Abstract: Embodiments herein describe providing feedback to a shopper at a POS system using a computer vision system. Many items at a store may lack barcodes or other identifying marks such as produce. The shopper may have to perform an action to identify the item to the POS system. The computer vision system can double check the identify provided by the shopper to reduce mistakes and deter nefarious actors. If the computer vision system cannot independently confirm that the item being purchased matched the identity provided by the shopper, the POS system can display a graphical user interface (GUI) that includes an image of the item captured by the computer vision system along with identification data of the item identified by the shopper. This gives the shopper a chance to correct any mistakes.

Abstract: A computing device retrieves a stabilized weight and a weight-based item count for one or more items to be purchased. Responsive to retrieving the stabilized weight, the computing device retrieves an image-based item count and an expected weight based on the image-based item count for the one or more items to be purchased. The computing device selects between authorizing and blocking checkout of the one or more items based on the weight-based item count, the image-based item count, the stabilized weight, and the expected weight.

Abstract: Computer vision grouping recognition is provided by receiving training images that include unpackaged items; identifying, by a computer vision model, a candidate identities for unpackaged items in a given training image; receiving, from a human user, a selected identity for the unpackaged item as feedback for the candidate identity; constructing a confusion matrix tallying matches and mismatches between candidate identities and the selected identities as analyzed across the training images for each unpackaged item; identifying at least one product category that includes at least a first unpackaged item and a second unpackaged item that the confusion matrix indicates as being misidentified for each other by the computer vision model; and reconfiguring the computer vision model to identify the product category instead of the first unpackaged item or the second unpackaged item when analyzing a given image including one or more of the first unpackaged item or the second unpackaged item.

Abstract: Computer vision grouping recognition is provided by receiving training images that include unpackaged items; identifying, by a computer vision model, a candidate identities for unpackaged items in a given training image; receiving, from a human user, a selected identity for the unpackaged item as feedback for the candidate identity; constructing a confusion matrix tallying matches and mismatches between candidate identities and the selected identities as analyzed across the training images for each unpackaged item; identifying at least one product category that includes at least a first unpackaged item and a second unpackaged item that the confusion matrix indicates as being misidentified for each other by the computer vision model; and reconfiguring the computer vision model to identify the product category instead of the first unpackaged item or the second unpackaged item when analyzing a given image including one or more of the first unpackaged item or the second unpackaged item.

Abstract: Computer vision grouping recognition is provided by receiving training images that include unpackaged items; identifying, by a computer vision model, a candidate identities for unpackaged items in a given training image; receiving, from a human user, a selected identity for the unpackaged item as feedback for the candidate identity; constructing a confusion matrix tallying matches and mismatches between candidate identities and the selected identities as analyzed across the training images for each unpackaged item; identifying at least one product category that includes at least a first unpackaged item and a second unpackaged item that the confusion matrix indicates as being misidentified for each other by the computer vision model; and reconfiguring the computer vision model to identify the product category instead of the first unpackaged item or the second unpackaged item when analyzing a given image including one or more of the first unpackaged item or the second unpackaged item.

Abstract: This disclosure describes an automated process for training an ML model used by a computer vision system in a point of sale (POS) system to recognize a new item. Instead of relying on a manual process performed by a data scientist, the automated process can use images of a new (i.e., unknown) item captured at one or more POS systems to then retrain the ML model to recognize the new item. That is, the images of the item are used to retrain the ML model and to test the accuracy of the updated ML model. If the updated ML model can confidently identify the new item, the updated ML model is then used by the computer vision system to identify items at the POS system.

Abstract: Embodiments herein describe providing feedback to a shopper at a POS system using a computer vision system. Many items at a store may lack barcodes or other identifying marks such as produce. The shopper may have to perform an action to identify the item to the POS system. The computer vision system can double check the identify provided by the shopper to reduce mistakes and deter nefarious actors. If the computer vision system cannot independently confirm that the item being purchased matched the identity provided by the shopper, the POS system can display a graphical user interface (GUI) that includes an image of the item captured by the computer vision system along with identification data of the item identified by the shopper. This gives the shopper a chance to correct any mistakes.