hilongjw/screen-ui-detector – Run with an API on Replicate

Input

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

npx create-replicate --model=hilongjw/screen-ui-detector

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 hilongjw/screen-ui-detector using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "hilongjw/screen-ui-detector:4391527954a30359df9e4d610beeb6f98cbaef2207737d3051de98dc366a9b6c",
  {
    input: {
      iou: 0.31,
      image: "https://replicate.delivery/pbxt/JWIP86XsvRtEVBXAmzKh3WjLm3lxLG8GqFqeSXUNtyLajiQH/screenshot-20230913-175026.png",
      imgsz: 640,
      show_conf: true,
      show_text: true,
      threshold: 0.53
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", 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 hilongjw/screen-ui-detector using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "hilongjw/screen-ui-detector:4391527954a30359df9e4d610beeb6f98cbaef2207737d3051de98dc366a9b6c",
    input={
        "iou": 0.31,
        "image": "https://replicate.delivery/pbxt/JWIP86XsvRtEVBXAmzKh3WjLm3lxLG8GqFqeSXUNtyLajiQH/screenshot-20230913-175026.png",
        "imgsz": 640,
        "show_conf": True,
        "show_text": True,
        "threshold": 0.53
    }
)
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 hilongjw/screen-ui-detector 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": "4391527954a30359df9e4d610beeb6f98cbaef2207737d3051de98dc366a9b6c",
    "input": {
      "iou": 0.31,
      "image": "https://replicate.delivery/pbxt/JWIP86XsvRtEVBXAmzKh3WjLm3lxLG8GqFqeSXUNtyLajiQH/screenshot-20230913-175026.png",
      "imgsz": 640,
      "show_conf": true,
      "show_text": true,
      "threshold": 0.53
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-09-13T09:50:40.729478Z",
  "created_at": "2023-09-13T09:50:39.362699Z",
  "data_removed": false,
  "error": null,
  "id": "wdegnhzbf4dvh5cizltkbuzyfa",
  "input": {
    "iou": 0.31,
    "image": "https://replicate.delivery/pbxt/JWIP86XsvRtEVBXAmzKh3WjLm3lxLG8GqFqeSXUNtyLajiQH/screenshot-20230913-175026.png",
    "imgsz": 640,
    "show_conf": true,
    "show_text": true,
    "threshold": 0.53
  },
  "logs": "image /tmp/tmpj3llvi7bscreenshot-20230913-175026.png\nimg_path /tmp/tmpj3llvi7bscreenshot-20230913-175026.png\n0: 640x640 1 ScreenImage, 84.8ms\nSpeed: 3.5ms preprocess, 84.8ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 640)\nresult: ultralytics.engine.results.Results object with attributes:\nboxes: ultralytics.engine.results.Boxes object\nkeypoints: None\nkeys: ['boxes']\nmasks: None\nnames: {0: 'ScreenImage', 1: 'Text'}\norig_img: array([[[  4,   4,   4],\n[  4,   4,   4],\n[  2,   2,   3],\n...,\n[  1,   1,   1],\n[  1,   1,   1],\n[  1,   1,   1]],\n[[  2,   2,   2],\n[  2,   2,   2],\n[  1,   1,   1],\n...,\n[  1,   1,   1],\n[  1,   1,   1],\n[  1,   1,   1]],\n[[  3,   3,   3],\n[  3,   3,   3],\n[  2,   2,   2],\n...,\n[  3,   3,   3],\n[  2,   2,   2],\n[  2,   2,   2]],\n...,\n[[255, 255, 255],\n[255, 255, 255],\n[255, 255, 255],\n...,\n[255, 255, 255],\n[255, 255, 255],\n[255, 255, 255]],\n[[255, 255, 255],\n[255, 255, 255],\n[255, 255, 255],\n...,\n[255, 255, 255],\n[255, 255, 255],\n[255, 255, 255]],\n[[255, 255, 255],\n[255, 255, 255],\n[255, 255, 255],\n...,\n[255, 255, 255],\n[255, 255, 255],\n[255, 255, 255]]], dtype=uint8)\norig_shape: (1298, 1306)\npath: '/tmp/tmpj3llvi7bscreenshot-20230913-175026.png'\nprobs: None\nsave_dir: None\nspeed: {'preprocess': 3.5409927368164062, 'inference': 84.7933292388916, 'postprocess': 2.7608871459960938}",
  "metrics": {
    "predict_time": 1.395366,
    "total_time": 1.366779
  },
  "output": "https://replicate.delivery/pbxt/lNeNKgpmtKRqUicNpV97se2JEpwewVTkbSrenWx4PSlDfbeYE/output_tmpj3llvi7bscreenshot-20230913-175026.png",
  "started_at": "2023-09-13T09:50:39.334112Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/wdegnhzbf4dvh5cizltkbuzyfa",
    "cancel": "https://api.replicate.com/v1/predictions/wdegnhzbf4dvh5cizltkbuzyfa/cancel"
  },
  "version": "4391527954a30359df9e4d610beeb6f98cbaef2207737d3051de98dc366a9b6c"
}

Generated in

1.4 seconds

Tweak itReport

Run time and cost

This model costs approximately $0.00015 to run on Replicate, or 6666 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 CPU hardware. Predictions typically complete within 2 seconds. The predict time for this model varies significantly based on the inputs.