pengdaqian2020/image-tagger – Run with an API on Replicate

pengdaqian2020 / image-tagger

image tagger

Warm

Public
40.5M runs
CPU

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=pengdaqian2020/image-tagger

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 pengdaqian2020/image-tagger using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "pengdaqian2020/image-tagger:5a3e65f223fe2291679a6c3c812ddb278aa6d43bbcf118c09530b4309aaac00e",
  {
    input: {
      image: "https://replicate.delivery/pbxt/Iq6u39HlpVeYwhM3hzWBfbOCVKJJ2LENzNC1ccXrw5SUYAaC/bunny3.webp",
      score_general_threshold: 0.35,
      score_character_threshold: 0.85
    }
  }
);
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 pengdaqian2020/image-tagger using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pengdaqian2020/image-tagger:5a3e65f223fe2291679a6c3c812ddb278aa6d43bbcf118c09530b4309aaac00e",
    input={
        "image": "https://replicate.delivery/pbxt/Iq6u39HlpVeYwhM3hzWBfbOCVKJJ2LENzNC1ccXrw5SUYAaC/bunny3.webp",
        "score_general_threshold": 0.35,
        "score_character_threshold": 0.85
    }
)
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 pengdaqian2020/image-tagger 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": "5a3e65f223fe2291679a6c3c812ddb278aa6d43bbcf118c09530b4309aaac00e",
    "input": {
      "image": "https://replicate.delivery/pbxt/Iq6u39HlpVeYwhM3hzWBfbOCVKJJ2LENzNC1ccXrw5SUYAaC/bunny3.webp",
      "score_general_threshold": 0.35,
      "score_character_threshold": 0.85
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Install Cog

brew install cog

If you don’t have Homebrew, there are other installation options available.

Pull and run pengdaqian2020/image-tagger using Cog (this will download the full model and run it in your local environment):

cog predict r8.im/pengdaqian2020/image-tagger@sha256:5a3e65f223fe2291679a6c3c812ddb278aa6d43bbcf118c09530b4309aaac00e \
  -i 'image="https://replicate.delivery/pbxt/Iq6u39HlpVeYwhM3hzWBfbOCVKJJ2LENzNC1ccXrw5SUYAaC/bunny3.webp"' \
  -i 'score_general_threshold=0.35' \
  -i 'score_character_threshold=0.85'

To learn more, take a look at the Cog documentation.

Pull and run pengdaqian2020/image-tagger using Docker (this will download the full model and run it in your local environment):

docker run -d -p 5000:5000 r8.im/pengdaqian2020/image-tagger@sha256:5a3e65f223fe2291679a6c3c812ddb278aa6d43bbcf118c09530b4309aaac00e
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "image": "https://replicate.delivery/pbxt/Iq6u39HlpVeYwhM3hzWBfbOCVKJJ2LENzNC1ccXrw5SUYAaC/bunny3.webp",
      "score_general_threshold": 0.35,
      "score_character_threshold": 0.85
    }
  }' \
  http://localhost:5000/predictions

Output

tag

blurry

confidence

0.8186014890670776

tag

no_humans

confidence

0.9389649629592896

tag

depth_of_field

confidence

0.5496093034744263

tag

animal

confidence

0.8239761590957642

tag

cat

confidence

0.608523964881897

tag

rabbit

confidence

0.4221939146518707

tag

realistic

confidence

0.7589364647865295

tag

animal_focus

confidence

0.911034345626831

Generated in

1.4 seconds

Tweak it Report

Run time and cost

This model costs approximately $0.0034 to run on Replicate, or 294 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 34 seconds. The predict time for this model varies significantly based on the inputs.

Readme

This model doesn't have a readme.