Prediction

ID

3bp2kcpwxnfnfehqdebqoniblq

Status

Succeeded

Source

Web

Hardware

–

Total duration

2m 16s

Created

almost 3 years ago by @vccheng2001

Input

image
model: qnrf

{
  "image": "https://replicate.delivery/mgxm/f56eaeb5-246c-4d03-ba4f-2d2ec352e19d/3.png",
  "model": "qnrf"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run cvlab-stonybrook/crowd-counting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d",
  {
    input: {
      image: "https://replicate.delivery/mgxm/f56eaeb5-246c-4d03-ba4f-2d2ec352e19d/3.png",
      model: "qnrf"
    }
  }
);

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 cvlab-stonybrook/crowd-counting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d",
    input={
        "image": "https://replicate.delivery/mgxm/f56eaeb5-246c-4d03-ba4f-2d2ec352e19d/3.png",
        "model": "qnrf"
    }
)
print(output)

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

Run cvlab-stonybrook/crowd-counting 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": "cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d",
    "input": {
      "image": "https://replicate.delivery/mgxm/f56eaeb5-246c-4d03-ba4f-2d2ec352e19d/3.png",
      "model": "qnrf"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

count

692

output

Generated in

11.3 seconds

cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d

Prediction

Model

ID

ehjukb6u25g2th7mx7yl6zkucu

Status

Succeeded

Source

Web

Hardware

–

Total duration

9.4s

Created

almost 3 years ago

Input

image
model: qnrf

{
  "image": "https://replicate.delivery/mgxm/9f60e190-d916-4bb3-8791-36c2a731706c/2.png",
  "model": "qnrf"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run cvlab-stonybrook/crowd-counting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d",
  {
    input: {
      image: "https://replicate.delivery/mgxm/9f60e190-d916-4bb3-8791-36c2a731706c/2.png",
      model: "qnrf"
    }
  }
);

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 cvlab-stonybrook/crowd-counting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d",
    input={
        "image": "https://replicate.delivery/mgxm/9f60e190-d916-4bb3-8791-36c2a731706c/2.png",
        "model": "qnrf"
    }
)
print(output)

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

Run cvlab-stonybrook/crowd-counting 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": "cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d",
    "input": {
      "image": "https://replicate.delivery/mgxm/9f60e190-d916-4bb3-8791-36c2a731706c/2.png",
      "model": "qnrf"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

count

481

output

Generated in

9.3 seconds

cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d

Prediction

Model

ID

tf5iif5o7rdyzho6mkll5vm4lu

Status

Succeeded

Source

Web

Hardware

–

Total duration

4.0s

Created

almost 3 years ago

Input

image
model: nwpu

{
  "image": "https://replicate.delivery/mgxm/c567d23f-7ee0-4c8d-bd35-7f52230033e1/Screen_Shot_2022-06-27_at_11.25.08_AM.png",
  "model": "nwpu"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run cvlab-stonybrook/crowd-counting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d",
  {
    input: {
      image: "https://replicate.delivery/mgxm/c567d23f-7ee0-4c8d-bd35-7f52230033e1/Screen_Shot_2022-06-27_at_11.25.08_AM.png",
      model: "nwpu"
    }
  }
);

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 cvlab-stonybrook/crowd-counting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d",
    input={
        "image": "https://replicate.delivery/mgxm/c567d23f-7ee0-4c8d-bd35-7f52230033e1/Screen_Shot_2022-06-27_at_11.25.08_AM.png",
        "model": "nwpu"
    }
)
print(output)

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

Run cvlab-stonybrook/crowd-counting 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": "cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d",
    "input": {
      "image": "https://replicate.delivery/mgxm/c567d23f-7ee0-4c8d-bd35-7f52230033e1/Screen_Shot_2022-06-27_at_11.25.08_AM.png",
      "model": "nwpu"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

count

86

output

Generated in

3.9 seconds

cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d

Prediction

Model

ID

rrof36537zdevfnnsyqq3ykteu

Status

Succeeded

Source

Web

Hardware

–

Total duration

10.2s

Created

almost 3 years ago

Input

image
model: sh_A

{
  "image": "https://replicate.delivery/mgxm/2bfacf5d-1487-4b97-89a6-98016e121183/shutterstock_634724363.jpeg",
  "model": "sh_A"
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run cvlab-stonybrook/crowd-counting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d",
  {
    input: {
      image: "https://replicate.delivery/mgxm/2bfacf5d-1487-4b97-89a6-98016e121183/shutterstock_634724363.jpeg",
      model: "sh_A"
    }
  }
);

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 cvlab-stonybrook/crowd-counting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d",
    input={
        "image": "https://replicate.delivery/mgxm/2bfacf5d-1487-4b97-89a6-98016e121183/shutterstock_634724363.jpeg",
        "model": "sh_A"
    }
)
print(output)

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

Run cvlab-stonybrook/crowd-counting 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": "cvlab-stonybrook/crowd-counting:8bd65590d146aa68522ed5ef2a8b7b19ba166efd1f5bc0543d14356f391d380d",
    "input": {
      "image": "https://replicate.delivery/mgxm/2bfacf5d-1487-4b97-89a6-98016e121183/shutterstock_634724363.jpeg",
      "model": "sh_A"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

count

21

output

Generated in

10.1 seconds