All demosWaste Sorter

Waste Sorter

Glass, paper, plastic. Put a label on the rubbish.

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Performance & cost

This session

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Delivery time · median
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Est. cost · USD
$0.00

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Model compute is the average per measured request. Delivery time is the median browser time for successful requests, including queue, network, and retries. Cached delivery speed is not inference speed. Model compute can exceed browser time when one request processes many inputs.

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Try it

Pick a photo and classify it.

Build with the SDKTypeScript · Python · dm1

One request from this demo. Copy it into your app.

Uses your MS_API_KEY and quota. Keep the key on your server.

Install · set your key
npm install @cloudraker/milliseconds
export MS_API_KEY="your-api-key"
example.ts
import { readFile } from "node:fs/promises";
import { DecisionMachine } from "@cloudraker/milliseconds";

// Run on your server; reads MS_API_KEY from the environment.
// The image carries the input; the leading text is optional context.
const dm = new DecisionMachine();

const { result, usage } = await dm.classify(
  await readFile("image.jpg"),
  {
    "glass": "A glass bottle, jar, drinking glass or other glass object.",
    "paper": "Ordinary paper, newspaper, office paper or a paper flyer.",
    "cardboard": "A cardboard box, corrugated board or thick cardboard packaging.",
    "plastic": "A plastic bottle, rigid plastic container or plastic packaging.",
    "metal": "A metal food can, drink can, lid or other metal object.",
    "trash": "Other waste or mixed-material packaging, such as a coated disposable coffee cup or flexible food pouch."
  },
  { detail: "low" }
).withUsage();

console.log(result);
console.log({ inputTokens: usage.inputTokens, modelMs: usage.inferenceMs });

Under the hood How it works & limitations

API calls/classify

Sort by what the camera sees

Use material classification to triage collection photos or flag uncertain items for a sorting operator. One image goes to /classify with six described labels.

A material label is not a recycling instruction

TrashNet photographed single objects against a light background. A cluttered conveyor belt, dirty container or mixed-material package is a different task. Local collection rules still decide what belongs in each bin.

Real photos, original labels

These twelve unmodified photos come from TrashNet by Gary Thung and Mindy Yang, shared under the MIT license. Each photo links to its source archive and identifies its original filename. The dataset labels are comparison points, not predictions.

Image dataset license
MIT License

Copyright (c) 2017 Gary Thung

Permission is hereby granted, free of charge, to any person obtaining a copy
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in the Software without restriction, including without limitation the rights
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copies of the Software, and to permit persons to whom the Software is
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The above copyright notice and this permission notice shall be included in all
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.