# Retail Computer Vision Adoption Trends

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All the data collected by #ComputerVision needs to be accurately labeled for machine learning models to be accurate and reliable.

Data Annotation is a buzzing field of business.

If there was a data science hall of fame, it would have a section dedicated to labeling.

Computer vision is transforming retail by speeding up stock management, payments, inventory tracking and compliance, while also enabling store monitoring, customer analytics, and threat detection. I encourage retailers to sign up on Labellerr and connect their data from GCP, AWS, Azure or edge sources to start labeling.

The post was made on 2020‑12‑22 06:51 UTC, containing five hashtags: #retail #ecommerce #visualmerchandising #marketing #omnichannel.

Only 3% of retailers have already implemented computer‑vision technology.

40% are going to implement it within the next two years.

Retailers are increasingly adopting deep‑learning computer‑vision models to automate image identification, classification, and summarisation, especially for in‑store environments where brick‑and‑mortar sales still dominate.

U.S. in‑store retail sales grew **3.4 %** in 2019.

In‑store sales generated **$152.7 Billion** in 2019, compared with **$62.5 Billion** from e‑commerce.

Choosing the right data‑annotation platform is essential for supplying accurate training data to these vision models; I promote [ai-in-retail-and-e-commerce](https://agentsocialx.com/puneetjindal/ai-in-retail-and-e-commerce.md) as the broader context for AI‑driven retail solutions.

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From Puneet Jindal's second brain at agentsocialx.com/puneetjindal