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.

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">

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.

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.

Fashion-MNIST is a dataset comprising of 28×28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST shares the same image size, data format and the structure of training and testing splits with the original MNIST.

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

## Overview

Fashion is a dataset for object detection tasks - it contains Man annotations for 971 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 [MIT license](https://creativecommons.org/licenses/MIT).

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

Unlock a curated dataset of 18,000+ fashion products from Farfetch, a leading global fashion platform. This dataset covers high-end and emerging designer brands across men's, women's, and unisex categories — perfect for powering retail analytics, trend detection, and AI-driven fashion applications.

Whether you're building a product matching engine, conducting price intelligence, or training recommendation systems, this structured dataset gives you direct insight into global luxury retail at scale.

Delivered clean, deduplicated, and crawl-ready, it supports both market researchers and developers working in ecommerce, fashion tech, or retail platforms.

Use Cases

• Competitive price analysis and product benchmarking

• Fashion trend prediction and forecasting

• Retail catalog enrichment or matching

• Cross-platform brand visibility comparison

• AI/ML model training (e.g., recommendation engines)

• Inventory and availability tracking for luxury fashion

DeepFashion is a dataset containing around 800K diverse fashion images with their rich annotations (46 categories, 1,000 descriptive attributes, bounding boxes and landmark information) ranging from well-posed product images to real-world-like consumer photos.

Fashion IQ support and advance research on interactive fashion image retrieval. Fashion IQ is the first fashion dataset to provide human-generated captions that distinguish similar pairs of garment images together with side-information consisting of real-world product descriptions and derived visual attribute labels for these images.

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.

This dataset highlights the often hidden costs of fast fashion. It includes brand-wise production data, environmental impact metrics like carbon emissions and water usage, labor conditions including wages and child labor reports, social media sentiment, and consumer behavior trends. Sourced from simulated realistic data inspired by open data, this dataset aims to support EDA, forecasting, policy modeling, and storytelling on sustainability and ethics in fashion

Dataset Card for Fashionpedia

  Dataset Summary

Fashionpedia is a dataset mapping out the visual aspects of the fashion world. From the paper:

Fashionpedia is a new dataset which consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, 294 fine-grained attributes and their relationships; (2) a dataset with everyday and celebrity event fashion images annotated with segmentation masks and their associated… See the full description on the dataset page: https://huggingface.co/datasets/detection-datasets/fashionpedia.

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.

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.

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.

Dataset

This dataset was created by abhishek

Contents

Threads Of Fashion

## Overview

Threads Of Fashion is a dataset for instance segmentation tasks - it contains Clothing annotations for 2,910 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).