Title: Clothing Fit Dataset

Description: The Clothing Fit Dataset is derived from ModCloth and RentTheRunway, containing measurements related to clothing fit. This dataset provides an opportunity to develop and evaluate models targeting product size recommendations, understanding user preferences, and analyzing customer feedback regarding the fit of clothing items.

Basic Statistics: - ModCloth: - Users: 47,958 - Items: 1,378 - Transactions: 82,790 - RentTheRunway: - Users: 105,508 - Items: 5,850 - Transactions: 192,544

Metadata: - Ratings and Reviews: Customer ratings and textual reviews for the clothing items. - Fit Feedback: Feedback regarding the fit of the clothing items (e.g., small, fit, large, etc.). - User/Item Measurements: Physical measurements of users and items. - Category Information: Information regarding the category of clothing items.

Example (RentTheRunway): Each entry in the dataset contains detailed information about a user's experience with a clothing item, including the fit, user and item measurements, rating, review text, and category. json { "fit": "fit", "user_id": "420272", "bust size": "34d", "item_id": "2260466", "weight": "137lbs", "rating": "10", "rented for": "vacation", "review_text": "An adorable romper! Belt and zipper were a little hard to navigate in a full day of wear/bathroom use, but that's to be expected. Wish it had pockets, but other than that-- absolutely perfect! I got a million compliments.", "body type": "hourglass", "review_summary": "So many compliments!", "category": "romper", "height": "5' 8\"", "size": 14, "age": "28", "review_date": "April 20, 2016" }

Download Links: - Modcloth: Download Link (8.5mb) - Renttherunway: Download Link (31mb)

Citation: If you utilize this dataset, please cite the following paper: Title: Decomposing fit semantics for product size recommendation in metric spaces Authors: Rishabh Misra, Mengting Wan, Julian McAuley Published in: RecSys, 2018 Link to paper

This dataset is valuable for researchers and practitioners in the domain of e-commerce, fashion retail, and recommender systems.

Context

Product size recommendation and fit prediction are critical in order to improve customers’ shopping experiences and to reduce product return rates. However, modeling customers’ fit feedback is challenging due to its subtle semantics, arising from the subjective evaluation of products and imbalanced label distribution (most of the feedbacks are "Fit"). These datasets, which are the only fit related datasets available publically at this time, collected from ModCloth and RentTheRunWay could be used to address these challenges to improve the recommendation process.

Content

Following type of information is available in the datasets:

• ratings and reviews
• fit feedback (small/fit/large)
• customer/product measurements
• category information

Please refer to file description for more granular details. These datasets are highly sparse, with most products and customers having only a single transaction. Note that, here a ‘product’ refers to a specific size of a product, as our goal is to predict fitness for associated catalog sizes. Also, since different clothing products use different sizing conventions, we standardize sizes into a single numerical scale preserving the order. Please read the paper for further details.

Citation

If you're using this dataset for your work, please cite the following articles:

Citation in text format: 1. Misra, Rishabh, Mengting Wan, and Julian McAuley. "Decomposing fit semantics for product size recommendation in metric spaces." In Proceedings of the 12th ACM Conference on Recommender Systems, pp. 422-426. 2018. 2. Misra, Rishabh and Jigyasa Grover. "Sculpting Data for ML: The first act of Machine Learning." ISBN 9798585463570 (2021). Citation in BibTex format: @inproceedings{misra2018decomposing, title={Decomposing fit semantics for product size recommendation in metric spaces}, author={Misra, Rishabh and Wan, Mengting and McAuley, Julian}, booktitle={Proceedings of the 12th ACM Conference on Recommender Systems}, pages={422--426}, year={2018}, organization={ACM} } @book{misra2021sculpting, author = {Misra, Rishabh and Grover, Jigyasa}, year = {2021}, month = {01}, pages = {}, title = {Sculpting Data for ML: The first act of Machine Learning}, isbn = {9798585463570} }

Please link to rishabhmisra.github.io/publications as the source of this dataset. Thanks!

Inspiration

These datasets obtained for ModCloth and RentTheRunWay could be used:

• To tackle the catalog size recommendation problem.

• To tackle the review or rating prediction problems.

• To generate a summary of reviews (given both full review text and summary are provided).

Want to contribute your own datasets?

If you are interested in learning how to collect high-quality datasets for various ML tasks and the overall importance of data in the ML ecosystem, consider reading my book Sculpting Data for ML.

Other datasets

Please also checkout the following datasets collected by me:

• News Headlines Dataset For Sarcasm Detection

• News Category Dataset

• IMDB Spoiler Dataset

• Politifact Fact Check Dataset

This dataset contains 1,000 rows of individual user data and 420+ standardized photos representing 1,000+ people, providing a rich resource for personalized clothing design, body measurement estimation, and sizing recommendations in e-commerce. Each user is captured with multiple anthropometric measurements, lifestyle activity information, and style preferences. The dataset includes 19 columns covering key features for fit prediction, style recommendation, and user satisfaction assessment.

Key Features (Columns):

UserID – Unique identifier for each user

Age – Age of the user (years)

Gender – Gender of the user (Male/Female/Other)

Height_cm – User height in centimeters

Weight_kg – User weight in kilograms

Chest_cm – Chest measurement

Waist_cm – Waist measurement

Hip_cm – Hip measurement

ShoulderWidth_cm – Shoulder width

ArmLength_cm – Arm length

LegLength_cm – Leg length

PostureScore – Posture assessment score (0–1)

ActivityLevel – Lifestyle activity level (Low/Medium/High)

StylePreference – Preferred clothing style

ColorPreference – Preferred clothing color

FitScore – Fit assessment score (0–1)

SatisfactionScore – User satisfaction score (0–1)

RecommendedSize – Suggested clothing size

RecommendedStyle – Suggested clothing style

Fit Recommendation AI Market Outlook

According to our latest research, the global Fit Recommendation AI market size stood at USD 1.72 billion in 2024, reflecting a robust adoption curve across diverse retail and fitness sectors. The market is forecasted to reach USD 10.36 billion by 2033, expanding at a striking CAGR of 21.8% from 2025 to 2033. This impressive growth is primarily driven by the surging demand for personalized shopping experiences, rapid digital transformation in retail, and the increasing integration of artificial intelligence in consumer-facing applications. As companies strive to reduce product returns and enhance customer satisfaction, Fit Recommendation AI solutions are becoming a pivotal differentiator in the global marketplace.

The evolution of e-commerce and the digitalization of brick-and-mortar retail have significantly contributed to the growth of the Fit Recommendation AI market. Retailers and brands are increasingly leveraging AI-driven fit recommendation systems to address the persistent challenge of product returns, which often result from sizing and fit issues. By harnessing advanced algorithms, computer vision, and deep learning, these solutions can accurately predict the best fit for individual customers, thereby reducing the likelihood of returns and improving overall customer satisfaction. Furthermore, the integration of Fit Recommendation AI with virtual try-on solutions and augmented reality is enhancing the online shopping experience, driving higher conversion rates and fostering greater brand loyalty.

Another key growth factor is the rising consumer preference for personalized shopping journeys, especially in the apparel, footwear, and eyewear segments. Consumers today expect tailored recommendations that align with their unique body shapes, preferences, and lifestyle choices. Fit Recommendation AI systems utilize vast datasets, including purchase history, user feedback, and biometric data, to deliver highly personalized suggestions. This not only enhances the customer experience but also enables retailers to differentiate themselves in an increasingly competitive landscape. The proliferation of mobile shopping apps and omnichannel retail strategies further amplifies the need for seamless and accurate fit recommendation technologies.

Technological advancements in machine learning, computer vision, and natural language processing are also propelling the market forward. The continuous improvement of AI models enables more precise and context-aware fit recommendations, even for complex product categories such as sportswear and athleisure. Moreover, the growing adoption of cloud-based deployment models is making these solutions more accessible to small and medium enterprises (SMEs), democratizing the benefits of AI-powered fit recommendations. Strategic partnerships between technology providers, retailers, and fashion brands are fostering innovation and accelerating market adoption across regions.

From a regional perspective, North America currently leads the Fit Recommendation AI market, driven by the presence of major e-commerce players, high consumer awareness, and significant investments in AI technologies. Europe follows closely, with a strong emphasis on innovation and digital transformation in retail. The Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by the rapid expansion of online retail, increasing smartphone penetration, and a burgeoning middle-class population. Latin America and the Middle East & Africa are also emerging as promising markets, supported by growing e-commerce ecosystems and rising consumer expectations for personalized shopping experiences.

The emergence of an AI Clothing Size Exchange Platform is revolutionizing how retailers and consumers approach sizing and fit challenges. By leveraging artificial intelligence, these platforms facilitate seamless exchanges and returns by predicting the most accurate clothing sizes for customers. This not only enhances customer satisfaction but also significantly reduces the operational costs associated with handling returns. As retailers integrate these platforms into their systems, they can offer a more streamlined shopping experience, ensuring that customers receive the right fit the first time. This technological advancement is particularly be

Size & Fit Recommendation API Market Outlook

According to our latest research, the global Size & Fit Recommendation API market size in 2024 stands at USD 1.13 billion, registering a robust CAGR of 21.6% from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 8.08 billion. This remarkable growth trajectory is primarily driven by the surging adoption of digital transformation in the retail sector, the increasing demand for personalized shopping experiences, and the rapid expansion of e-commerce across all major regions.

A primary growth factor for the Size & Fit Recommendation API market is the exponential rise in online shopping, particularly in apparel, footwear, and accessories. As consumers increasingly shift their purchasing behavior toward digital channels, the challenge of product returns due to poor fit or sizing discrepancies has become more pronounced. Retailers and brands are leveraging advanced APIs to provide real-time, data-driven size and fit recommendations, significantly reducing return rates and enhancing customer satisfaction. The integration of AI and machine learning algorithms into these APIs further refines the accuracy of recommendations, fostering greater consumer trust and loyalty in online retail platforms.

Another significant driver is the growing emphasis on enhancing the customer journey through hyper-personalization. Modern consumers expect tailored experiences, especially when it comes to fit-sensitive products such as eyewear, footwear, and apparel. Size & Fit Recommendation APIs enable retailers to collect and analyze a multitude of data points, including body measurements, purchase history, and user preferences, to deliver highly customized recommendations. This not only boosts conversion rates but also deepens brand engagement and encourages repeat purchases. Furthermore, the proliferation of mobile commerce and the increasing integration of these APIs into mobile applications have further broadened the market’s reach and accessibility.

The market is also benefiting from technological advancements and strategic collaborations among technology providers, fashion brands, and e-commerce platforms. The adoption of cloud-based deployment models has made it easier for both large enterprises and small and medium businesses to implement sophisticated recommendation solutions without significant upfront investments. Additionally, the growing trend of omnichannel retailing, where brands aim to provide a seamless shopping experience across online and offline channels, is fueling the demand for Size & Fit Recommendation APIs. These APIs enable consistent fit recommendations regardless of the shopping channel, thus enhancing overall customer experience and operational efficiency.

From a regional perspective, North America currently leads the market, driven by the presence of major e-commerce giants and early technology adopters. However, Asia Pacific is anticipated to witness the fastest growth, supported by a rapidly expanding middle-class population, increasing smartphone penetration, and a booming e-commerce ecosystem. Europe also holds a significant share, owing to the strong presence of fashion-conscious consumers and established retail infrastructure. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, spurred by growing internet accessibility and the digitalization of retail businesses.

Component Analysis

The Size & Fit Recommendation API market by component is broadly segmented into software and services. The software segment dominates the market, accounting for the majority of the revenue share. This dominance is attributed to the continuous advancements in artificial intelligence, machine learning, and data analytics, which are integral to the development of robust recommendation engines. These software solutions are designed to seamlessly integrate with existing e-commerce and retail platforms, offering real-time fit and size suggestions based on complex algorithms and vast datasets. The scalability and flexibility of these solutions make them highly attractive for both large enterprises and smaller retailers looking to enhance customer experience.

The services segment, while smaller in comparison, is experiencing significant growth as retailers and brands increasingly seek expert guidance for implementation, integrati

The dataset is collected on our own from various sources. This dataset comprises a comprehensive collection of reviews pertaining to clothing products and serves as a valuable resource for multilabel classification research. Each data entry is meticulously annotated with relevant labels, allowing researchers to explore various dimensions of the clothing products being reviewed. The dataset offers a rich diversity of perspectives and opinions, enabling the development and evaluation of robust classification models that can accurately predict multiple aspects of a given clothing item. With its focus on multilabel classification, this data contributes significantly to advancing the understanding and application of machine learning algorithms in the fashion industry.

Girawan, Nadhif ; Alamsyah, Andry (2023), “Consumer Review of Clothing Product”, Mendeley Data, V2, doi: 10.17632/pg3s4hw68k.2 The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

AI Clothing Size Exchange Platform Market Outlook

According to our latest research, the global AI Clothing Size Exchange Platform market size reached USD 1.27 billion in 2024, demonstrating robust momentum as retailers, brands, and consumers increasingly seek digital solutions for sizing challenges in the apparel industry. The market is expected to expand at a CAGR of 21.3% from 2025 to 2033, with projections indicating the market will reach USD 8.93 billion by 2033. This accelerated growth is primarily driven by the surge in e-commerce adoption, the demand for personalized shopping experiences, and the ongoing digital transformation across the global fashion and retail sectors. As per our latest research, these factors are reshaping how brands and consumers approach apparel sizing, fueling the rapid expansion of AI-powered clothing size exchange platforms worldwide.

One of the most significant growth factors for the AI Clothing Size Exchange Platform market is the increasing penetration of e-commerce and the rising frequency of online apparel purchases. Consumers are often hesitant to buy clothing online due to sizing uncertainties, leading to high return rates and operational inefficiencies for retailers. AI-driven platforms address this challenge by leveraging advanced algorithms, machine learning, and user data to provide accurate size recommendations and facilitate seamless size exchanges. This not only enhances the customer experience by reducing the guesswork in sizing but also helps retailers minimize costly returns, boost conversion rates, and build customer loyalty. The widespread adoption of smartphones and the growing comfort with digital shopping further amplify the need for such innovative solutions, positioning AI-powered sizing platforms as indispensable tools for the modern retail ecosystem.

Another pivotal driver is the increasing emphasis on sustainability and operational efficiency within the fashion and apparel industry. High return rates, often stemming from improper sizing, contribute significantly to environmental waste and increased logistics costs. AI Clothing Size Exchange Platforms help mitigate these issues by ensuring better fit accuracy, thereby reducing the volume of returned items and the associated carbon footprint. Brands and retailers are under mounting pressure from both consumers and regulatory bodies to adopt more sustainable practices, and AI-driven sizing solutions align perfectly with these objectives. By streamlining the size exchange process and minimizing waste, these platforms not only support environmental goals but also drive cost savings and operational efficiencies throughout the supply chain.

Furthermore, advancements in artificial intelligence, data analytics, and cloud computing are enhancing the capabilities and scalability of clothing size exchange platforms. Modern AI algorithms can analyze vast datasets, including body measurements, purchase history, and fit feedback, to deliver highly personalized and accurate size recommendations. The integration of 3D body scanning, augmented reality, and virtual fitting rooms further elevates the user experience, making the process of finding the right size intuitive and engaging. As technology continues to evolve, the potential for even greater precision and automation in size exchanges will drive further adoption among retailers, brands, and consumers alike. These technological advancements are expected to remain a cornerstone of innovation and growth in the AI Clothing Size Exchange Platform market over the coming years.

Regionally, North America and Europe are at the forefront of market adoption, driven by high digital literacy, mature e-commerce ecosystems, and strong investments in retail technology. The Asia Pacific region, however, is emerging as the fastest-growing market, fueled by rapid urbanization, increasing smartphone penetration, and a burgeoning middle class with rising disposable income. Latin America and the Middle East & Africa are also witnessing steady growth, supported by expanding retail infrastructure and growing consumer awareness of digital shopping solutions. Each region presents unique opportunities and challenges, with local consumer preferences, regulatory environments, and technology adoption rates influencing the pace and nature of market development.

The integration of "https://growthmarketreports.com/report/size-and-fit-prediction-ai-market" target="_blank">Size and Fit Pr

Size and Fit Data Exchange Market Outlook

According to our latest research and analysis, the global Size and Fit Data Exchange market size reached USD 1.42 billion in 2024, and it is anticipated to grow at a robust CAGR of 13.8% from 2025 to 2033. By the end of the forecast period in 2033, the market is projected to attain a value of approximately USD 4.14 billion. The principal growth factor driving this market is the increasing demand for personalized shopping experiences and the reduction of product returns, particularly in the apparel and footwear industries.

One of the key growth drivers for the Size and Fit Data Exchange market is the rapid digital transformation of the retail sector. As consumers increasingly turn to online shopping, retailers and brands face the challenge of providing accurate sizing information to minimize returns and improve customer satisfaction. The integration of advanced technologies such as artificial intelligence, machine learning, and big data analytics into size and fit data exchange solutions has enabled businesses to offer highly personalized recommendations. This, in turn, enhances the overall shopping experience and fosters customer loyalty. The proliferation of e-commerce platforms and the expansion of digital marketplaces have further accelerated the adoption of these solutions, making them an indispensable tool for modern retail operations.

Another significant factor propelling market growth is the rising focus on sustainability and operational efficiency. High return rates due to size and fit issues not only erode profit margins but also contribute to environmental waste and increased logistics costs. By leveraging size and fit data exchange systems, retailers and brands can significantly reduce the volume of returns, streamline inventory management, and optimize supply chain operations. Moreover, as environmental regulations tighten and consumers become more eco-conscious, the demand for sustainable business practices is expected to bolster the adoption of these solutions across the value chain. This trend is particularly evident in the apparel and footwear segments, where returns and exchanges are most prevalent.

The market is also benefitting from the growing adoption of omnichannel retail strategies. Retailers are increasingly seeking to provide a seamless and consistent shopping experience across physical stores, online platforms, and mobile applications. Size and fit data exchange platforms enable the synchronization of customer data and preferences, ensuring that accurate sizing recommendations are available regardless of the shopping channel. This omnichannel integration not only improves customer engagement but also drives higher conversion rates and repeat purchases. Furthermore, the rise of direct-to-consumer (DTC) brands and the increasing popularity of virtual fitting rooms are expected to create new opportunities for market expansion in the coming years.

In the context of the Size and Fit Data Exchange market, the emergence of Size and Fit Recommendation Software has become a pivotal development. This software leverages advanced algorithms and machine learning techniques to analyze customer data and provide precise sizing recommendations. By integrating such software, retailers can significantly enhance the accuracy of their size charts, reducing the likelihood of returns due to incorrect sizing. This not only improves customer satisfaction but also optimizes inventory management, as businesses can better predict demand for specific sizes. The adoption of Size and Fit Recommendation Software is particularly beneficial in the apparel and footwear sectors, where fit and comfort are critical purchase drivers. As more retailers recognize the value of this technology, its integration into existing systems is expected to become increasingly widespread, driving further growth in the market.

From a regional perspective, North America currently dominates the Size and Fit Data Exchange market, accounting for the largest share in 2024. This leadership position can be attributed to the early adoption of advanced retail technologies, the presence of major e-commerce players, and a highly digital-savvy consumer base. Europe follows closely, driven by a strong fashion industry and stringent regulations regarding product returns and sustainability. The Asia Pacific region is poised for the fastest growth during th

This dataset contains images (scenes) containing fashion products, which are labeled with bounding boxes and links to the corresponding products.

Metadata includes

• product IDs

• bounding boxes

Basic Statistics:

• Scenes: 47,739

• Products: 38,111

• Scene-Product Pairs: 93,274

Size and Fit Recommendation Platform Market Outlook

According to our latest research, the global size and fit recommendation platform market size stood at USD 1.42 billion in 2024. The market is expected to grow at a robust CAGR of 18.7% during the forecast period, reaching a projected value of USD 7.18 billion by 2033. This impressive growth is primarily driven by the increasing adoption of AI-driven personalization solutions in the fashion and retail sectors, coupled with the rising demand for enhanced online shopping experiences that reduce return rates and improve customer satisfaction.

One of the primary growth drivers for the size and fit recommendation platform market is the rapid expansion of the global e-commerce industry. As online shopping becomes the preferred channel for apparel, footwear, and accessories, consumers are increasingly seeking personalized and accurate size recommendations to ensure a better fit and reduce the inconvenience of returns. Retailers and brands are leveraging advanced algorithms, machine learning models, and big data analytics to offer tailored recommendations, thereby improving conversion rates and customer loyalty. The integration of these platforms not only enhances the overall shopping experience but also provides valuable insights into consumer preferences, enabling retailers to optimize their inventory and product offerings.

Another significant growth factor is the technological advancements in artificial intelligence and computer vision. These innovations have enabled size and fit recommendation platforms to analyze vast datasets, including body measurements, purchase history, and style preferences, to deliver highly accurate and individualized suggestions. The emergence of virtual fitting rooms and 3D body scanning technologies has further elevated the precision and appeal of these solutions. As a result, both established fashion brands and emerging direct-to-consumer (DTC) players are investing in these platforms to differentiate themselves in a highly competitive market. Additionally, the increasing penetration of smartphones and high-speed internet has made it easier for consumers to access and utilize these recommendation tools, further fueling market growth.

The growing emphasis on sustainability and cost efficiency is also contributing to the market's expansion. By reducing the volume of product returns, size and fit recommendation platforms help retailers minimize logistical expenses and environmental impact associated with reverse logistics. This aligns with the broader industry trend towards eco-friendly and responsible business practices. Moreover, as consumer expectations for seamless and personalized shopping experiences continue to rise, retailers are compelled to adopt innovative solutions to stay relevant. The convergence of these factors is expected to drive sustained demand for size and fit recommendation platforms over the coming years.

From a regional perspective, North America currently dominates the size and fit recommendation platform market, followed closely by Europe and Asia Pacific. The high adoption rate of advanced retail technologies, presence of major fashion brands, and the maturity of the e-commerce ecosystem in these regions are key contributors to their market leadership. Meanwhile, the Asia Pacific region is anticipated to witness the fastest growth during the forecast period, driven by the rapid digital transformation of the retail sector, rising disposable incomes, and an expanding base of tech-savvy consumers. Latin America and the Middle East & Africa are also expected to experience steady growth as retailers in these regions increasingly embrace digital solutions to enhance customer engagement and operational efficiency.

Component Analysis

The size and fit recommendation platform market is segmented by component into software and services. The software segment constitutes the core of this market, encompassing AI-powered engines, data analytics modules, and user interface solutions that deliver personalized size and fit suggestions. These software platforms are designed to integrate seamlessly with e-commerce websites and mobile applications, leveraging advanced algorithms to process user data and generate accurate recommendations. The increasing demand for real-time, automated, and scalable solutions has led to continuous innovation in software functionalities, including the integration of 3D visualization, virtual fitting rooms,

Fit Analytics Sizing Market Outlook

According to our latest research, the Global Fit Analytics Sizing market size was valued at $1.2 billion in 2024 and is projected to reach $5.8 billion by 2033, expanding at a robust CAGR of 19.3% during the forecast period from 2025 to 2033. The primary driver fueling this substantial growth is the rapid digital transformation of the retail and e-commerce sectors, where brands and platforms are increasingly leveraging advanced fit analytics solutions to reduce product returns, enhance customer satisfaction, and optimize inventory management. As consumers demand more personalized and accurate online shopping experiences, the adoption of AI-driven sizing tools, virtual fitting rooms, and body measurement scanners is accelerating, setting the stage for dynamic market expansion across diverse industry verticals.

Regional Outlook

North America currently dominates the Fit Analytics Sizing market, capturing the largest market share globally. The region’s leadership is attributed to its mature e-commerce ecosystem, advanced technological infrastructure, and early adoption of AI and machine learning for retail personalization. Major apparel and footwear brands headquartered in the United States and Canada have significantly invested in fit analytics solutions to address high return rates and improve customer loyalty. Additionally, supportive regulatory frameworks and a strong culture of innovation have enabled rapid integration of virtual fitting rooms and body measurement scanners into both online and brick-and-mortar retail environments. North America’s market value is estimated at $420 million in 2024, with sustained growth projected as retailers continue to prioritize omnichannel strategies and seamless customer engagement.

Asia Pacific is set to emerge as the fastest-growing region in the Fit Analytics Sizing market, forecasted to register a remarkable CAGR of 24.1% through 2033. This rapid expansion is driven by the explosive growth of e-commerce, especially in China, India, Japan, and Southeast Asian countries, where digital shopping adoption is surging. Local and international brands are investing heavily in fit analytics technologies to cater to a diverse and increasingly fashion-conscious consumer base. Government initiatives supporting digital transformation, coupled with rising disposable incomes and smartphone penetration, are further catalyzing market growth. Strategic partnerships between technology providers and regional e-commerce giants are also fueling innovation and accelerating the rollout of advanced sizing solutions across the Asia Pacific region.

Emerging economies in Latin America, the Middle East, and Africa are witnessing gradual adoption of Fit Analytics Sizing solutions, though the pace is moderated by infrastructural and economic challenges. In these regions, localized demand is influenced by increasing internet access, urbanization, and the entry of global e-commerce players seeking to tap into underserved markets. However, barriers such as limited digital literacy, inconsistent regulatory standards, and price sensitivity among consumers present hurdles to widespread adoption. Nonetheless, tailored fit analytics offerings and collaborative efforts with local retailers are beginning to bridge the gap, creating new growth opportunities and paving the way for future market penetration as digital ecosystems mature.

Report Scope

This dataset was AI-generated to simulate personalized clothing and color recommendations based on individual features like skin tone, undertone, body proportion, and hair/eye color. It contains over 15,000 entries covering detailed fashion and style insights.

Despite growing interest in AI-based styling assistants, there are no open datasets that combine features like skin tone, undertone, body proportions, and fashion recommendations based on color theory. Most fashion datasets focus on clothing images or product metadata, not personal styling guidance.

To address this gap, this dataset was generated using AI models trained on fashion theory, color harmony rules, and body proportion guidelines to simulate expert stylist recommendations.

Purpose: Useful for training AI-based fashion recommendation systems, personal styling apps, and color theory analysis.

Dataset Structure:

💇 Hair Color, Eye Color, Skin Tone, Under Tone: Key appearance features

🧍 Body Proportion, Torso Length: Body structure information

🎨 Recommended Clothing Colors, Avoid Clothing Colors: AI-based color theory output

👗 Recommended Materials, Fitting Style, Patterns: Style suggestions

💎 Jewelry Metal, Shoes, Fabric Nature: Accessory and fabric guidance

Potential Use Cases:

• Training ML models for style prediction

• Building personalized outfit suggestion engines

• Studying human color harmony and undertone matching

Size and Fit Data Exchange Market Outlook

According to our latest research, the global Size and Fit Data Exchange market size reached USD 1.42 billion in 2024, reflecting a robust ecosystem driven by the increasing digitization of retail and e-commerce channels. The sector is expected to grow at a CAGR of 18.3% from 2025 to 2033, projecting a market value of USD 6.65 billion by 2033. This impressive growth trajectory is underpinned by the accelerating adoption of advanced data analytics and AI-driven personalization in the fashion and retail industries, which is rapidly transforming how brands, retailers, and consumers interact and make purchasing decisions.

One of the primary growth factors for the Size and Fit Data Exchange market is the increasing demand for personalized shopping experiences. Consumers are seeking products that not only match their style preferences but also offer a perfect fit, reducing the likelihood of returns and enhancing overall satisfaction. Retailers and brands, in response, are integrating sophisticated size and fit recommendation engines powered by AI and machine learning algorithms. These technologies leverage vast datasets, including customer measurements, purchase histories, and product specifications, to provide highly accurate fit suggestions. As a result, businesses can significantly lower return rates, optimize inventory management, and foster stronger customer loyalty, thereby driving market expansion.

Another significant driver is the surge in e-commerce activity, particularly in the apparel and footwear segments. The global shift towards online shopping, accelerated by the COVID-19 pandemic, has intensified the need for effective size and fit solutions. Consumers are often hesitant to purchase clothing or footwear online due to concerns about sizing discrepancies across brands and regions. The adoption of Size and Fit Data Exchange platforms addresses this challenge by enabling seamless sharing of standardized sizing data among retailers, brands, and third-party platforms. This not only enhances the online shopping experience but also supports omnichannel retail strategies, positioning the market for sustained growth over the forecast period.

The proliferation of wearable technology and smart devices is also playing a pivotal role in shaping the Size and Fit Data Exchange market. Advanced hardware, such as 3D body scanners and mobile measurement tools, is enabling consumers to capture accurate body dimensions in real-time. These devices integrate with software platforms to create dynamic, user-centric profiles that can be shared across multiple retail touchpoints. The convergence of hardware innovation and data analytics is fostering an ecosystem where precision, convenience, and personalization converge, further catalyzing market growth and innovation.

From a regional perspective, North America currently leads the Size and Fit Data Exchange market, accounting for the largest share in 2024, driven by early technology adoption, a mature e-commerce landscape, and strong investments in digital retail infrastructure. Europe follows closely, characterized by a high concentration of fashion-forward consumers and a robust ecosystem of luxury and mass-market brands. The Asia Pacific region, however, is expected to witness the fastest CAGR during the forecast period, fueled by rapid urbanization, rising disposable incomes, and the exponential growth of online retail channels in countries such as China, India, and Japan. These regional dynamics highlight the global nature of the market and the diverse opportunities for stakeholders across geographies.

Component Analysis

The Component segment of the Size and Fit Data Exchange market encompasses Software, Hardware, and Services, each playing a distinct role in the ecosystem. Software solutions form the backbone of this market, providing the algorithms, analytics engines, and integration platforms that enable accurate size and fit recommendations. These platforms leverage a combination of artificial intelligence, machine learning, and big data analytics to process vast amounts of user and product data in real-time. The continuous evolution of software capabilities, including cloud-based delivery and API integrations, is making it easier for retailers and brands to deploy and scale fit solutions across multiple channels, thereby enhancing their operational efficiency and customer engagement.

Hardware components, such as 3D b

The virtual fitting room market for e-commerce is experiencing robust growth, driven by the increasing adoption of online shopping and the desire for enhanced customer experience. Consumers are demanding more convenient and accurate ways to try on clothes before purchasing, leading to a surge in demand for virtual fitting room technologies. This market is projected to witness significant expansion over the next decade, fueled by technological advancements in 3D body scanning, augmented reality (AR), and artificial intelligence (AI). The integration of these technologies allows for more realistic simulations of clothing on different body types, improving the overall shopping experience and reducing return rates for online retailers. Key players in this space are continuously innovating to offer more accurate and user-friendly virtual fitting solutions, including features like personalized recommendations and virtual styling advice. The market is segmented by technology type (AR, VR, 3D scanning), application (apparel, accessories, footwear), and deployment mode (web-based, mobile app). While challenges remain, such as the accuracy of body measurements and the need for high-quality 3D models, the overall market outlook is very positive, with continuous investment in R&D driving further advancements and wider adoption. The growth trajectory of the virtual fitting room market is further supported by the increasing penetration of smartphones and high-speed internet, facilitating seamless access to virtual try-on applications. Furthermore, the rising popularity of omnichannel retailing is creating new opportunities for virtual fitting rooms to bridge the gap between online and offline shopping experiences. Competitive rivalry among established players and new entrants is intensifying, leading to continuous innovation and price competition. However, factors like high initial investment costs for businesses and the potential for technical glitches could hinder market growth to some extent. Nevertheless, the long-term prospects for the virtual fitting room market remain exceptionally promising, with widespread adoption expected across various e-commerce sectors and geographical regions. We project a considerable market expansion, with substantial growth expected in key regions such as North America and Europe, followed by a strong increase in Asia-Pacific and other emerging markets.

Snapshot img

AI In Fashion Market Size 2025-2029

The AI in fashion market size is valued to increase USD 10.81 billion, at a CAGR of 36.9% from 2024 to 2029. Imperative for hyper-personalization and enhanced customer experience will drive the AI in fashion market.

Major Market Trends & Insights

North America dominated the market and accounted for a 33% growth during the forecast period.
By Type - Apparels segment was valued at USD 73.90 billion in 2023
By Deployment - Cloud segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 6.00 million
Market Future Opportunities: USD 10807.70 million
CAGR from 2024 to 2029 : 36.9%

Market Summary

In the realm of business innovation, the market has emerged as a significant disruptor, revolutionizing the way apparel is designed, produced, and marketed. According to recent market intelligence, the global fashion tech industry is projected to reach a value of USD300 billion by 2025, underscoring its immense potential. AI's impact on the fashion sector is multifaceted. It enhances the customer experience by enabling hyper-personalization, tailoring recommendations based on individual preferences and purchasing histories. Moreover, generative AI is transforming creative design, producing innovative patterns and textures that challenge traditional fashion norms. However, the adoption of AI in fashion faces challenges.
Prohibitive implementation costs and complex systems integration hinder widespread adoption. Yet, the benefits far outweigh the obstacles. By automating repetitive tasks and streamlining production processes, AI contributes to increased efficiency and cost savings. The future of AI in fashion is bright, with endless possibilities for innovation. From virtual fitting rooms to AI-generated marketing campaigns, the potential applications are vast. As this technology continues to evolve, it will redefine the fashion industry, offering unprecedented opportunities for businesses to stay ahead of the curve.

What will be the Size of the AI In Fashion Market during the forecast period?

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How is the AI In Fashion Market Segmented ?

The AI in fashion industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Apparels
  Footwear
  Accessories
  Beauty and cosmetics
  Jewelry


Deployment

  Cloud
  On premise


Application

  Product recommendation
  Supply chain management and forecasting
  Product search and discovery
  Creative designing and trend forecasting
  Customer relationship management


End-user

  Fashion stores
  Fashion designers


Geography

  North America

    US


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The apparels segment is estimated to witness significant growth during the forecast period.

The fashion industry's apparel segment is witnessing a revolutionary transformation through the integration of artificial intelligence (AI). From haute couture to fast fashion, AI is reshaping every aspect of clothing production, addressing long-standing challenges of inefficiency, waste, and disconnected consumer demand. Driven by the pursuit of hyper-personalization, operational agility, and sustainability, AI applications span various stages of the product lifecycle. In the realm of design and trend forecasting, AI algorithms analyze vast datasets, including social media imagery, runway shows, street style photography, and real-time sales data, to predict emerging trends and their trajectory. AI-driven inventory management systems optimize stock levels based on predictive analytics, reducing excess inventory by 30%.

AI-based sizing systems ensure accurate measurements, minimizing returns. AI-powered clothing customization offers personalized fit recommendations, while 3D garment simulations enable virtual try-on experiences. Style transfer algorithms and AI-driven textile design create unique, innovative fabrics. AI-enhanced pattern grading and AI-powered customer segmentation cater to diverse consumer needs. Ethical fashion AI ensures sustainable production processes, while AI-driven fabric selection and smart textile integration promote eco-friendly materials. AI-powered design tools, sales forecasting, and trend forecasting streamline operations and boost profitability. Virtual fashion models and predictive analytics fashion further enhance the consumer experience. The future of fashion is AI-driven, offering endless possibilities for innovation and growth.

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The Apparels segment was

Here are a few use cases for this project:

1. E-commerce Personalization: Retailers could use the "clothes1" model to personalize customer shopping experiences. Analyzing the uploaded customer photos or selfies, the model can make tailored suggestions based on the clothes they usually wear.

2. Fashion Trend Analysis: The model can be used to scan social media platforms/ websites to identify trending clothing styles and patterns. This can guide fashion designers or retailers in their product development and inventory planning.

3. Virtual Try-On Apps: "Clothes1" could enable virtual try-on applications to identify the type of clothing a customer wants to try on. This can help online shoppers figure out how different clothes might fit or look on them.

4. Loss Prevention in Retail: The model can be employed to monitor surveillance footage in real-time to detect potential shoplifting activities. A sudden change or removal of a clothing item from its original position could flag suspicious behavior.

5. Automated Sorting in Warehouses: In an e-commerce warehouse setup, "clothes1" can be used to sort clothes automatically based on their categories, increasing efficiency of the entire sorting process.

Explore the burgeoning virtual fitting tools market, driven by e-commerce growth and AI innovation. Discover market size, CAGR, key drivers, trends, restraints, and regional insights from 2019-2033.

According to cognitive market research, The plus size clothing market is driven largely by shifting societal attitudes towards body positivity and inclusivity. Market Dynamics of Plus Size Clothing Market

Key Drivers of Plus Size Clothing Market

Rising Body Positivity and Demand for Size-Inclusive Fashion

One of the primary drivers of the plus-size clothing market is the global shift toward body positivity and self-acceptance. Movements advocating for inclusive beauty standards have gained significant traction, especially among younger demographics. A 2023 consumer study by Fashion Retail Analytics revealed that over 70% of Gen Z and Millennial shoppers prefer brands that promote body diversity and inclusivity, influencing their purchasing decisions. This cultural shift has pressured fashion brands to reevaluate their offerings, not just in terms of sizing but also in representation and marketing.

(Source:https://www.indianretailer.com/article/d2c-new-commerce/fashion-accessories/how-d2c-activewear-brands-are-redefining-fitness)

Furthermore, the increased visibility of plus-size influencers and models on social media platforms like Instagram and TikTok has amplified demand for stylish, well-fitting clothing options. According to a 2024 report by Global Fashion Insights, 62% of plus-size consumers reported dissatisfaction with the lack of fashionable choices available to them, despite expressing a high intent to purchase if better options existed.

(Source:https://www.mckinsey.com/industries/retail/our-insights/state-of-fashion-2024)

Opportunities in Plus Size Clothing Market

Untapped Demand in Emerging Markets and E-Commerce Expansion

While much of the focus has been on Western markets, there is a significant untapped opportunity for plus-size clothing in emerging economies. As urbanization increases and income levels rise in regions like Asia-Pacific, Latin America, and the Middle East, so does the demand for fashionable and inclusive clothing. Yet, nearly 60% of plus-size consumers in these regions report limited access to well-fitting apparel, according to a 2024 survey by Emerging Market Fashion Outlook. Local and global brands that proactively expand their size ranges and marketing strategies to cater to these underserved populations stand to gain a competitive edge.

(Source:https://foundryjournal.net/wp-content/uploads/2025/03/13.FJ25C587.pdf)

Additionally, the rise of e-commerce presents a powerful channel for reaching the plus-size demographic. Online platforms allow for broader size ranges, personalized recommendations, and customer feedback integration something traditional retail has struggled to offer consistently. A study by Digital Fashion Insights in 2023 showed that over 68% of plus-size consumers prefer shopping online due to better variety and fit information, making digital-first strategies a critical opportunity area for both startups and legacy brands looking to grow in this segment.

(Source:https://www.capgemini.com/wp-content/uploads/2025/01/Consumer-trends14012025.pdf)

AI in Plus-Size Clothing Market

Artificial Intelligence (AI) is playing an increasingly transformative role in the plus-size clothing market by enhancing product development, personalization, and customer experience. AI-powered fit prediction tools and virtual try-on technologies are helping brands address one of the industry's biggest challenges: sizing accuracy. A 2024 retail tech survey found that 58% of plus-size consumers abandon online purchases due to uncertainty about fit, a gap AI is now helping to close.

Additionally, AI-driven data analytics are enabling brands to better understand consumer preferences by analyzing purchase behavior, reviews, and social media feedback leading to more inclusive and data-informed design decisions. AI is also being used in inventory planning, reducing overproduction in less popular sizes and ensuring better availability in high-demand plus sizes. As the market continues to evolve, AI offers a powerful tool for brands aiming to deliver more personalized, size-inclusive, and efficient shopping experiences.

(Source:https://shaku.tech/blogs/the-role-of-ai-in-size-inclusivity-revolutionizing-fashion-for-all-bodies) Introduction of Plus Size Clothing Market

The plus-size clothing market has seen a significant transformation in recent years, fueled by growing body positivity movements, changing consumer attitudes...

These datasets contain attributes about products sold on ModCloth and Amazon which may be sources of bias in recommendations (in particular, attributes about how the products are marketed). Data also includes user/item interactions for recommendation.

Metadata includes

• ratings

• product images

• user identities

• item sizes, user genders