A tech startup is developing a new image recognition app to identify objects in real-time from live video feeds. The team is deciding whether to use a shallow neural network with fewer layers or a deeper neural network with more layers. Based on your understanding of network depth importance, what should they consider when choosing the network architecture?

Choosing a network depth involves balancing the ability to learn complex visual patterns with the need for fast, low‑power inference. A deeper network can extract finer, hierarchical features—useful when the video contains varied, detailed objects—but it also needs more training data, longer training times, and more GPU memory, which can hurt real‑time performance on mobile or edge devices. If the startup has limited labeled data or strict latency limits, a shallower model with carefully engineered feature maps may be preferable, even if it sacrifices some accuracy. Conversely, if they can collect thousands of labeled frames and have powerful hardware, a deeper architecture like ResNet can capture subtle differences between similar objects, improving accuracy. For example, distinguishing a bicycle from a motorcycle in a busy street is easier for a deep network that learns multi‑scale features, while a simple 3‑layer CNN may quickly misclassify them under time constraints.