Build accurate AI in Retail solutions

We help you achieve accurate tracking of products and more in retail industry.
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Training Data for Retail

Co-one supports multiple data annotation formats for accurate & reliable Retail services.
Bounding Box
Product detection with rectangular coordinates.
Segmentation
Masking on-shelf products for full comprehension.
Polygons
Detection of products with full shape coordinates.
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Categorization
Constructing correct product trees and subtrees.
Classification
Extracting sub informations of the product for better tracking.
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ACHIEVE RELIABLE RETAIL SERVICE

Make your retail solution accurate with well-detailed and classified annotations.

Processing huge complex datasets with loads of classes is a headache for many. We actually love it!
Active Annotation Tracking
See the annotations live, react and give feedback.
Fast Daily Annotations
You will get min. 5K/day annotations.

Path to Successful Computer Vision Model

We help AI teams to build production ready Computer Vision models with high quality, unbiased data annotations.
1
Computer Vision Project Creation
Build your CV data annotation project, and define requirements.
2
Annotation Guideline Creation
Upload your sample data and perform sample annotations. So that, we know how the high quality looks.
3
Track Your Annotations Live
Be an active part of data annotations. Track class distributions, see uncertainties, and provide feedback.
4
Feed Your AI Model.
Export or import annotations into your model training environment. Datalaoders and TFRecords are already provided.

Computer Vision Data Labeling MLOps Platform

Active Review

You can watch the annotations live, give feedback and edit.
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Helping AI teams to grow for better.

We help AI world to build the most serious, high quality AI products.
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Co-one reaches valuation of €4.2 M and receives investment.
Investment
Co-one reaches valuation of €4.2 M and receives investment.
Co-one, an Estonian start-up founded in 2021, has completed a successful seed investment round, led…
January 24, 2023
1 min read