There are indications that artificial intelligence has penetrated into the fashion market. The data shows that the global market size of artificial intelligence in the fashion market is expected to grow from US$228 million in 2019 to US$1.26 billion in 2024, and will climb rapidly at a compound annual growth rate of 40.8% between 2019 and 2024.

In the past, artificial intelligence was mainly used in e-commerce platforms of fashion brands, responsible for intelligent customer service, or providing simple personalized product recommendations to shoppers. However, as the needs of consumers for personalized experience become more and more diverse, and the needs of enterprises for inventory management are also becoming stronger and stronger. Today, artificial intelligence is also playing a more complex and sophisticated role in the fashion industry.

In terms of shopping experience, last year the fashion retail platform Farfetch demonstrated an artificial intelligence technology platform called “Future Store”. The information and data of all purchase behaviors, through in-depth insight into customer needs, ultimately promote a super-personalized consumption experience; in terms of customer management, Mytheresa, a well-known German luxury e-commerce platform, uses artificial intelligence to predict fashion trends and cultivate customer loyalty; In terms of inventory sales, LVMH just announced a five-year strategic cooperation with Google last year to jointly develop artificial intelligence-powered business solutions to improve business operations in demand forecasting and inventory optimization.

Artificial intelligence has gradually upgraded the fashion industry to “smart fashion”, and the subdivision of smart fashion is very broad, including clothing image recognition, classification, retrieval, feature extraction, virtual fitting, fashion image synthesis, recommendation system, popular trend analysis and prediction, production quality inspection and other fields.

As the world’s leading AI data services, Nexdata provides one-stop data solutions for fashion domain. We have off-the-shelf datasets including 2D/3D face landmarks, facial expressions, gesture, body keypoint, and etc. All the datasets are collected with proper authorization agreement.

144,810 Images Multi-class Fashion Item Detection Data

144,810 Images Multi-class Fashion Item Detection Data. In this dataset, 19,968 images of male and 124,842 images of female were included. The Fashion Items were divided into 4 parts based on the season (spring, autumn, summer and winter). In terms of annotation, rectangular bounding boxes were adopted to annotate fashion items. The data can be used for tasks such as fashion items detection, fashion recommendation and other tasks.

1,507 People 102,476 Images Multi-pose and Multi-expression Face Data

1,507 People 102,476 Images Multi-pose and Multi-expression Face Data. The data includes 1,507 Asians (762 males, 745 females). For each subject, 62 multi-pose face images and 6 multi-expression face images were collected. The data diversity includes multiple angles, multiple poses and multple light conditions image data from all ages. This data can be used for tasks such as face recognition and facial expression recognition.

314,178 Images 18_Gestures Recognition Data

314,178 Images 18_Gestures Recognition Data. This data diversity includes multiple scenes, 18 gestures, 5 shooting angels, multiple ages and multiple light conditions. For annotation, gesture 21 landmarks (each landmark includes the attribute of visible and visible), gesture type and gesture attributes were annotated. This data can be used for tasks such as gesture recognition and human-machine interaction.

602 People –3,010 Images Multi-Races Human Body Semantic Segmentation Data

602 People –3,010 Images Multi-Races Human Body Semantic Segmentation Data,The data diversity includes headphones, body, background,and glasses.In terms of annotation, we adpoted segmentation annotations on headphones, body, background and glasses.The data can be used for tasks such as human body segmentation and the behavior detection of Video conference.

5,199 People — 3D Face Recognition Images Data

5,199 People — 3D Face Recognition Images Data. The collection scene is indoor scene. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes multiple facial postures, multiple light conditions, multiple indoor scenes. This data can be used for tasks such as 3D face recognition.

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If you want to know more details about the datasets or how to acquire, please feel free to contact us: info@nexdata.ai.