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Data Annotation for E-Commerce and Retail

Data annotation for e-commerce involves labeling product images, categorizing items, and tagging attributes to train AI models for enhanced search, recommendation systems, and inventory management. This process improves customer experience, streamlines operations, and boosts sales efficiency.

Visual Search for Similar Products

Visual search for similar products enables customers to find items by simply uploading an image. Powered by AI, this technology identifies visually matching products, enhancing the shopping experience with faster and more intuitive searches.

Automatically Generate Labels from an Image

Automatically generating labels from an image uses AI to identify and tag objects, categories, or attributes within the image. This automation accelerates data processing, improves accuracy, and reduces manual effort in tasks like inventory management and product cataloging.

Semantic Annotation for Clothing Brand

Semantic annotation for clothing brands involves labeling images with detailed information, such as fabric type, color, patterns, and design features. This process helps train AI models for better product search, categorization, and personalized recommendations, enhancing the overall shopping experience.

OCR

OCR (Optical Character Recognition) in retail and e-commerce automates the extraction of text from product labels, invoices, and receipts. This technology enhances inventory management, simplifies order processing, and improves customer experiences through faster data handling and accurate information capture.

Counting left products on shelves

Counting left products on shelves uses AI-powered image recognition to track inventory in real time, ensuring accurate stock levels. This technology helps retailers optimize shelf management, prevent stockouts, and improve the overall shopping experience for customers.

Detecting Customer Facial emotions

Detecting customer facial emotions uses AI-powered facial recognition technology to analyze expressions and identify emotions such as happiness, frustration, or surprise. This helps businesses understand customer sentiments, improve engagement, and tailor personalized experiences in real time.

Detecting Expiration Date of the Product

Detecting expiration dates of products uses AI and image recognition technology to automatically read and extract expiration dates from product labels. This ensures accurate tracking, reduces waste, and helps businesses maintain product quality and compliance in inventory management.

Save Both Time and Money!

Save Both Time and Money!

Discover why Unitlab AI stands out as the ultimate solution for your data annotation needs.

15X

Faster Data Annotation
Auto Data Collect
Auto Data Labeling
Auto Model Validation
Auto QA
Auto Model-In-the-Loop

60%

Free Up AI Engineer’s Time
Dataset Curation
Dataset Version Control
Dataset QA
AI Model Integration
AI Model Validation

5X

Save AI Development Cost
5X saving in Data Collection
10X saving in Data Labeling
5X saving in Dataset Curation
5X saving in AI Development
5X saving in Model Validation

Annotation types

Annotation types for e-commerce and retail involve categorizing and labeling product images, descriptions, and attributes such as color, size, brand, and material. These annotations help train AI models for improved search accuracy, personalized recommendations, inventory management, and customer experience enhancement in online retail environments.

Bounding Box for Object Detection

Bounding box annotation for object detection in retail and e-commerce involves drawing boxes around products or key elements in images to train AI models. This technique enhances product recognition, enables accurate inventory tracking, and improves visual search capabilities, providing a seamless shopping experience for customers.

Landmarking

Detecting customer facial emotions uses AI-powered facial recognition technology to analyze expressions and identify emotions such as happiness, frustration, or surprise. This helps businesses understand customer sentiments, improve engagement, and tailor personalized experiences in real time.

Segmentation for Detect clothing brand

Semantic annotation for clothing brands involves labeling images with detailed information, such as fabric type, color, patterns, and design features. This process helps train AI models for better product search, categorization, and personalized recommendations, enhancing the overall shopping experience.

FAQs

Is Unitlab a free Data Annotation Tool?

Yes, it is! No credit card is required to use Unitlab. You can use the Unitlab tool for free forever as long as you stay within the set limits. However, for more extensive use, Unitlab offers a subscription model. This model's pricing is customized to your organization's specific usage, data requirements, and any extra services you might need, like our advanced labeling solutions. To get a tailored quote and further information, please connect with our sales team.

Can I switch my plan after I create my account?

Yes, you can start working with a Free Plan and then change plans in the future as you evaluate which is best for you.

How is the Price of a Data Labeling Service Calculated?

Data Labeling Services start at just $0.02 per image. The base price depends on the types of data annotation, the number of classes, and the average number of objects to annotate per image.

How Can I Set Up the Unitlab On-Premises Solution in My Local Workspace?

Unitlab offers a range of scalable On-Premises solutions! Contact Us to discuss your requirements. You can purchase Unitlab's On-Premises solution after consulting with us. We help you install our entire annotation system in your workspace.

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