Started with the obvious stuff, CNNs, anchor boxes, two-stage vs single-stage detectors.
Start by clarifying the requirements (e.g., real-time vs. offline, accuracy vs. latency, deployment constraints) and then propose a two-stage detector like Faster R-CNN or a one-stage detector like YOLO, justifying the choice based on trade-offs. Discuss data collection, annotation, training, evaluation, and optimization for Apple's ecosystem, emphasizing on-device inference and privacy.
Pro tip: Highlight Apple's emphasis on on-device processing and privacy by suggesting quantization and Core ML deployment, and mention the importance of a diverse, well-annotated dataset to handle varied flower species and lighting conditions.
Ask about the intended use case: real-time detection on mobile devices, accuracy targets, latency constraints, and whether it's for a consumer app or internal tool. This shapes the entire design.
Select an object detection model (e.g., YOLO for speed, Faster R-CNN for accuracy) and backbone (e.g., MobileNet for efficiency). Justify based on trade-offs between accuracy, speed, and model size.
Discuss sourcing a diverse dataset of flower images with bounding box annotations. Mention data augmentation, handling class imbalance, and ensuring privacy if using user data.
Outline training pipeline: loss functions (e.g., focal loss), optimization, and metrics (mAP, IoU). Include validation strategy and hyperparameter tuning.
Explain model compression (quantization, pruning), conversion to Core ML, and on-device inference. Discuss latency, power consumption, and privacy considerations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.