What is MobileNet designed for?
mediumAnswer
- Efficient inference on mobile and embedded devices.
- Uses depthwise-separable convolutions everywhere and a 'width multiplier' α scaling all layer widths, plus a resolution multiplier ρ.
- MobileNet-v2 adds inverted residuals (expand → depthwise → project) and linear bottlenecks.
- MobileNet-v3 uses NAS + h-swish. 5-10x smaller and faster than ResNet at similar accuracy.
Check yourself — multiple choice
- Standard convolutions
- Depthwise-separable convs + width/resolution multipliers — small, fast on mobile/embedded
- Same size as VGG
- Only for cloud
MobileNet: depthwise-separable convs + multipliers → mobile-scale efficient inference.
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