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BPM

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

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

Client Profile

The Californian technology company builds Artificial Intelligence and Machine Learning models for global SMBs and Fortune 500 companies operating in the retail space. The AI/ML models help in product categorization, visual search, virtual fitting rooms, and provide personalized recommendations to customers.

Business Need.

To build accurate AI/ML models, the company extracts meaningful information from visual data repositories available with their retail partners. They label images to train, validate and build computer-vision based algorithms to recognize objects similar in a way that a human can.

To create an intelligent AI model for a home décor, furniture and fashion client, the company received a training data repository of 1.2 million images. They had to:

  • annotate the images using appropriate terms to identify and classify the various products
  • prepare an accurate data set with 2D bounding box images

Due to the variety and volume of the data, annotating images for different specifications was slowing their speed to market. Managing a vast data annotation team demanded huge time, training and cost investment.

Faced with a tight deadline and critical need for accuracy, the company partnered with Hitech for tagging of data and consistent flow of labelled dataset to train computer vision model.

Challenges.

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.

Business Impact.

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