For K-12 Students · Image Convolution
Image Convolution for an image classifier
Built for k-12 students learning it in middle or high school. Watch the idea come alive with plain-language steps and everyday examples — perfect for projects and homework. Simulate an image classifier live below — adjust the inputs and watch it respond, right in your browser.
Image Convolution / KernelsLive
the operation inside every CNN
Controls
Presets
3×3 kernel
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8
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Each output pixel is a weighted sum of its 3×3 neighborhood. Slide this kernel over an image and you get blur, sharpening, or edge maps — the exact operation a convolutional neural network learns.
Data Inspector
KernelEdge detect
Window3×3
Smoothing sigma0.4
Sum0.00
Governing equation
Reading this result: Convolution slides the 3-tap kernel over the image; each output pixel is a weighted sum of its 3×3 neighbors. At sigma ≈ 0.4 the weights stay tightly concentrated, so edges and fine detail survive and high-frequency structure is preserved.
Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.
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Frequently asked questions
- Is this good for k-12 students?
- Yes — this version of "Image Convolution for an image classifier" is framed for k-12 students learning it in middle or high school. Watch the idea come alive with plain-language steps and everyday examples — perfect for projects and homework.
- Do I need to install anything?
- No. It runs in any modern browser, free, with no account required.