Which SAS DLPy architecture is most appropriate for detecting and localizing multiple objects within a single image?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — c is correct because object detection (identifying and localizing multiple objects with bounding boxes) requires specialized architectures like YOLO, Faster R-CNN, or SSD that predict class labels and bounding coordinates simultaneously. SAS DLPy provides built-in object detection models including YOLO variants.
Full explanation below image
Full Explanation
C is correct because object detection (identifying and localizing multiple objects with bounding boxes) requires specialized architectures like YOLO, Faster R-CNN, or SSD that predict class labels and bounding coordinates simultaneously. SAS DLPy provides built-in object detection models including YOLO variants. A (standard CNN) performs image-level classification, not localization. B (LSTM) is for sequential data. D (PCA + k-means) is unsupervised and unsuitable for detection.