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Retail

Product classification and images annotation helped to improve grocery store efficiency by 40%

Industry and use case:

Retail, store shelves monitoring

Data:

100,000 shelves images with products

Project duration:

4 months
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Challenge:

The customer wanted to automate the process of grocery store shelf monitoring, including automatic product identification using a neural network. The goal was to improve the merchandising strategies and analyze the impact of promotion campaigns on sales in real-time. The main challenge for data labelling and classification resulted from a wide range of product categories, types, and packages

Solution:

The Training Data annotators were split into two teams. One group prepared product requirements by looking for product examples in each product category. The second group used the requirements to label actual shelves images. This approach allowed the team to reach high accuracy of product classification

Outcomes:

  • Up to 40% in cost savings
  • Accurate real-time data about product merchandising and sales
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Our goal was to pass the autonomous robot tests successfully. Thanks to the cohesive and independent work of the Training Data teams, we trained the robot on an excellent dataset and passed field trial.
Ivan N.
Project Manager

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Stages of work

  • Application

    /01
    Leave a request on the website for a free consultation with an expert. Th e acco unt manager will guide you on the services, timelines, and price

  • Free pilot

    /02
    We will conduct a test pilot project for you and provide a golden set, based on which we will determine the final technical requirements and approve project metrics

  • Agreement

    /03
    We prepare a contract and all necessary documentation upon the request of your accountants and lawyers

  • Workflow customization

    /04
    We form a pool of suitable tools and assign an experienced manager who will be in touch with you regarding all project details

  • Quality control

    /05
    Data uploads for verification are done iteratively, allowing your team to review and approve collected/annotated data

  • Post-payment

    /06
    You pay for the work after receiving the data in agreed quality and quantity

Timeline

  • 24 hours
    Application
  • 24 hours
    Consultation
  • 1 to 3 days
    Pilot
  • 1 to 5 days
    Conducting a pilot
  • 1 day to several years
    Carrying out work on the project
  • 1 to 5 days
    Quality control
You pay for the work after you have received the data
in the established quality and quantity

Why
Training Data

  • Quality Assurance:
  • Enhanced Data Accuracy
  • Consistency in Labels
  • Reliable Ground Truth
  • Mitigation of Annotation Biases
  • Cost and Time Efficiency
  • Data Security and Confidentiality:
  • GDPR Compliance
  • Non-disclosure agreement
  • Data Encryption
  • Multiple data storage options
  • Access Controls and Authentication
  • Expert Team:
  • 6 years in industry
  • 35 top project managers
  • 40+ languages
  • 100+ countries
  • 250k+ assessors
  • Flexible and Scalable Solutions:
  • 24/7 availability of customer service
  • 100% post payment
  • $550 minimum check
  • Variable Workload
  • Customized Solutions
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    Select an option

    • Data labeling

    • Data collection

    • Datasets

    • Human Moderation

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