men1scus/birefnet – Run with an API on Replicate

men1scus / birefnet

Bilateral Reference for High-Resolution Dichotomous Image Segmentation (CAAI AIR 2024)

Warm

Public
2.4M runs
A100 (80GB)
GitHub
Paper
License

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=men1scus/birefnet

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 men1scus/birefnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "men1scus/birefnet:f74986db0355b58403ed20963af156525e2891ea3c2d499bfbfb2a28cd87c5d7",
  {
    input: {
      image: "https://replicate.delivery/pbxt/LLcFnHRq5MfjLpWbnLXZEtZIVn1io5sKf2lDnfQrh6tmcFK9/dog.jpg",
      resolution: ""
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", 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 men1scus/birefnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "men1scus/birefnet:f74986db0355b58403ed20963af156525e2891ea3c2d499bfbfb2a28cd87c5d7",
    input={
        "image": "https://replicate.delivery/pbxt/LLcFnHRq5MfjLpWbnLXZEtZIVn1io5sKf2lDnfQrh6tmcFK9/dog.jpg",
        "resolution": ""
    }
)
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 men1scus/birefnet 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": "men1scus/birefnet:f74986db0355b58403ed20963af156525e2891ea3c2d499bfbfb2a28cd87c5d7",
    "input": {
      "image": "https://replicate.delivery/pbxt/LLcFnHRq5MfjLpWbnLXZEtZIVn1io5sKf2lDnfQrh6tmcFK9/dog.jpg",
      "resolution": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

11.6 seconds

Tweak it ShareReport

Examples

View more examples

Run time and cost

This model costs approximately $0.0022 to run on Replicate, or 454 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 Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 2 seconds.

Readme

https://huggingface.co/ZhengPeng7/BiRefNet-legacy

This repo holds the weights of BiRefNet for general use trained on DIS5K-TR,DIS-TEs, DUTS-TR_TE,HRSOD-TR_TE,UHRSD-TR_TE, HRS10K-TR_TE (w/o portrait seg data).