lucataco / watermark_detector

amrul-hzz's fine-tuned version of vit-base-patch16-224-in21k for watermark image detection (Updated 1 year, 4 months ago)

Cold

Public
355 runs
CPU
GitHub
Paper

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Prediction

lucataco/watermark_detector:0a94805ee1591e251590bddc8bdd989451c66b8ea99c97d762708fe5a5b66391

Model

lucataco/watermark_detector:0a94805e

thrm5vbbocvy54g2qc7v2dxrra

Status

Succeeded

Source

Web

Hardware

CPU

Total duration

2m 13s

Created

over 1 year ago

Input

image

{
  "image": "https://replicate.delivery/pbxt/KILJGjmrSutHFh6cfxSGm4RMNEI1vOCw6aoYWf3DMuRKuNPg/replicate-prediction-vbb5733b72rj2o3akgzbbowa3y.png"
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "lucataco/watermark_detector:0a94805ee1591e251590bddc8bdd989451c66b8ea99c97d762708fe5a5b66391",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KILJGjmrSutHFh6cfxSGm4RMNEI1vOCw6aoYWf3DMuRKuNPg/replicate-prediction-vbb5733b72rj2o3akgzbbowa3y.png"
    }
  }
);

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

Import the client:

import replicate

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

output = replicate.run(
    "lucataco/watermark_detector:0a94805ee1591e251590bddc8bdd989451c66b8ea99c97d762708fe5a5b66391",
    input={
        "image": "https://replicate.delivery/pbxt/KILJGjmrSutHFh6cfxSGm4RMNEI1vOCw6aoYWf3DMuRKuNPg/replicate-prediction-vbb5733b72rj2o3akgzbbowa3y.png"
    }
)
print(output)

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

Run lucataco/watermark_detector 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": "lucataco/watermark_detector:0a94805ee1591e251590bddc8bdd989451c66b8ea99c97d762708fe5a5b66391",
    "input": {
      "image": "https://replicate.delivery/pbxt/KILJGjmrSutHFh6cfxSGm4RMNEI1vOCw6aoYWf3DMuRKuNPg/replicate-prediction-vbb5733b72rj2o3akgzbbowa3y.png"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/lucataco/watermark_detector@sha256:0a94805ee1591e251590bddc8bdd989451c66b8ea99c97d762708fe5a5b66391 \
  -i 'image="https://replicate.delivery/pbxt/KILJGjmrSutHFh6cfxSGm4RMNEI1vOCw6aoYWf3DMuRKuNPg/replicate-prediction-vbb5733b72rj2o3akgzbbowa3y.png"'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 r8.im/lucataco/watermark_detector@sha256:0a94805ee1591e251590bddc8bdd989451c66b8ea99c97d762708fe5a5b66391
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "image": "https://replicate.delivery/pbxt/KILJGjmrSutHFh6cfxSGm4RMNEI1vOCw6aoYWf3DMuRKuNPg/replicate-prediction-vbb5733b72rj2o3akgzbbowa3y.png"
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

Predicted:watermark

Generated in

0.6 seconds

Tweak it Share Report

Prediction

lucataco/watermark_detector:0a94805ee1591e251590bddc8bdd989451c66b8ea99c97d762708fe5a5b66391

Model

lucataco/watermark_detector:0a94805e

ty3ie3zbyzmznougoihv7hpily

Status

Succeeded

Source

Web

Hardware

CPU

Total duration

0.6s

Created

over 1 year ago

Input

image

{
  "image": "https://replicate.delivery/pbxt/KILM05JByD1VlXcSCqv0249RIsK2V2FXieWzdb6LZnwPOmqm/cats.jpg"
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "lucataco/watermark_detector:0a94805ee1591e251590bddc8bdd989451c66b8ea99c97d762708fe5a5b66391",
  {
    input: {
      image: "https://replicate.delivery/pbxt/KILM05JByD1VlXcSCqv0249RIsK2V2FXieWzdb6LZnwPOmqm/cats.jpg"
    }
  }
);

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

Import the client:

import replicate

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

output = replicate.run(
    "lucataco/watermark_detector:0a94805ee1591e251590bddc8bdd989451c66b8ea99c97d762708fe5a5b66391",
    input={
        "image": "https://replicate.delivery/pbxt/KILM05JByD1VlXcSCqv0249RIsK2V2FXieWzdb6LZnwPOmqm/cats.jpg"
    }
)
print(output)

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

Run lucataco/watermark_detector 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": "lucataco/watermark_detector:0a94805ee1591e251590bddc8bdd989451c66b8ea99c97d762708fe5a5b66391",
    "input": {
      "image": "https://replicate.delivery/pbxt/KILM05JByD1VlXcSCqv0249RIsK2V2FXieWzdb6LZnwPOmqm/cats.jpg"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/lucataco/watermark_detector@sha256:0a94805ee1591e251590bddc8bdd989451c66b8ea99c97d762708fe5a5b66391 \
  -i 'image="https://replicate.delivery/pbxt/KILM05JByD1VlXcSCqv0249RIsK2V2FXieWzdb6LZnwPOmqm/cats.jpg"'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 r8.im/lucataco/watermark_detector@sha256:0a94805ee1591e251590bddc8bdd989451c66b8ea99c97d762708fe5a5b66391
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "image": "https://replicate.delivery/pbxt/KILM05JByD1VlXcSCqv0249RIsK2V2FXieWzdb6LZnwPOmqm/cats.jpg"
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.