a uniq_id, ensuring data integrity and making it easier to track and analyze over time.
• Data Collection Timestamp: The scraped_at field records the exact time and date when the data was collected, aiding in time-based analysis of inventory and pricing.

Potential Use Cases:

• Market Research: Analyze UK and European fashion trends, consumer preferences, and competitive positioning within the fast fashion sector.
• E-commerce Analysis: Study ZARA's product placement, pricing, and availability to optimize online retail strategies.
• Stock Management: Use SKU and availability data to predict inventory needs and enhance supply chain efficiency.
• Brand Analysis: Examine the impact of brand identity on consumer choices and product performance in the UK market.
• Academic Research: Ideal for research projects focused on fashion retail, marketing strategies, and consumer behavior in Europe.

Data Sources:

The data is meticulously collected from ZARA's official UK website and other reliable retail databases, reflecting the latest product offerings and market dynamics specific to the UK and European fashion markets.

• ZARA US Retail Products Dataset: Explore over 10,000 product records from ZARA's USA online store, including titles, prices, images, and availability.
  
  
• Fashion Products Dataset from GAP.com: Access detailed product information from GAP's online store, featuring over 4,500 fashion items with attributes like price, brand, color, reviews, and images.
  
  
• Myntra Fashion Products Dataset: A comprehensive dataset from Myntra.com, offering over 12,000 fashion products with detailed attributes for in-depth analysis.

Fashion Products Dataset from GAP.com offers a curated collection of over 4,500 fashion items, meticulously extracted by Crawl Feeds' in-house web scraping team for research and analysis purposes. This dataset, last updated on October 11, 2021, encompasses a diverse range of products, including clothing, accessories, and more, providing a comprehensive view of GAP's offerings.

Key Features:

• Comprehensive Data Points: Each entry in the dataset includes 16 essential attributes such as product URL, name, product ID (PID), brand, price, currency, condition, availability, color, SKU, product details, average rating, review count, images, breadcrumbs, and the date of data extraction.

• Sample Dataset Access: Prospective users can view a sample of the dataset by signing in, allowing them to assess its structure and relevance to their specific needs.

• Immediate Availability: The dataset is readily available for purchase at $14.00 and is delivered in JSON format, ensuring seamless integration into various applications and systems.

For businesses and researchers seeking more extensive data, the Powerful Fashion Dataset offers a broader spectrum of fashion-related information. This comprehensive dataset is designed to transform your fashion business by providing insights into trend forecasting, customer behavior analysis, and market dynamics. Leveraging such data can enhance decision-making processes, optimize supply chains, and identify emerging markets, ensuring your brand stays ahead in the competitive fashion industry.

ktrinh38/fashion-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Context

This dataset contains product listings from Vestiaire, an online marketplace for buying and selling pre-owned luxury fashion items. It was scraped using Python and the Hrequests Library. The CSV file contains approximately 900k rows and 36 columns.

Inspiration

Trend Analysis: Investigate current trends in second-hand luxury fashion, such as brands, product types, and item pricing, to gain a deeper understanding of the current market trends.
Geographical Analysis: Analyze which countries are the most active in terms of both buyers and sellers on Vestiaire Collective. Look for trends in user demographics, such as regions with a high concentration of second-hand luxury fashion.
Item Price Prediction: Utilize machine learning algorithms to predict the price of listed items based on various available features.

Fashion

## Overview

Fashion is a dataset for classification tasks - it contains Men annotations for 1,000 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.

Fashion

## Overview

Fashion is a dataset for object detection tasks - it contains Yfdjukj annotations for 844 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).

kg-09/Fashion-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

LAION - Referred Visual Search - Fashion

Introduced in LRVS-Fashion: Extending Visual Search with Referring Instructions Simon Lepage — Jérémie Mary — David Picard CRITEO AI Lab & ENPC

Useful Links Test set — Benchmark Code— LRVS-F Leaderboard — Demo

  Composition

LAION-RVS-Fashion is composed of images from :

LAION 2B EN LAION 2B MULTI TRANSLATED LAION 1B NOLANG TRANSLATED

These images have been grouped based on extracted product IDs. Each product… See the full description on the dataset page: https://huggingface.co/datasets/Slep/LAION-RVS-Fashion.

The Fashion Dataset offers a comprehensive collection of product-level data covering multiple fashion categories including clothing, accessories, dresses, bags, and apparel. With structured fields and metadata, this dataset provides a robust foundation for e-commerce, AI, analytics, and research use cases.

Fashion is one of the most dynamic industries, constantly evolving with new trends, consumer preferences, and product innovations. This dataset captures fashion-related product information across a wide spectrum, ranging from everyday clothing to high-demand accessories. Each record contains essential details along with category and breadcrumb hierarchies, making it easy to organize, analyze, and integrate into various applications.

Researchers, developers, and businesses can leverage this dataset for multiple purposes. It can serve as training data for AI and NLP models, enhancing their understanding of fashion-specific terminology and context. It also supports trend and market analysis, helping retailers and brands track consumer interest across categories like clothing, bags, and dresses. For recommendation systems, this dataset enables personalized product suggestions, improving user experience in online shopping platforms. Additionally, regulatory and compliance teams can use the structured data to verify labeling, product classification, and category accuracy.

Send request for large target dataset

The Fashion Dataset is designed for flexibility, with records organized into major categories:

• Clothing: A wide range of garments covering multiple styles and types.

• Accessories: Fashion items that complement clothing, including jewelry and more.

• Dresses: Formal and casual dress listings with structured data.

• Bags: Handbags, purses, and other fashion bags.

• Apparel: Broader category items classified under general apparel.

By combining structured product data with category hierarchies, this dataset empowers users to conduct retail intelligence, consumer behavior analysis, product classification, and AI-driven insights. It is a valuable resource for businesses seeking to innovate in the digital fashion economy.

Note: Each record includes both a url (main product page) and a buy_url (purchase page). Records are based on the buy_url to ensure unique, product-level data rather than generic landing pages.

🌟 Highlights

• Comprehensive coverage of fashion products across multiple categories.

• Clean, structured data with category and breadcrumb hierarchy.

• Useful for AI, analytics, market research, and recommendation systems.

• Includes url and buy_url fields for accurate product-level references.

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.

dejasi5459/fashion-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

1) Data Introduction • The Clothes Dataset is a classification dataset composed of clothing images collected from Carousell, an online marketplace. It consists of 15 clothing categories (e.g., T-shirts, shirts, jackets, dresses, etc.), and can be utilized in various computer vision tasks such as clothing classification, fashion recommendation systems, virtual try-on applications, and fashion trend analysis.

2) Data Utilization (1) Characteristics of the Clothes Dataset: • The dataset contains clothing images with a wide range of colors, textures, and styles, making it highly suitable for realistic fashion item recognition tasks.

(2) Applications of the Clothes Dataset: • Clothing image classification model development: Can be used to train deep learning models that automatically classify images into 15 clothing categories. • Fashion recommendation and virtual try-on systems: Useful for building AI models that recommend appropriate clothing based on user preferences or body silhouettes.

This dataset is a curated collection of fashion product images paired with their titles and descriptions, designed for training and fine-tuning multimodal AI models. Originally derived from Param Aggraval's "Fashion Product Images Dataset," it has undergone extensive preprocessing to improve usability and efficiency.

Preprocessing steps include:
1. Resizing all images to a median size of 1080 x 1440 px, preserving their original aspect ratio.
2. Streamlining the reference CSV file to retain only essential fields: image file name, display name, product description, and category.
3. Removing redundant style JSON files to minimize dataset complexity.

These optimizations have reduced the dataset size by 73%, making it lighter and faster to use without compromising data quality. This refined dataset is ideal for research and applications in multimodal AI, including tasks like product recommendation, image-text matching, and domain-specific fine-tuning.