Prediction

ID

kfj6p5fyoza4dp6l2zcvyekl5u

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago by @andreasjansson

Input

image
model: coco
use_beam_search

{
  "image": "https://replicate.delivery/mgxm/4dc7763a-f234-4a7c-a85f-cb9e05e37cf8/COCO_val2014_000000579664.jpg",
  "model": "coco",
  "use_beam_search": false
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
  {
    input: {
      image: "https://replicate.delivery/mgxm/4dc7763a-f234-4a7c-a85f-cb9e05e37cf8/COCO_val2014_000000579664.jpg",
      model: "coco",
      use_beam_search: false
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    input={
        "image": "https://replicate.delivery/mgxm/4dc7763a-f234-4a7c-a85f-cb9e05e37cf8/COCO_val2014_000000579664.jpg",
        "model": "coco",
        "use_beam_search": False
    }
)

# The rmokady/clip_prefix_caption 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/rmokady/clip_prefix_caption/api#output-schema
    print(item)

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

Run rmokady/clip_prefix_caption 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": "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    "input": {
      "image": "https://replicate.delivery/mgxm/4dc7763a-f234-4a7c-a85f-cb9e05e37cf8/COCO_val2014_000000579664.jpg",
      "model": "coco",
      "use_beam_search": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

A bunch of bananas sitting on top of a table.

rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0

Prediction

Model

ID

ewv35dttpnhcvmftflpfer4eem

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

image
model: coco
use_beam_search

{
  "image": "https://replicate.delivery/mgxm/e9270f77-ebdd-44c9-a5a7-750b817d9291/COCO_val2014_000000060623.jpg",
  "model": "coco",
  "use_beam_search": false
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
  {
    input: {
      image: "https://replicate.delivery/mgxm/e9270f77-ebdd-44c9-a5a7-750b817d9291/COCO_val2014_000000060623.jpg",
      model: "coco",
      use_beam_search: false
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    input={
        "image": "https://replicate.delivery/mgxm/e9270f77-ebdd-44c9-a5a7-750b817d9291/COCO_val2014_000000060623.jpg",
        "model": "coco",
        "use_beam_search": False
    }
)

# The rmokady/clip_prefix_caption 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/rmokady/clip_prefix_caption/api#output-schema
    print(item)

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

Run rmokady/clip_prefix_caption 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": "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    "input": {
      "image": "https://replicate.delivery/mgxm/e9270f77-ebdd-44c9-a5a7-750b817d9291/COCO_val2014_000000060623.jpg",
      "model": "coco",
      "use_beam_search": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

A woman is eating a piece of cake with a candle.

rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0

Prediction

Model

ID

hopopplhszeezcbos4mrk6lgre

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

image
model: coco
use_beam_search

{
  "image": "https://replicate.delivery/mgxm/73429dbf-1254-4e19-99c7-5cab7945f8c2/COCO_val2014_000000562207.jpg",
  "model": "coco",
  "use_beam_search": false
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
  {
    input: {
      image: "https://replicate.delivery/mgxm/73429dbf-1254-4e19-99c7-5cab7945f8c2/COCO_val2014_000000562207.jpg",
      model: "coco",
      use_beam_search: false
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    input={
        "image": "https://replicate.delivery/mgxm/73429dbf-1254-4e19-99c7-5cab7945f8c2/COCO_val2014_000000562207.jpg",
        "model": "coco",
        "use_beam_search": False
    }
)

# The rmokady/clip_prefix_caption 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/rmokady/clip_prefix_caption/api#output-schema
    print(item)

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

Run rmokady/clip_prefix_caption 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": "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    "input": {
      "image": "https://replicate.delivery/mgxm/73429dbf-1254-4e19-99c7-5cab7945f8c2/COCO_val2014_000000562207.jpg",
      "model": "coco",
      "use_beam_search": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

A couple of people standing next to an elephant.

rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0

Prediction

Model

ID

dn5k5uosand3leo5apyacydg2y

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

image
model: coco
use_beam_search

{
  "image": "https://replicate.delivery/mgxm/ef235bdc-d2dd-466a-b356-9e3225aad9be/COCO_val2014_000000165547.jpg",
  "model": "coco",
  "use_beam_search": false
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
  {
    input: {
      image: "https://replicate.delivery/mgxm/ef235bdc-d2dd-466a-b356-9e3225aad9be/COCO_val2014_000000165547.jpg",
      model: "coco",
      use_beam_search: false
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    input={
        "image": "https://replicate.delivery/mgxm/ef235bdc-d2dd-466a-b356-9e3225aad9be/COCO_val2014_000000165547.jpg",
        "model": "coco",
        "use_beam_search": False
    }
)

# The rmokady/clip_prefix_caption 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/rmokady/clip_prefix_caption/api#output-schema
    print(item)

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

Run rmokady/clip_prefix_caption 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": "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    "input": {
      "image": "https://replicate.delivery/mgxm/ef235bdc-d2dd-466a-b356-9e3225aad9be/COCO_val2014_000000165547.jpg",
      "model": "coco",
      "use_beam_search": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

A dining table and chairs in a room.

rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0

Prediction

Model

ID

rwlidpfkbzfmpcsnzwei4maivm

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

image
model: coco
use_beam_search

{
  "image": "https://replicate.delivery/mgxm/1000c3e5-c3f2-4a44-9ad3-8c33f59bb3a6/COCO_val2014_000000354533.jpg",
  "model": "coco",
  "use_beam_search": false
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
  {
    input: {
      image: "https://replicate.delivery/mgxm/1000c3e5-c3f2-4a44-9ad3-8c33f59bb3a6/COCO_val2014_000000354533.jpg",
      model: "coco",
      use_beam_search: false
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    input={
        "image": "https://replicate.delivery/mgxm/1000c3e5-c3f2-4a44-9ad3-8c33f59bb3a6/COCO_val2014_000000354533.jpg",
        "model": "coco",
        "use_beam_search": False
    }
)

# The rmokady/clip_prefix_caption 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/rmokady/clip_prefix_caption/api#output-schema
    print(item)

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

Run rmokady/clip_prefix_caption 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": "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    "input": {
      "image": "https://replicate.delivery/mgxm/1000c3e5-c3f2-4a44-9ad3-8c33f59bb3a6/COCO_val2014_000000354533.jpg",
      "model": "coco",
      "use_beam_search": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

A motorcycle parked on a dirt road near a fence.

rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0

Prediction

Model

ID

u3rqaoxfcjc7bgs7e32izyhn3a

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

image
model: coco
use_beam_search

{
  "image": "https://replicate.delivery/mgxm/717831f6-cac5-43bd-9ba2-3714d3d1e3d8/COCO_val2014_000000386164.jpg",
  "model": "coco",
  "use_beam_search": false
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
  {
    input: {
      image: "https://replicate.delivery/mgxm/717831f6-cac5-43bd-9ba2-3714d3d1e3d8/COCO_val2014_000000386164.jpg",
      model: "coco",
      use_beam_search: false
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    input={
        "image": "https://replicate.delivery/mgxm/717831f6-cac5-43bd-9ba2-3714d3d1e3d8/COCO_val2014_000000386164.jpg",
        "model": "coco",
        "use_beam_search": False
    }
)

# The rmokady/clip_prefix_caption 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/rmokady/clip_prefix_caption/api#output-schema
    print(item)

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

Run rmokady/clip_prefix_caption 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": "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    "input": {
      "image": "https://replicate.delivery/mgxm/717831f6-cac5-43bd-9ba2-3714d3d1e3d8/COCO_val2014_000000386164.jpg",
      "model": "coco",
      "use_beam_search": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

A row of wooden cutting boards with wooden spoons.

rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0

Prediction

Model

ID

cz4q2c6pn5hnjiitl434dkspla

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

image
model: conceptual-captions
use_beam_search

{
  "image": "https://replicate.delivery/mgxm/ba2bb931-4b66-4ba9-a60a-1f97ea490fd6/COCO_val2014_000000386164.jpg",
  "model": "conceptual-captions",
  "use_beam_search": false
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
  {
    input: {
      image: "https://replicate.delivery/mgxm/ba2bb931-4b66-4ba9-a60a-1f97ea490fd6/COCO_val2014_000000386164.jpg",
      model: "conceptual-captions",
      use_beam_search: false
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    input={
        "image": "https://replicate.delivery/mgxm/ba2bb931-4b66-4ba9-a60a-1f97ea490fd6/COCO_val2014_000000386164.jpg",
        "model": "conceptual-captions",
        "use_beam_search": False
    }
)

# The rmokady/clip_prefix_caption 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/rmokady/clip_prefix_caption/api#output-schema
    print(item)

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

Run rmokady/clip_prefix_caption 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": "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    "input": {
      "image": "https://replicate.delivery/mgxm/ba2bb931-4b66-4ba9-a60a-1f97ea490fd6/COCO_val2014_000000386164.jpg",
      "model": "conceptual-captions",
      "use_beam_search": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

wooden spoons on a wooden table.

rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0

Prediction

Model

ID

cols6f74xbgczcgbwk5ccmty5q

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

image
model: conceptual-captions
use_beam_search

{
  "image": "https://replicate.delivery/mgxm/9057f2c5-b59e-43e6-849b-a17797f1280d/CONCEPTUAL_01.jpg",
  "model": "conceptual-captions",
  "use_beam_search": false
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
  {
    input: {
      image: "https://replicate.delivery/mgxm/9057f2c5-b59e-43e6-849b-a17797f1280d/CONCEPTUAL_01.jpg",
      model: "conceptual-captions",
      use_beam_search: false
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    input={
        "image": "https://replicate.delivery/mgxm/9057f2c5-b59e-43e6-849b-a17797f1280d/CONCEPTUAL_01.jpg",
        "model": "conceptual-captions",
        "use_beam_search": False
    }
)

# The rmokady/clip_prefix_caption 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/rmokady/clip_prefix_caption/api#output-schema
    print(item)

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

Run rmokady/clip_prefix_caption 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": "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    "input": {
      "image": "https://replicate.delivery/mgxm/9057f2c5-b59e-43e6-849b-a17797f1280d/CONCEPTUAL_01.jpg",
      "model": "conceptual-captions",
      "use_beam_search": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

3d render of a man holding a globe.

rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0

Prediction

Model

ID

x4etf525kfa6hamm66jkqn6mqi

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

image
model: conceptual-captions
use_beam_search

{
  "image": "https://replicate.delivery/mgxm/404e8b7c-53bf-4f2c-8bb1-79cf9cf2aabc/CONCEPTUAL_02.jpg",
  "model": "conceptual-captions",
  "use_beam_search": false
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
  {
    input: {
      image: "https://replicate.delivery/mgxm/404e8b7c-53bf-4f2c-8bb1-79cf9cf2aabc/CONCEPTUAL_02.jpg",
      model: "conceptual-captions",
      use_beam_search: false
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    input={
        "image": "https://replicate.delivery/mgxm/404e8b7c-53bf-4f2c-8bb1-79cf9cf2aabc/CONCEPTUAL_02.jpg",
        "model": "conceptual-captions",
        "use_beam_search": False
    }
)

# The rmokady/clip_prefix_caption 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/rmokady/clip_prefix_caption/api#output-schema
    print(item)

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

Run rmokady/clip_prefix_caption 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": "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    "input": {
      "image": "https://replicate.delivery/mgxm/404e8b7c-53bf-4f2c-8bb1-79cf9cf2aabc/CONCEPTUAL_02.jpg",
      "model": "conceptual-captions",
      "use_beam_search": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

students enjoying the cherry blossoms.

rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0

Prediction

Model

ID

hx4lb2muwjfnra4lbdo4relojy

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

image
model: conceptual-captions
use_beam_search

{
  "image": "https://replicate.delivery/mgxm/284cdf4f-a0fb-4afc-b9b8-5006fd9e7fd5/CONCEPTUAL_03.jpg",
  "model": "conceptual-captions",
  "use_beam_search": false
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
  {
    input: {
      image: "https://replicate.delivery/mgxm/284cdf4f-a0fb-4afc-b9b8-5006fd9e7fd5/CONCEPTUAL_03.jpg",
      model: "conceptual-captions",
      use_beam_search: false
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    input={
        "image": "https://replicate.delivery/mgxm/284cdf4f-a0fb-4afc-b9b8-5006fd9e7fd5/CONCEPTUAL_03.jpg",
        "model": "conceptual-captions",
        "use_beam_search": False
    }
)

# The rmokady/clip_prefix_caption 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/rmokady/clip_prefix_caption/api#output-schema
    print(item)

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

Run rmokady/clip_prefix_caption 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": "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    "input": {
      "image": "https://replicate.delivery/mgxm/284cdf4f-a0fb-4afc-b9b8-5006fd9e7fd5/CONCEPTUAL_03.jpg",
      "model": "conceptual-captions",
      "use_beam_search": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

green leaf of lettuce on a white plate.

rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0

Prediction

Model

ID

63iqnqsfyjbjzenj7xj7g47knq

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

image
model: conceptual-captions
use_beam_search

{
  "image": "https://replicate.delivery/mgxm/a6ede1ae-e2fe-43fe-b438-ba31eae65c43/CONCEPTUAL_04.jpg",
  "model": "conceptual-captions",
  "use_beam_search": false
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
  {
    input: {
      image: "https://replicate.delivery/mgxm/a6ede1ae-e2fe-43fe-b438-ba31eae65c43/CONCEPTUAL_04.jpg",
      model: "conceptual-captions",
      use_beam_search: false
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    input={
        "image": "https://replicate.delivery/mgxm/a6ede1ae-e2fe-43fe-b438-ba31eae65c43/CONCEPTUAL_04.jpg",
        "model": "conceptual-captions",
        "use_beam_search": False
    }
)

# The rmokady/clip_prefix_caption 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/rmokady/clip_prefix_caption/api#output-schema
    print(item)

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

Run rmokady/clip_prefix_caption 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": "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    "input": {
      "image": "https://replicate.delivery/mgxm/a6ede1ae-e2fe-43fe-b438-ba31eae65c43/CONCEPTUAL_04.jpg",
      "model": "conceptual-captions",
      "use_beam_search": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

the hotel and casino on the waterfront.

rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0

Prediction

Model

ID

ek6xau3alzhmxjph2twglbf6pq

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

image
model: conceptual-captions
use_beam_search

{
  "image": "https://replicate.delivery/mgxm/e14d4856-4a0f-409c-b89a-5be38f97dbbe/CONCEPTUAL_05.jpg",
  "model": "conceptual-captions",
  "use_beam_search": false
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
  {
    input: {
      image: "https://replicate.delivery/mgxm/e14d4856-4a0f-409c-b89a-5be38f97dbbe/CONCEPTUAL_05.jpg",
      model: "conceptual-captions",
      use_beam_search: false
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    input={
        "image": "https://replicate.delivery/mgxm/e14d4856-4a0f-409c-b89a-5be38f97dbbe/CONCEPTUAL_05.jpg",
        "model": "conceptual-captions",
        "use_beam_search": False
    }
)

# The rmokady/clip_prefix_caption 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/rmokady/clip_prefix_caption/api#output-schema
    print(item)

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

Run rmokady/clip_prefix_caption 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": "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    "input": {
      "image": "https://replicate.delivery/mgxm/e14d4856-4a0f-409c-b89a-5be38f97dbbe/CONCEPTUAL_05.jpg",
      "model": "conceptual-captions",
      "use_beam_search": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

the triangle is a symbol of the soul.

rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0

Prediction

Model

ID

wwvvdbyjvjbobc5fx4qffcem4a

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

image
model: conceptual-captions
use_beam_search

{
  "image": "https://replicate.delivery/mgxm/d699e131-b9c1-4a9a-964a-2739c525218b/CONCEPTUAL_06.jpg",
  "model": "conceptual-captions",
  "use_beam_search": false
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
  {
    input: {
      image: "https://replicate.delivery/mgxm/d699e131-b9c1-4a9a-964a-2739c525218b/CONCEPTUAL_06.jpg",
      model: "conceptual-captions",
      use_beam_search: false
    }
  }
);

console.log(output);

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

pip install replicate

Import the client:

import replicate

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

output = replicate.run(
    "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    input={
        "image": "https://replicate.delivery/mgxm/d699e131-b9c1-4a9a-964a-2739c525218b/CONCEPTUAL_06.jpg",
        "model": "conceptual-captions",
        "use_beam_search": False
    }
)

# The rmokady/clip_prefix_caption 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/rmokady/clip_prefix_caption/api#output-schema
    print(item)

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

Run rmokady/clip_prefix_caption 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": "rmokady/clip_prefix_caption:d703881e7b50eb009779b7e1e79d394639df0af71be28c4100f88c03fdccdbf0",
    "input": {
      "image": "https://replicate.delivery/mgxm/d699e131-b9c1-4a9a-964a-2739c525218b/CONCEPTUAL_06.jpg",
      "model": "conceptual-captions",
      "use_beam_search": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

cartoon boy in the bath.