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Answer: Use a lightweight model architecture, label with bounding boxes, evaluate using mean average precision.
For real-time applications, a lightweight model architecture is preferred to ensure efficiency. Labeling with bounding boxes is necessary for object detection models. Evaluating using mean average precision (mAP) is appropriate as it provides a comprehensive measure of how well the model performs across different IoU thresholds, which is crucial for object detection tasks.
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You are developing a custom object detection model to detect various types of fruits in an orchard. The model needs to be highly accurate and efficient due to the real-time nature of the application. Describe how you would approach the training of this model, including the choice of model architecture, the labeling process, and the metrics you would use to evaluate the model's performance.
A
Use a lightweight model architecture, label with bounding boxes, evaluate using recall.
B
Use a complex model architecture, label with object tags, evaluate using accuracy.
C
Use a lightweight model architecture, label with bounding boxes, evaluate using mean average precision.
D
Use a complex model architecture, label with bounding boxes, evaluate using precision.