# ONNX.css – Neural network inference using stylesheets

> Source: <https://onnx.css.evanking.io/>
> Published: 2026-08-20 14:26:08+00:00

ONNX model → CSS stylesheet

# ONNX.css

Choose an ONNX model, compile it into a CSS stylesheet, then let the browser style engine run inference.

Compiled successfully

## Draw a digit.

Your browser's style engine will infer the result using a neural network expressed in CSS.

**Model compiled to CSS** Official ONNX Model Zoo / opset 8

## Input canvas

28×28 tensor

Draw with mouse, trackpad, or touch.

## CSS prediction

Argmax of ten computed logits

**—** Waiting for a stroke

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**→** CSS variables

**→** getComputedStyle()

**LeNet-style CNN**

**12 ONNX nodes**

**30,852 CSS properties**

**10 raw logits**

Generated artifact

## Raw compiled CSS

**Preview truncated** Only the beginning of the stylesheet is shown. Copy or download to inspect the complete file.

Model source: [ONNX Model Zoo / MNIST](https://github.com/onnx/models/tree/main/validated/vision/classification/mnist). Official validated `mnist-8.onnx`

model using ONNX opset 8.

Compiled successfully

## Explore latent space.

Your browser's style engine generates images using a neural network expressed in CSS.

**VAE decoder compiled to CSS** 6 nodes · 3 dense layers

## Latent position

Two model inputs

The controls bind directly to two CSS custom properties.

## CSS-generated image

784 computed pixel values

`(0.00, 0.00)`

Waiting for CSS output**→** CSS decoder

**→** 784 pixels

**Variational Autoencoder (VAE)**

**6 ONNX nodes**

**3,170 CSS properties**

**28×28 generated image**

Generated artifact

## Raw decoder CSS

**Preview truncated** Only the beginning of the stylesheet is shown. Copy or download to inspect the complete file.

Pretrained checkpoint: [pszmk/mnist-vae-latent2](https://huggingface.co/pszmk/mnist-vae-latent2). Decoder-only fixed-shape ONNX export generated by this project.

Compiled successfully

## Listen for speech.

Load audio, choose a fixed 32 ms window, and estimate its speech probability using a neural network in CSS.

**Silero VAD v6.2.1 compiled to CSS** Fixed 8 kHz · recurrent state

## Audio window

256 samples at 8 kHz

Audio decoding and resampling happen in JavaScript.

## CSS speech estimate

Silero probability output

**—**

`—`

`0.50`

Load an audio file to begin**→** CSS STFT + VAD

**→** Speech probability

**Silero VAD v6.2.1**

**48 primitive nodes**

**8,188 CSS properties**

**32 ms fixed window**

Generated artifact

## Raw VAD CSS

**Preview truncated** Only the beginning of the stylesheet is shown. Copy or download to inspect the complete file.

Model source: [Silero VAD v6.2.1](https://github.com/snakers4/silero-vad/releases/tag/v6.2.1). With minor model surgery to remove dynamic shapes: fixed 8 kHz branch with prior-audio context and the LSTM lowered to primitive operators.