Retail

High-quality data labeling for retail AI training - from monitoring shopping habits to product placement, habit assessment, and much more.

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Expert Labeling
of Retail Data

Retail Data Annotation services for a wide range of computer vision applications.

Keymakr is an expert in providing annotations and creating training datasets for retail AI applications.

We specialize in custom-made training datasets for machine learning models and computer vision AI. We create actionable datasets from any source. AI in retail has the potential to revolutionize the shopping experience by reducing workloads for staff, aiding loss prevention, and improving the customer experience.

Our experienced managers and innovative annotation platform can ensure your machine-learning solutions reach their full potential.

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Retail
Annotation Types

01. Automatic Annotation

Fast AI-assisted labeling for your retail data - our team will validate every single image or video frame for quality control.

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Automatic Annotation

02. Bounding Box

Label individual objects such as people or products with the help of bounding boxes that help AI recognize them.

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03. Oriented Bounding Box

More accurate labeling of retail objects to also capture their orientation - useful for transitioning between frames.

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Oriented Bounding Box

04. Cuboid

Add an extra dimension to your object, for example, extrapolating the volume of a shopping cart or another container.

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05. Polygon

Label irregular shapes such as items of different sizes, non-standard products, specific labels on containers, and so on.

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06. Semantic Segmentation

Classifies all objects in a scene, including backgrounds, into separate categories for more accurate AI processing.

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Semantic Segmentation

07. Instance Segmentation

More granular segmentation where each repeated object gets a separate label for datasets where detail matters.

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Instance Segmentation

08. Skeletal

Usually helps mark humans in your retail image - helps us track movement, exact position at the store, and so on.

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09. Key Points

Granular labeling used for fine details such as emotion detection, often helps extrapolate buying intent based on complex algorithms.

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10. Lane

Label lanes such as isles in your store, parking spots, and so on for AI to understand the infrastructure of your retail property.

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11. 3D Point Cloud

Simulate entire 3D environments for planning entire routes to better train cleaning and loading robots how to navigate your stores.

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12. Custom

Combine different data labeling techniques to create accurate datasets for training your AI in specific ways.

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12

Need
custom solution

Professional Data Annotation
for Retail

Get accurately labeled data for your retail AI systems from seasoned annotation teams!

01. Why Retail Companies Choose Keymakr

Retail Companies

We create bespoke annotation types based on your specific requirements. Our proprietary annotation platform features a full range of annotation techniques, so we can produce incredibly accurate data for your training.

Retail annotations are performed by our in-house employees - trained professional annotators.
Datasets go through 4 layers of quality control to ensure the highest-quality results.
All retail labeling can have different precision levels up to “pixel-perfect.”

02. Loss Prevention - Smart Checkouts

Loss Prevention

Smart checkouts are an important part of the robotization of stores. They help retailers minimize theft by monitoring checkouts in real time to provide security staff with crucial information.

Accurately monitoring each item passing through the checkout requires pixel-perfect image annotation in training data sets - that’s where we come in!

03. Real-time Monitoring of Store Performance

Real-time Monitoring of Store Performance

Receive real-time situational alerts about what’s happening on the shelf, like missing products or empty spaces. Instance segmentation annotation powers this technology by allowing AIs to distinguish between individual items:

Identifying low shelf share in the store.
Inventory management.
Planogram improvements.
Expiration date monitoring.

04. In-Store Traffic and Sentiment Analysis

In-Store Traffic

This promising use case of machine learning in retail is made possible by combining key point labeling with skeletal annotation to predict purchase intent.

Analyze facial expressions to understand customer sentiment towards your promotions and products on shelves.
Map shoppers’ paths around the store to optimize the placement of products and promotions.
Capture rate of pass-by traffic to measure how many shoppers walked through the store, and discover which promotions capture engagement.

05. Facial Recognition

Use state-of-the-art technology to improve your retail performance:

Identify regular customers, and reward loyal customers.
Remember customer’s purchasing history and preferences, and make personalized product recommendations.
Provide your customers with targeted promotions designed to catch their attention.

Reviews
on

high perfomer

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leader

"Delivering Quality and Excellence"

The upside of working with Keymakr is their strategy to annotations. You are given a sample of work to correct before they begin on the big batches. This saves all parties time and...

"Great service, fair price"

bility to accommodate different and not consistent workflows.
Ability to scale up as well as scale down.
All the data was in the custom format that...

"Awesome Labeling for ML"

I have worked with Keymakr for about 2 years on several segmentation tasks.
They always provide excellent edge alignment, consistency, and speed...

Meet
Keylabs

We created a proprietary annotation platform with enterprise-grade tools capable of achieving pixel-perfect labeling for all your AI training datasets.

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Keylabs can help you handle annotation projects of any complexity. Our platform was originally created for internal use, so usability and quality are main priorities. Keylabs features a full suite of annotation techniques that can be adapted for your specific needs.

You can work with projects of any size and label vast quantities of data. The in-built management tools will help you collaborate on projects and get the most out of your data.

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