Machine Learning Powered Fashion Data Annotation (Landmark detection in Fashion - Virtual styling)

Описание: 47% of return, refund, and exchanges occur because the product does not fulfill the customer’s expectations.

A Global web index report highlights that 56% of clothing/ shoes sold online were returned just in the year 2019. The increased rate of returns not only concerns the retailer but increases the load on the customer side as well. 33% of online retailers admit that they offer free returns but offset the cost of returns by charging for delivery and 20% said they would increase the cost of the product to cover the return expenses.

Computer vision AI-driven fashion landmark technology is one of the ways. Fashion landmark detection aims to predict the positions of functional key points defined on clothing, such as the corners of the neckline, the hemline, and the cuff.

Not only do these landmarks indicate the functional regions of clothing, but they also implicitly capture their bounding boxes, forming the design, pattern, and category of clothing.

Scale variations and non-rigid deformations in clothing lead to increased challenges in more complex patterns than restricted deformations, makes landmark detection even more data-hungry to satisfy algorithms and this differentiates it from known pose estimation.

Now you wonder, the solution is good but how to access good quality  labeled data for training the model.

Here’s your solution - http://www.labellerr.com

Wait are you wondering how to get this integrated within a few weeks into your production system without investing in building an AI team!

Visit our website at http://www.labellerr.com and mention your use case in brief and our customer engineer will contact you and help you prepare the plan and get you running on a trial with us to validate.

Mail us at [email protected] for more info.