meta / detic

Detects any class given class names

Public
28K runs
T4
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=meta/detic

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

const output = await replicate.run(
  "meta/detic:37fbaca39b3185aa297903062fcab7248cada248ddd0c70bf01622dffc8d16bb",
  {
    input: {
      image: "https://replicate.delivery/mgxm/d4825bcc-d07f-4c85-91d4-b3a5b14067aa/k67kjlB.jpeg",
      vocabulary: "lvis",
      custom_vocabulary: ""
    }
  }
);

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

output = replicate.run(
    "meta/detic:37fbaca39b3185aa297903062fcab7248cada248ddd0c70bf01622dffc8d16bb",
    input={
        "image": "https://replicate.delivery/mgxm/d4825bcc-d07f-4c85-91d4-b3a5b14067aa/k67kjlB.jpeg",
        "vocabulary": "lvis",
        "custom_vocabulary": ""
    }
)
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 meta/detic 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/detic:37fbaca39b3185aa297903062fcab7248cada248ddd0c70bf01622dffc8d16bb",
    "input": {
      "image": "https://replicate.delivery/mgxm/d4825bcc-d07f-4c85-91d4-b3a5b14067aa/k67kjlB.jpeg",
      "vocabulary": "lvis",
      "custom_vocabulary": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

file

Generated in

3.0 seconds

This output was created using a different version of the model, meta/detic:dd886d17.

Examples

View more examples

Run time and cost

This model costs approximately $0.00049 to run on Replicate, or 2040 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 3 seconds.

Readme

Detecting Twenty-thousand Classes using Image-level Supervision

Detic: A Detector with image classes that can use image-level labels to easily train detectors.

Detecting Twenty-thousand Classes using Image-level Supervision,
Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krähenbühl, Ishan Misra,
arXiv technical report (arXiv 2201.02605)

Features

-Detects any class given class names (using CLIP).

-We train the detector on ImageNet-21K dataset with 21K classes.

-Cross-dataset generalization to OpenImages and Objects365 without finetuning.

-State-of-the-art results on Open-vocabulary LVIS and Open-vocabulary COCO.

-Works for DETR-style detectors.

License

The majority of Detic is licensed under the Apache 2.0 license, however portions of the project are available under separate license terms: SWIN-Transformer, CLIP, and TensorFlow Object Detection API are licensed under the MIT license; UniDet is licensed under the Apache 2.0 license; and the LVIS API is licensed under a custom license (https://github.com/lvis-dataset/lvis-api/blob/master/LICENSE)” If you later add other third party code, please keep this license info updated, and please let us know if that component is licensed under something other than CC-BY-NC, MIT, or CC0

Ethical Considerations

Detic’s wide range of detection capabilities may introduce similar challenges to many other visual recognition and open-set recognition methods. As the user can define arbitrary detection classes, class design and semantics may impact the model output.

Citation

If you find this project useful for your research, please use the following BibTeX entry.

@inproceedings{zhou2021detecting,
  title={Detecting Twenty-thousand Classes using Image-level Supervision},
  author={Zhou, Xingyi and Girdhar, Rohit and Joulin, Armand and Kr{\"a}henb{\"u}hl, Philipp and Misra, Ishan},
  booktitle={arXiv preprint arXiv:2201.02605},
  year={2021}
}