Faucet Flow Rate Estimator

Fast-SCNN Model Complexity Estimator

This tool estimates the number of parameters (model size) and Floating Point Operations (FLOPs - computational cost) for a simplified Fast-SCNN-like architecture.

Adjust the input parameters to see how they impact the model's complexity, which directly affects its speed and memory footprint.

Note: These are estimations based on common layer calculations and do not represent the exact values of the official Fast-SCNN model, which has specific, optimized layers.

Input Parameters:

Input Image Width (pixels):

Input Image Height (pixels):

Number of Output Classes (e.g., 21 for Pascal VOC):

Initial Channels (e.g., 32 for MobileNet-like base):

Number of Feature Blocks (Simplified, e.g., 3-5):

Decimal Places for Results:

Estimated Model Complexity

Total Parameters (Millions)

Total FLOPs (Billions)

Estimated Memory (MB, for parameters)

Estimations are based on simplified convolutional and depthwise-separable layers. Actual Fast-SCNN parameters and FLOPs may vary due to specific architectural choices and optimization techniques not accounted for in this simplified model.