Overview

Our dataset is about second-hand fashion making it a valuable resource for researchers, fashion enthusiasts, and data scientists interested in analyzing and understanding the second-hand clothing market. It provides a large collection of clothing items with detailed attributes, allowing for comprehensive analysis of various factors related to second-hand fashion.

Dataset Details

- The dataset includes attributes that are unique to second-hand fashion, such as damage, stains, and more. Whenever possible, ISO standards have been followed to define these attributes on a 1-5 scale (ex: `pilling`), ensuring consistency and comparability across the dataset.

- The data has been annotated by a group of expert second-hand sorters at Wargön Innovation AB, ensuring high-quality and accurate attribute information.

- Images are provided for each clothing item, including front and back views, as well as a separate close-up image of the brand. The image resolutions mostly come in two sizes: `1280x720` and `1920x1080`. Please note that some brand images may be missing.

- This dataset represents approximately 10% of the total dataset that will be eventually created for the Vinnova funded project "AI for resource-efficient circular fashion." The project involves collaboration between RISE Research Institutes of Sweden AB and Wargön Innovation AB.

- Some attributes such as `price` should be considered with caution. Many distinct pricing models exist in the second-hand industry: price by weight, price by brand and demand (similar to first-hand fashion), generic pricing at a fixed value (for example, 1 Euro or 10 SEK). Wargön Innovation AB does not set the prices in practice. These prices are suggestive only.

Dataset Structure

The annotations are structured in JSON format, with each clothing item represented as a JSON object. Each object contains various attributes, including brand, category, type, size, colors, season, price, and more.

Partners

The data collection for this dataset has been carried out in collaboration with the following partners:

1. RISE Research Institutes of Sweden AB: RISE is a leading research institute dedicated to advancing innovation and sustainability across various sectors, including fashion and textiles.

2. Wargön Innovation AB: Wargön Innovation is an expert in sustainable and circular fashion solutions, contributing valuable insights and expertise to the dataset creation.

Contribution

We encourage researchers, data scientists, and fashion enthusiasts to contribute to the dataset by providing additional annotations, images, or insights. Your contributions will help enhance the dataset's comprehensiveness and value, enabling further advancements in AI-driven circular fashion.

Citation

Please use the DOI associated with the Zenodo release and look at the sidebar for citation information.

License

The Clothing Dataset for Second-Hand Fashion is made available under the CC-BY 4.0 license. Please refer to the LICENSE file for more details.

Second-Hand Fashion Dataset

Overview

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.

The dataset has 31,997 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).

Project page

- Webpage: https://fnauman.github.io/second-hand-fashion/">second-hand-fashion
- Contact: farrukh.nauman@ri.se

Dataset Details

- The dataset contains 31,997 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.

- Three images are provided for each clothing item:
1. Front view.
2. Back view.
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.

- 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.

- Each JSON file contains a list of annotations, some of which require nuanced interpretation (see `labels.py` for the options):
- `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.
- `price`: The price field should be viewed as suggestive rather than definitive. Pricing models in the second-hand industry vary widely, including pricing by weight, brand, demand, or fixed value. Wargön Innovation AB does not determine actual pricing.
- `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.'
- `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.
- Damage-related attributes include:
- `condition` (1-5 scale, 5 being the best)
- `pilling` (1-5 scale, 5 meaning no pilling)
- `stains`, `holes`, `smell` (each with options 'None,' 'Minor,' 'Major').

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:

      "damageimage": "back",
      "damageloc": "bottom left",
      "damage": "stain ",
      "damage2image": "front",
      "damage2loc": "None",
      "damage2": "",
      "damage3image": "back",
      "damage3loc": "bottom right",
      "damage3": "stain"

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.
- `comments`: The comments field is mostly empty, but sometimes contains important information about the garment, such as a detailed text description of the damage.

- Whenever possible, ISO standards have been followed to define these attributes on a 1-5 scale (e.g., `pilling`).

- Gold dataset: `Test` inside the comments field is meant for garments that were annotated multiple times by different annotators for annotator agreement comparisons. These 100 garments were annotated twice at Wargön Innovation AB (search within `station1/[dec2022,feb2023]`)and once at Myrorna AB (see `station3/test100` folder for JSON files containing their annotations).

- The data has been annotated by a group of expert second-hand sorters at Wargön Innovation AB and Myrorna AB.

- Some attributes, such as `price`, should be considered with caution. Many distinct pricing models exist in the second-hand industry:
- Price by weight
- Price by brand and demand (similar to first-hand fashion)
- Generic pricing at a fixed value (e.g., 1 Euro or 10 SEK)

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.

Comments

- 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.
- We further learned that a handful of data items were duplicates. Several duplicate images were removed, but about 400 still remain.
- 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.
- Most JSON files parse fine using any standard JSON reader, but a handful that are problematic have been set aside in the `json_errors` folder.
- 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 station1/sep2023 since people used their real names. Since then, we used internally assigned IDs.
- Many brand images contained people's hands, faces, or other personal information. We have removed about 4000-5000 brand images for privacy reasons.
- Please inform us immediately if you find any personal information revelations in the dataset:
- Farrukh Nauman (RISE AB): `farrukh.nauman@ri.se`,
- Susanne Eriksson (Wargön Innovation AB): `susanne.eriksson@wargoninnovation.se`,
- Gabriella Engstrom (Wargön Innovation AB): `gabriella.engstrom@wargoninnovation.se`.

We went through 100k images three times to ensure no personal information is leaked, but we are human and can make mistakes.

Partners

The data collection for this dataset has been carried out in collaboration with the following partners:

1. RISE Research Institutes of Sweden AB: RISE is a leading research institute dedicated to advancing innovation and sustainability across various sectors, including fashion and textiles.

2. Wargön Innovation AB: Wargön Innovation is an expert in sustainable and circular fashion solutions, contributing valuable insights and expertise to the dataset creation.

3. Myrorna AB: Myrorna is Sweden's oldest chain of stores for collecting clothes and furnishings that can be reused.

License

CC-BY 4.0. Please refer to the LICENSE file for more details.

Acknowledgments

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.

Success.ai’s Fashion & Apparel Data for Apparel, Fashion & Luxury Goods Professionals in Asia provides a robust dataset tailored for businesses seeking to connect with key players in Asia’s thriving fashion and luxury goods industries. Covering roles such as brand managers, designers, retail executives, and supply chain leaders, this dataset includes verified contact details, professional insights, and actionable business data.

With access to over 700 million verified global profiles and 130 million profiles focused on Asia, Success.ai ensures your outreach, marketing, and business development strategies are supported by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution positions you to succeed in Asia’s competitive and ever-growing fashion markets.

Why Choose Success.ai’s Fashion & Apparel Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of professionals in apparel, fashion, and luxury goods industries across Asia.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and enhancing communication efficiency.

2. Comprehensive Coverage of Asian Fashion Professionals

   • Includes profiles from major fashion hubs such as China, India, Japan, South Korea, and Southeast Asia.
   • Gain insights into regional consumer trends, emerging fashion markets, and luxury goods opportunities.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership, market expansions, and product launches.
   • Stay aligned with evolving industry trends and capitalize on new opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with professionals across the global fashion and apparel industries, with a focus on Asia.
• 130M+ Profiles in Asia: Gain detailed insights into professionals shaping the region’s fashion and luxury goods markets.
• Verified Contact Details: Access work emails, phone numbers, and business locations for precise targeting.
• Leadership Insights: Engage with designers, brand managers, and retail leaders driving Asia’s fashion trends.

Key Features of the Dataset:

1. Comprehensive Professional Profiles

   • Identify and connect with decision-makers in apparel design, luxury goods branding, retail operations, and supply chain management.
   • Target individuals leading innovation in sustainable fashion, fast fashion, and digital transformation.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (luxury goods, ready-to-wear, footwear), geographic location, or job function.
   • Tailor campaigns to align with specific market needs, such as emerging e-commerce platforms or regional fashion preferences.

3. Industry and Regional Insights

   • Leverage data on consumer behaviors, market growth, and regional trends in Asia’s fashion and luxury goods sectors.
   • Refine marketing strategies, product development, and partnership outreach based on actionable insights.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Brand Expansion

   • Design targeted campaigns to promote apparel, luxury goods, or retail solutions to fashion professionals in Asia.
   • Leverage multi-channel outreach, including email, phone, and social media, to maximize engagement.

2. Product Development and Consumer Insights

   • Utilize data on regional trends and consumer preferences to guide product development and marketing strategies.
   • Collaborate with brand managers and designers to tailor collections or launch new offerings aligned with market demands.

3. Partnership Development and Retail Collaboration

   • Build relationships with retail chains, luxury brands, and supply chain leaders seeking strategic alliances.
   • Foster partnerships that expand distribution channels, enhance brand visibility, or improve operational efficiencies.

4. Market Research and Competitive Analysis

   • Analyze trends in Asia’s fashion industry to refine business strategies, identify market gaps, and anticipate consumer demands.
   • Benchmark against competitors to stay ahead in the fast-paced fashion landscape.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality fashion and apparel data at competitive prices, ensuring strong ROI for your marketing, sales, and product development efforts.

2. Seamless Integration

   • Integrate verified data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, streamlining workfl...

It appears that you've provided a list of column headers typically found in a dataset. To create a Kaggle dataset description, you can provide a detailed explanation of each feature to help users understand the data. Here's a potential description for the features you've listed:

Date: The date when the data entry was recorded or the event occurred.

ID: A unique identifier for each entry in the dataset, serving as a primary key.

SKU_ID: Stock Keeping Unit (SKU) identifier, a unique code assigned to each distinct product or item.

Price: The cost or retail value associated with a particular SKU at a specific point in time.

SKU_Name: The name or description of the SKU, providing information about the product.

Gender: The target demographic for the product, indicating whether it is designed for males, females, or is unisex.

Category: The broad product category to which the SKU belongs (e.g., clothing, electronics, accessories).

Brand: The brand associated with the SKU, representing the company or manufacturer.

Collection: The specific collection or product line to which the SKU belongs.

Price_Tier: A categorization based on price, indicating whether the product is budget-friendly, mid-range, or premium.

Style: The aesthetic or design characteristics of the product, providing information about its appearance or features.

Cost: The cost incurred by the seller or manufacturer to produce or acquire the SKU.

Sales: The quantity of units sold for a particular SKU during a given period.

When creating a Kaggle dataset description, it's essential to include information about the context of the data, its source, potential applications, and any relevant data cleaning or preprocessing that has been performed. Additionally, you may want to specify the data types (e.g., numerical, categorical, date) and any potential challenges or considerations for users working with the dataset.

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.

Second-Hand Fashion Dataset

Update Sep. 19th, 2024

• Some problematic and duplicate images have been removed from version 2.- All "gold dataset" data from station1 and station3 has been moved to a single test100 folder.- JSON errors have been fixed - all JSON files should be parsed correctly now.

The new dataset has 31,638 items (+ about 100 items in test100 folder) instead of the 31,997 items in version 2.

Overview

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.

The dataset has 31,638 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).

Project page

• Webpage: second-hand-fashion- Contact: farrukh.nauman@ri.se

Dataset Details

• 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.

• Three images are provided for each clothing item: 1. Front view. 2. Back view. 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.

• 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.

• Each JSON file contains a list of annotations, some of which require nuanced interpretation (see labels.py for the options): - 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. - 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.' - 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. - Damage-related attributes include: - condition (1-5 scale, 5 being the best) - pilling (1-5 scale, 5 meaning no pilling) - stains, holes, smell (each with options 'None,' 'Minor,' 'Major'). 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:

    "damageimage": "back",
    "damageloc": "bottom left",
    "damage": "stain ",
    "damage2image": "front",
    "damage2loc": "None",
    "damage2": "",
    "damage3image": "back",
    "damage3loc": "bottom right",
    "damage3": "stain"
  
  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.  - `comments`: The comments field is mostly empty, but sometimes contains important information about the garment, such as a detailed text description of the damage. 
  

• Whenever possible, ISO standards have been followed to define these attributes on a 1-5 scale (e.g., pilling).

• 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.

• The data has been annotated by a group of expert second-hand sorters at Wargön Innovation AB and Myrorna AB.

• Some attributes, such as price, should be considered with caution. Many distinct pricing models exist in the second-hand industry: - Price by weight - Price by brand and demand (similar to first-hand fashion) - Generic pricing at a fixed value (e.g., 1 Euro or 10 SEK) 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.

Comments

• 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. - 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.- 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 station1/sep2023 since people used their real names. Since then, we used internally assigned IDs. - Many brand images contained people's hands, faces, or other personal information. We have removed about 4000-5000 brand images for privacy reasons. - Please inform us immediately if you find any personal information revelations in the dataset: - Farrukh Nauman (RISE AB): farrukh.nauman@ri.se, - Susanne Eriksson (Wargön Innovation AB): susanne.eriksson@wargoninnovation.se, - Gabriella Engstrom (Wargön Innovation AB): gabriella.engstrom@wargoninnovation.se.

We went through 100k images four times to ensure no personal information is leaked, but we are human and can make mistakes.

Partners

The data collection for this dataset has been carried out in collaboration with the following partners:

1. RISE Research Institutes of Sweden AB: RISE is a leading research institute dedicated to advancing innovation and sustainability across various sectors, including fashion and textiles.

2. Wargön Innovation AB: Wargön Innovation is an expert in sustainable and circular fashion solutions, contributing valuable insights and expertise to the dataset creation.

3. Myrorna AB: Myrorna is Sweden's oldest chain of stores for collecting clothes and furnishings that can be reused.

License

CC-BY 4.0. Please refer to the LICENSE file for more details.

Acknowledgments

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.

Description

Fashion brands that appeared in Interbrand’s Top 100 Global Brand list from 2001 to 2021 are included in this dataset. The following fields were extracted from the Interbrand’s annual reports having string values.

🟢 Brand Name

🟢 Country of Origin

🟢 Region of origin (America, Europe)

🟢 Industry Sector (Fashion)

🟢 Industry Sub-sector (Apparel, Cosmetics, Luxury and Sportswear)

The following fields are extracted for each year (2001-2021) having numeric values.

🟢 Brand Ranking

🟢 Brand Equity (USD billion)

🟢 Growth in Brand Equity (%)

Categories

Ranking Factor, Brand Equity, Sports Marketing, Apparel Industry, Luxury Goods, Fashion Industry, Cosmetics Industry

Acknowledgements & Source

Kamran Siddiqui

Institutions: Imam Abdulrahman Bin Faisal University

Data Source

View Details

Image Source

Please don't forget to upvote if you find this useful.

Success.ai’s Fashion & Apparel Data for Apparel, Fashion & Luxury Goods Professionals in North America offers a comprehensive dataset designed to help businesses connect with decision-makers and key professionals in the dynamic fashion and apparel industry. Covering roles such as designers, brand managers, retail executives, and supply chain leaders, this dataset provides verified contact details, professional insights, and actionable business data.

With access to over 700 million verified global profiles, including 130 million in North America, Success.ai ensures your marketing, outreach, and business development strategies are powered by accurate, continuously updated, and AI-validated information. Backed by our Best Price Guarantee, this solution is indispensable for thriving in North America’s competitive fashion market.

Why Choose Success.ai’s Fashion & Apparel Data?

1. Verified Contact Data for Targeted Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of professionals in apparel, fashion, and luxury goods industries.
   • AI-driven validation ensures 99% accuracy, improving communication efficiency and minimizing data gaps.

2. Comprehensive Coverage of North American Fashion Professionals

   • Includes profiles of professionals from major fashion hubs such as New York, Los Angeles, Chicago, and Miami.
   • Gain insights into regional trends, consumer preferences, and market opportunities in the fashion and luxury goods sectors.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership, brand expansions, and market dynamics.
   • Stay aligned with evolving industry trends and seize new opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful use of data.

Data Highlights:

• 700M+ Verified Global Profiles: Engage with apparel, fashion, and luxury goods professionals worldwide, with a focus on North America.
• 130M+ Profiles in North America: Gain verified contact details and actionable insights into the region’s top professionals.
• Contact Details: Access work emails, phone numbers, and business addresses for precision targeting.
• Leadership Insights: Connect with C-suite executives, brand managers, and designers driving innovation in the fashion industry.

Key Features of the Dataset:

1. Professional Profiles in Fashion and Apparel

   • Identify and connect with professionals responsible for product design, brand management, retail operations, and luxury goods marketing.
   • Target individuals leading creative initiatives, logistics, and digital transformation in the fashion industry.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (apparel, luxury goods, footwear), geographic location, or job function.
   • Tailor campaigns to align with specific market needs such as sustainable fashion, e-commerce, or retail expansion.

3. Regional Trends and Industry Insights

   • Leverage data on emerging fashion trends, consumer demands, and market growth in North America.
   • Refine marketing and product development strategies to align with audience expectations and market opportunities.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Brand Outreach

   • Design targeted campaigns to promote apparel, luxury goods, or supply chain solutions to professionals in the fashion industry.
   • Use verified contact data for multi-channel outreach, including email, phone, and social media.

2. Product Development and Innovation

   • Utilize insights into consumer preferences and fashion trends to guide product design and marketing strategies.
   • Collaborate with designers and brand managers to refine collections or launch new products.

3. Partnership Development and Collaboration

   • Build relationships with apparel brands, luxury retailers, and fashion designers exploring strategic alliances.
   • Foster partnerships that expand market reach, enhance brand visibility, or improve supply chain efficiency.

4. Market Research and Competitive Analysis

   • Analyze trends in North America’s fashion industry to refine business strategies, identify market gaps, and anticipate consumer demands.
   • Benchmark against competitors to stay ahead in the rapidly evolving fashion landscape.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality fashion and apparel data at competitive prices, ensuring strong ROI for your marketing, sales, and product development efforts.

2. Seamless Integration

   • Integrate verified fashion data into CRM systems, analytics platforms, or marketing tools via APIs or dow...

Fashion Retail Sales Dataset

Introduction The "Fashion Retail Sales" is a comprehensive collection of data representing sales transactions from a clothing store. This dataset provides valuable insights into the purchasing behavior of customers, the items they buy, the payment methods used, and their satisfaction levels with the products. It is a rich source of information for retail analysts, data scientists, and business owners looking to understand and optimize their clothing store's operations.

Context In today's dynamic and competitive retail environment, understanding customer preferences and optimizing sales processes is crucial for the success of any clothing store. The "Fashion Retail Sales Dataset" has been meticulously curated to offer a diverse and realistic portrayal of customer interactions with the store. It encompasses data points such as customer reference IDs, purchased items, transaction amounts, purchase dates, review ratings, and payment methods. This dataset has been designed to simulate a real-world scenario and reflects the complexities of a clothing store's day-to-day operations.

Description The "Fashion Retail Sales Dataset" consists of six key columns:

• Customer Reference ID: This column contains unique identifiers for customers, enabling the tracking of individual buying patterns and preferences.
• Item Purchased: It provides information about the clothing items that customers have bought. This column includes a wide variety of clothing items, ranging from T-shirts and jeans to accessories like scarves and hats.
• Purchase Amount (USD): This column details the amount of money spent by each customer for their purchases. It may contain outliers, reflecting occasional high-value purchases.
• Date Purchase: The purchase date indicates when each transaction occurred, offering a temporal perspective on buying trends and seasonality.
• Review Rating: Customers' satisfaction levels are quantified using this column, with ratings ranging from 1 to 5. It is an essential metric for assessing product quality and customer experience.
• Payment Method: This column reveals the method used by customers to make payments, with options including 'Credit Card' and 'Cash'.

In this study, 403 Chinese consumers generalizable to the broader population were surveyed on their motivations to shop for fashion apparel in both high street and e-commerce environments. Statistical analysis was undertaken through multiple T-Tests and MANOVA with the assistance of SPSS and G*Power.

To increase the profits of international brands, this paper presents the motivations of Chinese consumers to engage in fashion retail, building upon established theory in hedonic and utilitarian motivations. With China set to capture over 24% of the $212 billion fashion market, international brands need to understand the unique motivations of Chinese consumers in order to capitalise on the market. However, the motivations of Chinese people to engage in fashion retail are as yet undefined, limiting the ability for international fashion retailers to operate with prosperity in the Chinese market.

👟 Sneakers & Streetwear Sales Data (2022)

This dataset offers a detailed snapshot of global retail sales from the fast-growing sneaker and streetwear market between January and August 2022. It captures essential sales insights from multiple countries, spanning brands like Nike, Adidas, Supreme, Yeezy, and Off-White, along with high-demand categories such as sneakers, hoodies, joggers, and graphic tees.

The data has been carefully simulated to mirror real-world patterns in retail e-commerce — including seasonality, gender preferences, price bands, and payment behaviors. Each record represents a successful transaction, making this dataset ideal for sales analytics, business intelligence projects, and predictive modeling.

📦 What’s Inside?

• 500 clean, non-null, and unique sales records
• Covering 10 countries and 30+ product names
• Fields include: Order Date, Country, Gender, Product, Category, Quantity Sold, Unit Price, Total Sale, and Payment Method

💡 Why This Dataset?

Sneakers and streetwear aren't just fashion — they're a data-rich ecosystem of global trends, influencer impact, resale value, and cultural relevance. Whether you're working on:

• EDA & trend visualization
• Time-series forecasting
• Market basket analysis
• Customer segmentation
• Sales dashboards

… this dataset gives you everything you need to explore, model, and tell a data story.

✅ Key Features

• Realistic sales simulation for 2022
• Useful for beginners and advanced practitioners alike
• Cleaned and curated — ready for analysis, dashboards, and ML
• Ideal for Power BI, Tableau, Python (Pandas, Seaborn, Plotly), and ML libraries

Forecast: Leather-Based or Leather Apparel Market Size Value in Poland 2023 - 2027 Discover more data with ReportLinker!

Forecast: Leather-Based or Leather Apparel Market Size Value in Germany 2023 - 2027 Discover more data with ReportLinker!

With the upgrading of consumption concepts, the fashion industry has huge market potential. According to statistics from authoritative organizations, the global market value of the fashion industry has exceeded US$3 trillion. At the same time, AI technology is also developing continuously, but the technology still faces many challenges in the process of integrating with the fashion industry. In order to promote the combination of AI technology and clothing fashion, Alibaba's "Image Harmony" team teamed up with the Department of Textiles and Clothing of the Hong Kong Polytechnic University to launch the industry's first large-scale high-quality fashion data set that meets both clothing professionalism and machine learning requirements, focusing on machines. There are two basic issues in cognitive fashion: clothing key point positioning and clothing attribute label identification. The clothing attribute label recognition data set was generated under this background. Clothing attribute tags are an important foundation for the clothing knowledge system, which is huge and complex. We have professionally organized and abstracted the clothing attributes, and built a label knowledge system that is consistent with the cognitive process, structured and meets the requirements of machine learning. The clothing attribute tag recognition technology born from this can be widely used in clothing image retrieval, tag navigation, clothing matching and other application scenarios. The image data is collected from Alibaba e-commerce data. This research topic focuses on local attribute identification of clothing products. All clearly identifiable attribute labels in the picture require prediction. Considering the complexity of clothing knowledge, this data set only retains the product image data of a single subject (single model or single piece tile), so that researchers can focus on solving the challenges in the attribute labeling task.

Description

Context Semantic Segmentation is one of major tasks in Computer Vision. It is the pixel-wise classification of an image into object classes. This dataset contains 1000 images and segmentation masks pairs of individual people's clothing. With 59 object classes and a relatively lesser data, the task of modelling is expected to be a challenging one! The data needs no preprocessing, all images are of same size, same format, and ready to model.

Content The dataset contains 1000 images and 1000 corresponding semantic segmentation masks each of size 825 pixels by 550 pixels in PNG format. The segmentation masks belong to 59 classes, the first being the background of individuals, and the rest belong to 58 clothing classes such as shirt, hair, pants, skin, shoes, glasses and so on. A CSV file containing the list of 59 classes is included in the dataset. The dataset contains data in both JPEG formats and PNG formats. However, JPEG is found to be lossy, while PNG is lossless with the essence of Originality.

Introducing a Compact Clothing Dataset: This dataset showcases a collection of fashion items across six distinct categories — T-Shirts, Shirts, Pants, Shorts, Shoes, and Sneakers. With a focus on diversity, it provides a snapshot of styles, colors, and patterns, making it an ideal resource for small-scale fashion analysis, image recognition, and machine learning applications.

Key Features:

Diversity Across Categories:

The dataset encapsulates an array of clothing categories, ensuring representation from various facets of the fashion landscape. From casual wear such as T-Shirts and Shorts to more formal attire like Shirts and Pants, and even encompassing footwear options with Shoes and Sneakers, this collection is designed to be inclusive and versatile. Snapshot of Styles:

With a keen emphasis on diversity, the dataset provides a snapshot of contemporary fashion trends. Users can explore a wide array of styles, from minimalist designs to vibrant patterns, facilitating a nuanced understanding of the evolving preferences in the fashion industry. Ideal for Small-Scale Analysis:

Tailored for small-scale fashion analysis, this dataset is perfect for researchers, developers, and enthusiasts looking to conduct insightful studies on trends, consumer preferences, and stylistic variations within a limited scope. Image Recognition and Machine Learning Applications:

The dataset is well-suited for image recognition and machine learning applications. Researchers and practitioners can leverage this resource to train and test algorithms, develop fashion recommendation systems, and explore the intersection of technology and style. Accessible Resource:

Whether you are a novice in the field of machine learning or an experienced researcher, this dataset serves as a valuable and accessible resource. Its compact size makes it particularly suitable for educational purposes, enabling learners to grasp fundamental concepts in image classification and pattern recognition. Usage Scenarios:

The Compact Clothing Dataset finds utility in a variety of scenarios, including but not limited to:

Developing and testing image classification algorithms. Training machine learning models for fashion recognition. Conducting small-scale fashion trend analyses. Exploring the correlation between styles and color patterns. In summary, this Compact Clothing Dataset emerges as an indispensable tool for those delving into the multifaceted world of fashion analysis and machine learning applications. With its thoughtful curation and focus on diversity, it paves the way for innovative exploration and understanding of the intricate nuances within the realm of clothing and style.

The Intelligent Multi-Layer Clothing Classification Dataset is designed for dynamic thermal comfort evaluation in energy-efficient environments. It provides data on clothing attributes, environmental conditions, and physiological responses to help develop AI-driven models for optimizing thermal adaptation.

This dataset supports clothing classification based on fabric type, thickness, and coverage while integrating IoT sensor data (temperature, humidity, air velocity, radiant heat) and wearable device data (skin temperature, heart rate variability, perspiration rate). Additionally, Clo values from infrared thermal imaging ensure accurate heat insulation estimation.

The dataset is ideal for machine learning models in thermal comfort analysis, smart clothing systems, and personalized climate control applications.

Key Features: Clothing Data: Fabric type, thickness, coverage, and shape.

Environmental Data: Temperature, humidity, air velocity, radiant heat.

Physiological Data: Skin temperature, heart rate variability, perspiration rate.

Infrared Imaging Data: Clo value estimation for insulation analysis.

Target Variable: Clothing classification (Single Layer, Double Layer, Multi-Layer).

VARSew dataset is designed for visual action recognition in the garment sewing industry. Also, VARSew is designed to address multiple research objectives in visual recognition, such as binary and multi-class action classification. The proposed data offers new research opportunities, such as visual pattern recognition and Internet-of-Things(IOT) based manufacturing. Also, it offers research opportunities on human activity and behavior during production time. To the best of our knowledge, no such industrial human action dataset is available in the computer vision community. VARSew dataset consists of high-resolution trimmed videos of certain actions. There are two levels of categorical labels in the VARSew dataset, i.e., super action classes and actions classes. The dataset includes 3,121 videos of 49,936 frames. The videos are grouped into value-added and non-value-added for human sewing action binary classification. For multi-class classification, the videos are also grouped into eight classes: sew, release, handle, prepare, adjust, wait, check, and maintain. Each video was fixed to 16 frames. Multiple human operators were employed to collect the videos, including Sewing Machine Operators (SMO) and Maintenance Machine Operators (MMO), respectively 26 and 8 operators. Subjective annotations for the participant proficiency throughout the 3,121 videos.

A league table of the 120 cryptocurrencies with the highest market cap reveals how diverse each crypto is and potentially how much risk is involved when investing in one. Bitcoin (BTC), for instance, had a so-called "high cap" - a market cap worth more than 10 billion U.S. dollars - indicating this crypto project has a certain track record or, at the very least, is considered a major player in the cryptocurrency space. This is different in Decentralize Finance (DeFi), where Bitcoin is only a relatively new player. A concentrated market The number of existing cryptocurrencies is several thousands, even if most have a limited significance. Indeed, Bitcoin and Ethereum account for nearly 75 percent of the entire crypto market capitalization. As crypto is relatively easy to create, the range of projects varies significantly - from improving payments to solving real-world issues, but also meme coins and more speculative investments. Crypto is not considered a payment method While often talked about as an investment vehicle, cryptocurrencies have not yet established a clear use case in day-to-day life. Central bankers found that usefulness of crypto in domestic payments or remittances to be negligible. A forecast for the world's main online payment methods took a similar stance: It predicts that cryptocurrency would only take up 0.2 percent of total transaction value by 2027.

Forecast: Leather Apparel Market Size Value in the UK 2022 - 2026 Discover more data with ReportLinker!

Online shopping for customized garments has become the fastest-growing field of the Chinese eBusiness market. Most consumers not only limit themselves to buying standardized garments but also want to buy garments customized to their preferences. This phenomenon has pushed the fashion textile and apparel industry to change its supply chain operations to meet the customization demand. Besides, the fashion textile and apparel industry also want to study how different channel factors will affect consumers' perceived value and further influence consumers' purchasing decisions. We initiated this study and empirically tested more than 200 experienced consumers. This study collaborated with a fashion textile and apparel company that aims to implement customized product lines soon. Based on the perceived value theory and risk management theory, we investigated whether product involvement and channel identification on supply chain design will affects potential customized product consumers' purchasing decisions. The findings reveal that channel recognition affects consumer decisions by having a positive impact on their perceived value. The perceived risk and shopping channel involvement of consumers have a negative impact on their perceived values and channel selections. In addition, product involvement has a moderating effect on the relationship between channel's perceived risk, perceived values, and channel selections as well.

From the apparel industry lifecycle, fiber production was the stage that withdrew the highest volume of freshwater in 2016, amounting to some **** billion cubic meters. Overall, the apparel industry's water abstraction reached around *** billion metric tons in this year.