Image Annotation
Projects
Product Image Captioning
Overview
MoonvalleyAI needed clean, attribute-rich captions to train and evaluate a vision-language model. Softechub produced consistent, brand-safe descriptions that capture the main product and key attributes to improve retrieval and classification.
Project Scope
The project involved 150,000 images across apparel, home, beauty, electronics, accessories, and grocery
- Reviewing a large, multi-category catalog (fashion, electronics, furniture).
- Writing two captions per image - a concise short caption and a fuller long caption in neutral, brand-safe language.
- Categorizing each product and tagging key attributes (category, color, material, pattern, style, intended use).
- Drawing a bounding box around the primary product and additional boxes for multi-product or lifestyle shots when needed.
- Recording camera details for each image - view and angle (front, side, three-quarter, top-down, close-up).
Our Approach
Expert Annotators - Assembled a retail-domain team trained on product taxonomies and vision-language standards.
Guideline mastery - studied the instructions, ran a quick calibration, and kept a handy style sheet.
Complete captioning - wrote short and long captions that fully describe each product using only what is visible.
Angle capture - logged shot angles consistently with a simple, controlled vocabulary.
Thorough review - peer checks on every item plus supervisor validation to keep quality high.
Expert Annotators - Assembled a retail-domain team trained on product taxonomies and vision-language standards.
Methodology & Quality Assurance
Screenshots of the project
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