Maplebear Inc.

Sarang Zambare

Lin Gao

Yilin Huang

Michael Joseph Sanzari

Shiyuan Yang

Ahmed Beshry

Jungsoo Woo

Griffin Kelly

Disclosed are technologies for generating training data for identification neural networks. Series of images are captured of a plurality of merchandise items from different angles and with different background assortments of other merchandise items. A labeled training dataset is generated for the plurality of merchandise items. The series of captured images is normalized, where the merchandise occupies a threshold percentage of pixels in the normalized image. The training dataset is extended by applying augmentation operations to the normalized images to generate a plurality of augmented images. Each image is stored in the training dataset as a unique training data point for the given merchandise item it depicts. Labels are generated mapping each training data point to attributes associated with the depicted merchandise item. Input neural networks are trained on the labeled training dataset to perform real-time identification of selected merchandise items placed into a self-checkout apparatus by a user.

Systems and methods for training data generation for object identification and self-checkout anti-theft

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Brooklyn, NY, United States of America

Sarang Zambare