The fashion industry has experienced dramatic changes during the pandemic. Economic turmoil and changes in shopping practices have led to significant drops in sales. Governments across the globe put in place restrictions that reduced and even banned in-store shopping, this put many brick and mortar stores under immense pressure.
As a result, retailers responded by shifting to online fashion sales, which experienced enormous growth. Many online fashion brands reported large rises in online sales for the first half of the 2020. As the global economy rebounds from COVID-19 retailers are looking to reinforce the gains made in online shopping.
With this growing demand retailers are looking at ways to improve and streamline the online clothes shopping experience. One way to achieve this is through AI powered virtual fitting rooms. These applications make it easy for customers to shop and try on clothes online. The right image annotation tool can help virtual fitting room developers to give shoppers the best possible experience online.
Firstly, this blog will look at what makes virtual fitting rooms so important for the future of online shopping. Secondly, we will look at the data annotation techniques that make virtual fitting rooms possible. And finally, will show how the right annotation tool can help developers create powerful training datasets for computer vision models.
An online fitting experience
Virtual fitting rooms allow consumers to see what any item of clothing will look like on their body. This can help make online shopping less of a gamble and improve customer satisfaction after all. In order to create a satisfying virtual fitting experience developers need to overcome the following challenges:
- Virtual fitting rooms need to be capable of accurately mapping the body shape of every shopper. In order to work for everyone, virtual fitting models need to understand diverse body shapes.
- AI models for virtual fitting rooms also need to recognize thousands of different clothing items and accessories.
- These two capabilities need to be combined so that any piece of clothing or accessory can be accurately modeled on any individual body shape.
Annotation techniques for virtual fitting rooms
Virtual fitting rooms applications need accurate training data so that they can provide the best service for customers. Developers combine annotation techniques to create labeled images and video that allow AI models to recognize the shape of the human body and every clothing and accessory item:
- Semantic Segmentation: AI models must be able to identify and distinguish between different clothing types as well as non clothing objects in images that contain multiple clothing items. Semantic segmentation is the process of assigning each pixel in an image to a class e.g. t-shirt, coat, dress. Semantic segmentation creates a simplified version of an image that helps computer vision models to learn.
- Instance Segmentation: This annotation method adds an extra level of detail for training images. Annotators label each instance of each object appearing in an image, for example, each individual shoe or individual sweaters worn by two people in an image.
- Polygon annotation: Training images for virtual fitting rooms need to precisely understand the shape of clothing items. Polygon annotation allows annotators to plot points around an object and connect them with vertices. This type annotation produces labeled items that adhere closely to their real world shape.
Annotation tools streamline data labeling
Keylabs is designed to accelerate annotation and improve labeling accuracy. Unique data analytics capabilities mean the virtual fitting room AI developers can give tasks to the highest performing annotators. Keylabs is also flexible, allowing any type of pre-annotation to be easily integrated in projects.
Keylabs also speeds up annotation with efficient quick outlining functions. The right annotation tool can transform AI projects in the fashion sector. Keylabs is the right choice for virtual fitting room innovators.