Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for automatic image selection for online product catalogs. An image selection system gathers feature data for images of an item included in listings posted to an online marketplace. The image selection system uses the feature data as input in a machine learning model to determine probability scores indicating an estimated probability that each image is suitable to represent the item. The machine learning model is trained based on a set of training images of the item that have been labeled to indicate whether they are suitable to represent the image. The image selection system compares the probability scores and selects an image to represent the item as a stock image based on the comparison.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for automatic image selection for online product catalogs. An image selection system gathers feature data for images of an item included in listings posted to an online marketplace. The image selection system uses the feature data as input in a machine learning model to determine probability scores indicating an estimated probability that each image is suitable to represent the item. The machine learning model is trained based on a set of training images of the item that have been labeled to indicate whether they are suitable to represent the image. The image selection system compares the probability scores and selects an image to represent the item as a stock image based on the comparison.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for automatic image selection for online product catalogs. An image selection system gathers feature data for images of an item included in listings posted to an online marketplace. The image selection system uses the feature data as input in a machine learning model to determine probability scores indicating an estimated probability that each image is suitable to represent the item. The machine learning model is trained based on a set of training images of the item that have been labeled to indicate whether they are suitable to represent the image. The image selection system compares the probability scores and selects an image to represent the item as a stock image based on the comparison.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for automatic image selection for online product catalogs. An image selection system gathers feature data for images of an item included in listings posted to an online marketplace. The image selection system uses the feature data as input in a machine learning model to determine probability scores indicating an estimated probability that each image is suitable to represent the item. The machine learning model is trained based on a set of training images of the item that have been labeled to indicate whether they are suitable to represent the image. The image selection system compares the probability scores and selects an image to represent the item as a stock image based on the comparison.

Abstract: A method of automatically producing a labeled dataset for training a supervised Machine Learning (ML) model to identify items purchased in a store. The method comprising receiving images captured by imaging sensor(s) deployed to monitor an interior space of a store in which a plurality of items are offered for sale, detecting items picked up by customers tracked in the store based on analysis of the images, detecting the picked up items while checked out for the tracked customers at a POS comprising a POS reader configured to read an identifier of each checked out item, correlating between the detected picked up items and respective identifiers received from the POS reader according to timestamps of the identifiers read events, labeling each image depicting a detected item with the respective identifier and outputting a labeled dataset comprising a plurality of labeled images.

Abstract: A method of automatically producing a labeled dataset for training a supervised Machine Learning (ML) model to identify items purchased in a store. The method comprising receiving images captured by imaging sensor(s) deployed to monitor an interior space of a store in which a plurality of items are offered for sale, detecting items picked up by customers tracked in the store based on analysis of the images, detecting the picked up items while checked out for the tracked customers at a POS comprising a POS reader configured to read an identifier of each checked out item, correlating between the detected picked up items and respective identifiers received from the POS reader according to timestamps of the identifiers read events, labeling each image depicting a detected item with the respective identifier and outputting a labeled dataset comprising a plurality of labeled images.

Abstract: A method of automatically producing a labeled dataset for training a supervised Machine Learning (ML) model to identify items purchased in a store. The method comprising receiving images captured by imaging sensor(s) deployed to monitor an interior space of a store in which a plurality of items are offered for sale, detecting items picked up by customers tracked in the store based on analysis of the images, detecting the picked up items while checked out for the tracked customers at a POS comprising a POS reader configured to read an identifier of each checked out item, correlating between the detected picked up items and respective identifiers received from the POS reader according to timestamps of the identifiers read events, labeling each image depicting a detected item with the respective identifier and outputting a labeled dataset comprising a plurality of labeled images.