fire/v-sekai.mediapipe-labeler:aa584fa4 – Run with an API on Replicate

Version

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fire /v-sekai.mediapipe-labeler:aa584fa4

Playground API

Input

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

npx create-replicate --model=fire/v-sekai.mediapipe-labeler

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 fire/v-sekai.mediapipe-labeler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fire/v-sekai.mediapipe-labeler:aa584fa4350829ea99b34753defdfeffa0a74c76417286ed0ce01b0b02ecf78b",
  {
    input: {
      test_mode: false,
      max_people: 100,
      export_train: true,
      frame_sample_rate: 1
    }
  }
);
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 fire/v-sekai.mediapipe-labeler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fire/v-sekai.mediapipe-labeler:aa584fa4350829ea99b34753defdfeffa0a74c76417286ed0ce01b0b02ecf78b",
    input={
        "test_mode": False,
        "max_people": 100,
        "export_train": True,
        "frame_sample_rate": 1
    }
)
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 fire/v-sekai.mediapipe-labeler 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": "aa584fa4350829ea99b34753defdfeffa0a74c76417286ed0ce01b0b02ecf78b",
    "input": {
      "test_mode": false,
      "max_people": 100,
      "export_train": true,
      "frame_sample_rate": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

You can run this model locally using Cog. First, install Cog:

brew install cog

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

Run this to download the model and run it in your local environment:

cog predict r8.im/fire/v-sekai.mediapipe-labeler@sha256:aa584fa4350829ea99b34753defdfeffa0a74c76417286ed0ce01b0b02ecf78b \
  -i 'test_mode=false' \
  -i 'max_people=100' \
  -i 'export_train=true' \
  -i 'frame_sample_rate=1'

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

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 r8.im/fire/v-sekai.mediapipe-labeler@sha256:aa584fa4350829ea99b34753defdfeffa0a74c76417286ed0ce01b0b02ecf78b
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "test_mode": false,
      "max_people": 100,
      "export_train": true,
      "frame_sample_rate": 1
    }
  }' \
  http://localhost:5000/predictions

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

Output

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