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Model Atlas · Neural networks

Convolutional Neural Network

Learns local spatial filters and hierarchical visual features.

core mathematical viewy[i,j]=Σₘₙ K[m,n]X[i-m,j-n]
Mental model

Understand it before memorizing it.

Small learned filters scan local neighborhoods then combine them into higher-level features.

Best fit

Where this model earns its place

Images

Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.

Spectrograms

Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.

Spatial signals

Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.

Strengths and limits

Trade-offs matter more than popularity.

Strengths

✓ Parameter sharing

✓ Translation-aware features

Limitations

△ Compute heavy

△ Can exploit spurious visual cues

Evaluation

Metrics to watch

Accuracy/F1Interpret with the product objective and error cost.
mAPInterpret with the product objective and error cost.
LatencyInterpret with the product objective and error cost.
Production checklist

Before it reaches real users

  1. 01

    Augment carefully

    Document the assumption and instrument the condition so regressions can be detected.

  2. 02

    Measure subgroup errors

    Document the assumption and instrument the condition so regressions can be detected.

  3. 03

    Optimize inference

    Document the assumption and instrument the condition so regressions can be detected.