willywongi/donut – Run with an API on Replicate

willywongi / donut

Extract structured data from receipt images using Donut 🍩 (Document Understanding Transformer)

Cold

Public
2.2K runs
T4
GitHub
Paper
License

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

Run this model in Node.js with one line of code:

npx create-replicate --model=willywongi/donut

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run willywongi/donut using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "willywongi/donut:6bdf3a5790f9cdf491e995a659563c3dcbe94d929e89216486e8fe3f909f4716",
  {
    input: {
      image: "https://replicate.delivery/pbxt/IgCzf30UdmaTYhtlaifqpVA7V7nQf7a8muE6AWie2Fm5bNv3/sample_image_cord_test_receipt_00004.png"
    }
  }
);

console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run willywongi/donut using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "willywongi/donut:6bdf3a5790f9cdf491e995a659563c3dcbe94d929e89216486e8fe3f909f4716",
    input={
        "image": "https://replicate.delivery/pbxt/IgCzf30UdmaTYhtlaifqpVA7V7nQf7a8muE6AWie2Fm5bNv3/sample_image_cord_test_receipt_00004.png"
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run willywongi/donut using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "willywongi/donut:6bdf3a5790f9cdf491e995a659563c3dcbe94d929e89216486e8fe3f909f4716",
    "input": {
      "image": "https://replicate.delivery/pbxt/IgCzf30UdmaTYhtlaifqpVA7V7nQf7a8muE6AWie2Fm5bNv3/sample_image_cord_test_receipt_00004.png"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{'menu': [{'nm': 'ICE BLAOKCOFFE', 'cnt': '2', 'price': '82,000'}, {'nm': 'AVOCADO COFFEE', 'cnt': '1', 'price': '61,000'}, {'nm': 'Oud CHINEN KATSU FF', 'cnt': '1', 'price': '51,000'}], 'sub_total': {'subtotal_price': '194,000', 'discount_price': '19,400'}, 'total': {'total_price': '174,600', 'cashprice': '200,000', 'changeprice': '25,400'}}

Generated in

1.1 seconds

Tweak it Report

Run time and cost

This model costs approximately $0.054 to run on Replicate, or 18 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 5 minutes. The predict time for this model varies significantly based on the inputs.

Readme

A simple implementation in COG of Donut 🍩: Document Understanding Transformer.

…

Model description

Donut 🍩, Document understanding transformer, is a new method of document understanding that utilizes an OCR-free end-to-end Transformer model. Donut does not require off-the-shelf OCR engines/APIs, yet it shows state-of-the-art performances on various visual document understanding tasks, such as visual document classification or information extraction (a.k.a. document parsing). (from the official repo README)

Intended use

This model uses the (pretrained weights from Huggingface)[https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2/tree/official] finetuned with CORD, a document parsing dataset. The task is fixed on cord-v2.

Cover image by AI