Fashion

Fashion

Solution for recognizing clothes and accessories in images

Hat: 91.89%, Outwear: 82.37%, Scarf: 79.35%, Top, t-shirt: 57.23%

About

This Fashion algorithm analyzes a picture and gives a list of possible clothing items and accessories that could be present on that picture.

Apparel recognition

The algorithm gives its suggestions for the most probable clothes and accessories that could potentially be on that picture.

Confidence level

This solution provides its confidence level for each suggested clothing item or accessory to allow for better customization.

Demo

Try this solution out right now! Select one of the pictures below or upload your own image.

Clothing and accessorices found on this image:

Benefits

Wine Recognition from CVisionLab is born from the best machine learning practices, thorough testing and mature integration processes.

Off-the-shelf
solution

The service is developed, tested and ready to go.

Cloud
service

This is a completely cloud-based solution which provides high reliability and uptime.

Wide
applicability.

The solution fits businesses from a large variety of industries

Robust
algorithm

Resilient to lighting conditions, rotation, small distortions.

 

 


 

Use cases

This Fashion solution, being an apparel recognition algorithm, fits a large number of businesses. From social to the corporate sector, this algorithm quickly and effectively gathers and provides data about people’s clothing and accessories in a simple response for each image.

Clothing stocktaking

Quickly and easily account for the clothing items in stock by putting them through an automatic apparel recognition system based on this Fashion solution.

Dress code verification

Make sure that your employees and visitors comply with the dress code rules by analyzing each person’s outfit in a quick and discrete manner.

Demand statistics

Gather information about geographic, seasonal, social status, and other correspondences with the people's clothing by using the apparel recognition algorithm.

 

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