Prediction

meta/mask2former:75abb8997b4469b1172447f57bc74845b00d8b29f534f9b85dc7fa7be4821b35

Model

meta/mask2former:75abb899

ID

qviwuhmxj5c6xjysqu6tavq6qa

Status

Succeeded

Source

Web

Hardware

–

Total duration

1m 51s

Created

about 3 years ago

Input

image

{
  "image": "https://replicate.delivery/mgxm/a92fcb44-4e31-4031-be49-9a71539a6158/input.jpg"
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run meta/mask2former using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "meta/mask2former:75abb8997b4469b1172447f57bc74845b00d8b29f534f9b85dc7fa7be4821b35",
  {
    input: {
      image: "https://replicate.delivery/mgxm/a92fcb44-4e31-4031-be49-9a71539a6158/input.jpg"
    }
  }
);

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

Import the client:

import replicate

Run meta/mask2former using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "meta/mask2former:75abb8997b4469b1172447f57bc74845b00d8b29f534f9b85dc7fa7be4821b35",
    input={
        "image": "https://replicate.delivery/mgxm/a92fcb44-4e31-4031-be49-9a71539a6158/input.jpg"
    }
)

# The meta/mask2former model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/meta/mask2former/api#output-schema
    print(item)

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

Run meta/mask2former 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": "meta/mask2former:75abb8997b4469b1172447f57bc74845b00d8b29f534f9b85dc7fa7be4821b35",
    "input": {
      "image": "https://replicate.delivery/mgxm/a92fcb44-4e31-4031-be49-9a71539a6158/input.jpg"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

2.9 seconds

Tweak it ShareReport

Prediction

meta/mask2former:75abb8997b4469b1172447f57bc74845b00d8b29f534f9b85dc7fa7be4821b35

Model

meta/mask2former:75abb899

ID

kl7ra44gk5a7xnqmjlrvnnepd4

Status

Succeeded

Source

Web

Hardware

–

Total duration

10.1s

Created

about 3 years ago

Input

image

{
  "image": "https://replicate.delivery/mgxm/a5e4ad1a-56dd-472c-a86a-46b2c3c698f6/desk.jpg"
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run meta/mask2former using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "meta/mask2former:75abb8997b4469b1172447f57bc74845b00d8b29f534f9b85dc7fa7be4821b35",
  {
    input: {
      image: "https://replicate.delivery/mgxm/a5e4ad1a-56dd-472c-a86a-46b2c3c698f6/desk.jpg"
    }
  }
);

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

Import the client:

import replicate

Run meta/mask2former using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "meta/mask2former:75abb8997b4469b1172447f57bc74845b00d8b29f534f9b85dc7fa7be4821b35",
    input={
        "image": "https://replicate.delivery/mgxm/a5e4ad1a-56dd-472c-a86a-46b2c3c698f6/desk.jpg"
    }
)

# The meta/mask2former model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/meta/mask2former/api#output-schema
    print(item)

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

Run meta/mask2former 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": "meta/mask2former:75abb8997b4469b1172447f57bc74845b00d8b29f534f9b85dc7fa7be4821b35",
    "input": {
      "image": "https://replicate.delivery/mgxm/a5e4ad1a-56dd-472c-a86a-46b2c3c698f6/desk.jpg"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

9.9 seconds

Tweak it ShareReport

Prediction

meta/mask2former:75abb8997b4469b1172447f57bc74845b00d8b29f534f9b85dc7fa7be4821b35

Model

meta/mask2former:75abb899

ID

cinnh62wn5a4pa2nwebjfkq2ry

Status

Succeeded

Source

Web

Hardware

–

Total duration

3.9s

Created

about 3 years ago

Input

image

{
  "image": "https://replicate.delivery/mgxm/4209a48a-1c86-4ab0-ac96-7047d68910a9/k67kjlB.jpeg"
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run meta/mask2former using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "meta/mask2former:75abb8997b4469b1172447f57bc74845b00d8b29f534f9b85dc7fa7be4821b35",
  {
    input: {
      image: "https://replicate.delivery/mgxm/4209a48a-1c86-4ab0-ac96-7047d68910a9/k67kjlB.jpeg"
    }
  }
);

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

Import the client:

import replicate

Run meta/mask2former using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "meta/mask2former:75abb8997b4469b1172447f57bc74845b00d8b29f534f9b85dc7fa7be4821b35",
    input={
        "image": "https://replicate.delivery/mgxm/4209a48a-1c86-4ab0-ac96-7047d68910a9/k67kjlB.jpeg"
    }
)

# The meta/mask2former model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/meta/mask2former/api#output-schema
    print(item)

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

Run meta/mask2former 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": "meta/mask2former:75abb8997b4469b1172447f57bc74845b00d8b29f534f9b85dc7fa7be4821b35",
    "input": {
      "image": "https://replicate.delivery/mgxm/4209a48a-1c86-4ab0-ac96-7047d68910a9/k67kjlB.jpeg"
    }
  }' \
  https://api.replicate.com/v1/predictions

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