ruvector/npm/packages/ruvector-cnn

@ruvector/cnn

Turn images into searchable vectors — runs in browsers, no backend needed.

What Does This Do?

This package converts images into numbers (called "embeddings") that describe what's in the picture. Similar images produce similar numbers.

Use it to:

• Build "find similar images" features
• Group photos by what they show
• Create visual search for products
• Compare images without AI APIs

import { init, CnnEmbedder } from '@ruvector/cnn';

await init();
const embedder = new CnnEmbedder();

// Turn an image into numbers
const numbers = embedder.extract(imagePixels, 224, 224);

// Compare two images (1.0 = identical, 0 = unrelated)
const similarity = embedder.cosineSimilarity(numbers1, numbers2);

Why Use This?

What You Get	Why It Matters
Runs in the browser	No server costs, instant results
~5ms per image	Fast enough for real-time
~900KB download	Small enough for any website
No API calls	Works offline, no per-image fees
Training included	Teach it your own categories

Installation

npm install @ruvector/cnn

How to Use It

1. Extract Image Features

import { init, CnnEmbedder } from '@ruvector/cnn';

// Start the engine (do this once)
await init();

// Create the feature extractor
const embedder = new CnnEmbedder({
  embeddingDim: 512,  // How many numbers per image
  normalize: true      // Make comparisons easier
});

// Get pixels from an image (RGB, no transparency)
// Each pixel has 3 values: red, green, blue (0-255)
const pixels = new Uint8Array(224 * 224 * 3);

// Turn pixels into 512 numbers that describe the image
const features = embedder.extract(pixels, 224, 224);
console.log('Got', features.length, 'numbers'); // 512

2. Compare Two Images

const features1 = embedder.extract(image1Pixels, 224, 224);
const features2 = embedder.extract(image2Pixels, 224, 224);

// How similar are they? (1.0 = same, 0 = different, -1 = opposite)
const score = embedder.cosineSimilarity(features1, features2);

if (score > 0.8) {
  console.log('These images are very similar!');
} else if (score > 0.5) {
  console.log('These images have some things in common');
} else {
  console.log('These images are different');
}

3. Find the Most Similar Image

// Your collection of images (already converted to features)
const catalog = [
  { name: 'red-shoe.jpg', features: embedder.extract(redShoePixels, 224, 224) },
  { name: 'blue-bag.jpg', features: embedder.extract(blueBagPixels, 224, 224) },
  { name: 'red-dress.jpg', features: embedder.extract(redDressPixels, 224, 224) },
];

// User uploads a photo
const userPhoto = embedder.extract(uploadedPixels, 224, 224);

// Find the best match
let bestMatch = null;
let bestScore = -1;

for (const item of catalog) {
  const score = embedder.cosineSimilarity(userPhoto, item.features);
  if (score > bestScore) {
    bestScore = score;
    bestMatch = item.name;
  }
}

console.log('Best match:', bestMatch, 'Score:', bestScore);

4. Get Pixels from a Canvas

// If you have an image in a canvas element
const canvas = document.getElementById('myCanvas');
const ctx = canvas.getContext('2d');

// Get the pixel data
const imageData = ctx.getImageData(0, 0, 224, 224);

// Canvas gives RGBA (4 values per pixel), we need RGB (3 values)
const rgb = new Uint8Array(224 * 224 * 3);
for (let i = 0, j = 0; i < imageData.data.length; i += 4, j += 3) {
  rgb[j] = imageData.data[i];       // Red
  rgb[j + 1] = imageData.data[i + 1]; // Green
  rgb[j + 2] = imageData.data[i + 2]; // Blue
  // Skip alpha (imageData.data[i + 3])
}

const features = embedder.extract(rgb, 224, 224);

Training (Teaching It Your Categories)

You can train the model to be better at recognizing your specific images.

Contrastive Training (SimCLR style)

Show it pairs of images that should match:

import { init, InfoNCELoss, CnnEmbedder } from '@ruvector/cnn';

await init();

const embedder = new CnnEmbedder();
const trainer = new InfoNCELoss(0.1); // Lower = stricter matching

// Get features for your training pairs
// Pairs: image1 and image1_different_angle should match
const image1 = embedder.extract(photo1, 224, 224);
const image1_alt = embedder.extract(photo1_rotated, 224, 224);

// Pack into one array: [view1s..., view2s...]
const batch = new Float32Array(2 * 512);
batch.set(image1, 0);
batch.set(image1_alt, 512);

const loss = trainer.forward(batch, 1, 512);
console.log('Loss:', loss); // Lower is better

Triplet Training

Show it: "A is similar to B, but different from C"

import { init, TripletLoss, CnnEmbedder } from '@ruvector/cnn';

await init();

const embedder = new CnnEmbedder();
const trainer = new TripletLoss(1.0); // margin

// Anchor: the reference image
// Positive: should be similar to anchor
// Negative: should be different from anchor
const anchor = embedder.extract(redShoePhoto, 224, 224);
const positive = embedder.extract(redShoePhoto2, 224, 224); // Same shoe
const negative = embedder.extract(blueBagPhoto, 224, 224);   // Different item

const loss = trainer.forward(
  new Float32Array(anchor),
  new Float32Array(positive),
  new Float32Array(negative),
  512
);
console.log('Loss:', loss);

Fast Math Operations

If you're building custom features, these are optimized:

import { init, SimdOps, LayerOps } from '@ruvector/cnn';

await init();

// Dot product (sum of element-wise multiplication)
const a = new Float32Array([1, 2, 3, 4]);
const b = new Float32Array([5, 6, 7, 8]);
const result = SimdOps.dotProduct(a, b); // 70

// ReLU: set negative values to 0
const data = new Float32Array([-1, 0, 1, 7]);
SimdOps.relu(data); // [0, 0, 1, 7]

// ReLU6: clamp between 0 and 6
SimdOps.relu6(data); // [0, 0, 1, 6]

// L2 normalize (make length = 1)
SimdOps.l2Normalize(data);

Performance

What	How Long	Notes
Extract features (224×224 image)	~5ms	With SIMD
Compare two images	~0.01ms	Just math
Training step	~1ms	Per batch
First load	~100ms	Downloads WASM

Browser Support

Works in all modern browsers with WebAssembly:

• Chrome 57+
• Firefox 52+
• Safari 11+
• Edge 16+

For best speed, use browsers with SIMD128 support:

• Chrome 91+
• Firefox 89+
• Safari 16.4+

Troubleshooting

"init() takes too long"

• Normal: First call downloads ~900KB WASM file
• Fix: Call init() early, before user needs results

"Images look wrong"

• Check: Images must be 224×224 pixels
• Check: Pixel format is RGB (3 values per pixel, not RGBA)
• Check: Values are 0-255, not 0-1

"Similarity scores are all low"

• Try: Set normalize: true in CnnEmbedder options
• Check: Are your images actually similar?

API Reference

CnnEmbedder

new CnnEmbedder(options?: {
  embeddingDim?: number;  // Default: 512
  normalize?: boolean;    // Default: true
})

.extract(pixels: Uint8Array, width: number, height: number): Float32Array
.cosineSimilarity(a: Float32Array, b: Float32Array): number
.embeddingDim: number

InfoNCELoss

new InfoNCELoss(temperature?: number)  // Default: 0.1

.forward(embeddings: Float32Array, batchSize: number, dim: number): number
.temperature: number

TripletLoss

new TripletLoss(margin?: number)  // Default: 1.0

.forward(anchors, positives, negatives: Float32Array, dim: number): number
.margin: number

Related Packages

• ruvector — Core vector database
• @ruvector/attention — AI attention layers
• @ruvector/gnn — Graph neural networks

License

MIT OR Apache-2.0