The representative should edit the product tags so that the detected object is associated with the correct product classification. Accurate tagging is fundamental to maintaining reliable object-recognition results, particularly when two products have visually similar packaging.

Object detection identifies objects within an image and maps those detections to labels or product classes. If a competitor changes packaging so that its product resembles another SKU, the resulting classification can incorrectly increase the measured facings or share-of-shelf for the wrong product. Correcting the tag provides accurate classification information and helps maintain the integrity of the shelf measurement.

Salesforce ' s Einstein Vision architecture supports a feedback process for incorrectly classified objects. When an object-detection model misclassifies an image, corrected examples and labels can be added back to the dataset and used in subsequent training or retraining. Salesforce describes this feedback mechanism specifically as a way to improve models after misclassification.

Creating a new planogram would only alter the expected shelf arrangement and would not correct product identity. Turning competitor products around manipulates the physical merchandising environment rather than correcting the data. Canceling the task similarly fails to improve future recognition.

Study Guide Topics:Platform - > Einstein Object Detection - > Product Tags - > Classification - > Share-of-Shelf Accuracy

===============