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Depthwise Separable Convolution: MobileNet's Efficiency Trick (2D)

2D top-down comparison of standard convolution against MobileNet's depthwise-separable convolution: pan and zoom a sliding K×K window across a fanned channel stack while live FLOP and parameter bar charts show how splitting a filter into per-channel and 1x1 stages cuts computation.

AI & Machine Learning2DModerate60 FPS📱 Mobile-adapted⇄ 3D version

Every convolutional neural network for computer vision spends most of its compute budget sliding filters across feature maps. This 2D companion renders that sliding-window mechanic as a top-down, pannable/zoomable scene: a fanned stack of channel cards with a gold K×K window sliding across it, next to a pair of live bar charts. Change the kernel size and the input/output channel counts and watch the two cost bars — standard convolution versus depthwise-plus-pointwise — update from the exact multiply-accumulate and parameter-count formulas used in the MobileNet paper, with a live speedup readout showing exactly how much cheaper the factored version is.

⚙ Under the hood

convolutional neural networksMobileNetdepthwise separable convolutionFLOPsmodel efficiencycomputer visiondeep learning

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install