Question 1
Choose 2
Which two of the following allows a Consumer Goods Cloud user to review the vision detection accuracy?
Correct answer:
B. Shelf Metrics
E. Model Metrics
Explanation
This question tests the metrics available for evaluating vision-detection results in Consumer Goods Cloud. Users need visibility into both the accuracy of the shelf-level comparison and the AI model’s confidence in identifying objects from a captured shelf image.
✅ B. Shelf Metrics
Shelf Metrics allow users to review detection accuracy at the shelf level by comparing the actual shelf image with the expected planogram or reference shelf arrangement. This comparison helps identify whether products are placed, counted, and positioned as expected. It provides insight into how accurately the vision process recognizes the overall shelf layout and compliance conditions.
✅ E. Model Metrics
Model Metrics provide information about the vision model’s detection performance for objects identified in a shelf image. Users can review measures such as the confidence score and number of detections associated with recognized objects. These metrics help determine whether the AI model is reliably identifying products and whether further model training or image-quality improvements are needed.
❌ A. Goods Metrics
Goods Metrics are not a standard metric category for reviewing vision-detection accuracy. Consumer Goods Cloud uses shelf-level metrics to assess detected shelf conditions and model-level metrics to evaluate detection confidence. This option does not represent the metrics used to validate the quality or accuracy of AI-powered shelf-image detection.
❌ C. Planogram Metrics
Planogram information provides the expected product arrangement or reference layout, but Planogram Metrics are not used to review vision-detection accuracy. Shelf Metrics use the shelf image and expected planogram to assess compliance, while Model Metrics focus on the AI model’s recognition confidence and object-detection results.
❌ D. Object Metrics
Object Metrics are not the standard area for reviewing vision-detection accuracy in this context. Although the vision model detects individual objects in an image, users assess detection quality through Model Metrics. Those metrics provide confidence and detection-count information needed to evaluate how accurately the model recognized the objects.
Reference
→ Planogram Checks — Salesforce Help confirms that Consumer Goods Cloud uses shelf images and reference planograms to assess retail shelf execution.
This question tests the metrics available for evaluating vision-detection results in Consumer Goods Cloud. Users need visibility into both the accuracy of the shelf-level comparison and the AI model’s confidence in identifying objects from a captured shelf image.
✅ B. Shelf Metrics
Shelf Metrics allow users to review detection accuracy at the shelf level by comparing the actual shelf image with the expected planogram or reference shelf arrangement. This comparison helps identify whether products are placed, counted, and positioned as expected. It provides insight into how accurately the vision process recognizes the overall shelf layout and compliance conditions.
✅ E. Model Metrics
Model Metrics provide information about the vision model’s detection performance for objects identified in a shelf image. Users can review measures such as the confidence score and number of detections associated with recognized objects. These metrics help determine whether the AI model is reliably identifying products and whether further model training or image-quality improvements are needed.
❌ A. Goods Metrics
Goods Metrics are not a standard metric category for reviewing vision-detection accuracy. Consumer Goods Cloud uses shelf-level metrics to assess detected shelf conditions and model-level metrics to evaluate detection confidence. This option does not represent the metrics used to validate the quality or accuracy of AI-powered shelf-image detection.
❌ C. Planogram Metrics
Planogram information provides the expected product arrangement or reference layout, but Planogram Metrics are not used to review vision-detection accuracy. Shelf Metrics use the shelf image and expected planogram to assess compliance, while Model Metrics focus on the AI model’s recognition confidence and object-detection results.
❌ D. Object Metrics
Object Metrics are not the standard area for reviewing vision-detection accuracy in this context. Although the vision model detects individual objects in an image, users assess detection quality through Model Metrics. Those metrics provide confidence and detection-count information needed to evaluate how accurately the model recognized the objects.
Reference
→ Planogram Checks — Salesforce Help confirms that Consumer Goods Cloud uses shelf images and reference planograms to assess retail shelf execution.
