ONNX.css — Neural network inference using stylesheets
ONNX model → CSS stylesheet
ONNX.css
Choose an ONNX model, compile it into a CSS stylesheet, then let the browser style engine run inference.
ModelMNIST classifierMNIST VAE generatorSilero voice activity detector<br>Compilemnist-8.onnx
Model ready to compile
Compiled successfully<br>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
01Input canvas<br>28×28 tensor
Clear
Draw with mouse, trackpad, or touch.
02CSS prediction<br>Argmax of ten computed logits
Prediction— Waiting for a stroke
Canvas pixels→ CSS variables→ getComputedStyle()
ModelLeNet-style CNN<br>Graph12 ONNX nodes<br>Runtime30,852 CSS properties<br>Output10 raw logits
View generated CSS ↓
Generated artifact<br>Raw compiled CSS
Preview truncated Only the beginning of the stylesheet is shown. Copy or download to inspect the complete file.
mnist-8.css · truncated previewDownload full CSSCopy full CSS
Model source: ONNX Model Zoo / MNIST. Official validated mnist-8.onnx model using ONNX opset 8.
Compiled successfully<br>Explore latent space.<br>Your browser's style engine generates images using a neural network expressed in CSS.
VAE decoder compiled to CSS 6 nodes · 3 dense layers
01Latent position<br>Two model inputs
Randomize
Horizontal 0.00<br>Vertical 0.00<br>↙↓↘←•→↖↑↗
The controls bind directly to two CSS custom properties.
02CSS-generated image<br>784 computed pixel values
Latent coordinate(0.00, 0.00)Waiting for CSS output
2 latent values→ CSS decoder→ 784 pixels
ModelVariational Autoencoder (VAE)<br>Graph6 ONNX nodes<br>Runtime3,170 CSS properties<br>Output28×28 generated image
View generated CSS ↓
Generated artifact<br>Raw decoder CSS
Preview truncated Only the beginning of the stylesheet is shown. Copy or download to inspect the complete file.
mnist-vae-decoder.css · truncated previewDownload full CSSCopy full CSS
Pretrained checkpoint: pszmk/mnist-vae-latent2. Decoder-only fixed-shape ONNX export generated by this project.
Compiled successfully<br>Listen for speech.<br>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
01Audio window<br>256 samples at 8 kHz
Load audio file WAV, MP3, M4A, or OGG
Window position 0.000 s<br>Analyze selected window<br>Audio decoding and resampling happen in JavaScript.
02CSS speech estimate<br>Silero probability output
Voice activity— —
Decision threshold0.50Load an audio file to begin
256 audio samples→ CSS STFT + VAD→ Speech probability
ModelSilero VAD v6.2.1<br>Graph48 primitive nodes<br>Runtime8,188 CSS properties<br>Input32 ms fixed window
View generated CSS ↓
Generated artifact<br>Raw VAD CSS
Preview truncated Only the beginning of the stylesheet is shown. Copy or download to inspect the complete file.
silero-vad-v6.2.1-8k-fixed.css · truncated previewDownload full CSSCopy full CSS
Model source: Silero VAD 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.