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.
Input canvas
28×28 tensor
Draw with mouse, trackpad, or touch.
CSS prediction
Argmax of ten computed logits
——————————Generated artifact
Raw compiled CSS
Model source: ONNX Model Zoo / 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.
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 outputGenerated artifact
Raw decoder CSS
Pretrained checkpoint: 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.
Audio window
256 samples at 8 kHz
Audio decoding and resampling happen in JavaScript.
CSS speech estimate
Silero probability output
—0.50Load an audio file to beginGenerated artifact
Raw VAD 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.