1.2M+ accurately annotated fashion images fuel high performing AI/ML model for Californian technology company

Challenges.

• Categorizing and comprehending different types of home décor, furniture, fashion apparels, and accessories from the compiled and raw mix of images
• Managing an input volume of 1.2 million images within a tight deadline of 12 days with quality checks
• Training a skilled and experienced team for the project in a short time

Solution.

Hitech data specialists classified and annotated more than a million home décor, furniture, fashion apparel and accessory images. Bounding boxes were used to enhance the accuracy of object detection and add identifiers for the model to learn autonomously. The annotated images prepared a perfect training baseline for the client’s AI-model.

Approach.

• Hired and trained an experienced team of data annotators and quality control professionals with specific industry/domain knowledge.
• SOPs with clearly defined KPIs and other project metrics were defined and documented.
• Working on the client portal through secure credentials, the team first assigned unique IDs to each product image.
• ‘Bounding Box’ tool was used to create rectangular boxes around the images to accurately identify objects.
• Annotated each product within the bounding boxes with relevant terminologies based on pre-defined industry- based tags and keywords.
• Added additional classification and keywords to each home décor, furniture and fashion image during annotation to simplify future identification and retrieval.
• Applied stringent quality check process to verify image batches for expected quality benchmarks; updated the approved batches into the client’s CRM system.
• Completed image annotation and tagging for 1.2 million images within a 12-day timeline.