Input

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

npx create-replicate --model=cjwbw/zoedepth

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

const output = await replicate.run(
  "cjwbw/zoedepth:6375723d97400d3ac7b88e3022b738bf6f433ae165c4a2acd1955eaa6b8fcb62",
  {
    input: {
      image: "https://replicate.delivery/pbxt/IPzzqLRb2x6XwGUK28l7dNTFO9MzQG1WmY2sdapZ2tnEdmMF/123.png",
      model_type: "ZoeD_N"
    }
  }
);
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 cjwbw/zoedepth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cjwbw/zoedepth:6375723d97400d3ac7b88e3022b738bf6f433ae165c4a2acd1955eaa6b8fcb62",
    input={
        "image": "https://replicate.delivery/pbxt/IPzzqLRb2x6XwGUK28l7dNTFO9MzQG1WmY2sdapZ2tnEdmMF/123.png",
        "model_type": "ZoeD_N"
    }
)
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 cjwbw/zoedepth 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": "6375723d97400d3ac7b88e3022b738bf6f433ae165c4a2acd1955eaa6b8fcb62",
    "input": {
      "image": "https://replicate.delivery/pbxt/IPzzqLRb2x6XwGUK28l7dNTFO9MzQG1WmY2sdapZ2tnEdmMF/123.png",
      "model_type": "ZoeD_N"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

8.1 seconds

Tweak itReport

Examples

View more examples

Run time and cost

This model costs approximately $0.00035 to run on Replicate, or 2857 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 T4 GPU hardware. Predictions typically complete within 2 seconds. The predict time for this model varies significantly based on the inputs.

Readme

ZoeDepth: Combining relative and metric depth (Official implementation)

ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth

Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, Matthias Müller

teaser

Citation

@misc{https://doi.org/10.48550/arxiv.2302.12288,
  doi = {10.48550/ARXIV.2302.12288},

  url = {https://arxiv.org/abs/2302.12288},

  author = {Bhat, Shariq Farooq and Birkl, Reiner and Wofk, Diana and Wonka, Peter and Müller, Matthias},

  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},

  title = {ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth},

  publisher = {arXiv},

  year = {2023},

  copyright = {arXiv.org perpetual, non-exclusive license}
}