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MobileNet MCQ
Depthwise separable convolutions slash compute so models run on phones and embedded devices.
Mobile
Edge deploy
Depthwise
Per-channel
Pointwise
1×1 mix
Multipliers
α, ρ
Efficient CNNs
MobileNet factorizes a standard convolution into a depthwise spatial filter per input channel followed by a pointwise 1×1 that mixes channels. This cuts FLOPs and parameters dramatically. Width and input resolution multipliers offer accuracy–latency tradeoffs.
Depthwise separable
Cost roughly channels × k² + channels² vs channels_in × channels_out × k² for a k×k conv.
Key ideas
Depthwise
Each channel convolved independently with its own spatial kernel.
Pointwise
1×1 conv combines channels—linear mix at each pixel.
Width multiplier α
Uniformly thins channel counts across the network.
Resolution
Smaller input ρ reduces compute quadratically in spatial size.
MobileNet block
depthwise 3×3 → BN → ReLU → pointwise 1×1 → BN → ReLU