winycg/anchor_net | Run with an API on Replicate

Input

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

npx create-replicate --model=winycg/anchor_net

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

const output = await replicate.run(
  "winycg/anchor_net:e361854bcb889339416728e464bf6ae7680ae5ccfbc0ed471e949ec038dd8e25",
  {
    input: {
      input_image: "https://replicate.delivery/mgxm/cf21baaf-55c3-49dd-a5e9-c723eaac5fd5/ILSVRC2012_val_00023440.JPEG",
      output_format: "Final-probs-plot",
      iou_thresholds: "0.0, 0.1"
    }
  }
);

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

output = replicate.run(
    "winycg/anchor_net:e361854bcb889339416728e464bf6ae7680ae5ccfbc0ed471e949ec038dd8e25",
    input={
        "input_image": "https://replicate.delivery/mgxm/cf21baaf-55c3-49dd-a5e9-c723eaac5fd5/ILSVRC2012_val_00023440.JPEG",
        "output_format": "Final-probs-plot",
        "iou_thresholds": "0.0, 0.1"
    }
)
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 winycg/anchor_net 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": "winycg/anchor_net:e361854bcb889339416728e464bf6ae7680ae5ccfbc0ed471e949ec038dd8e25",
    "input": {
      "input_image": "https://replicate.delivery/mgxm/cf21baaf-55c3-49dd-a5e9-c723eaac5fd5/ILSVRC2012_val_00023440.JPEG",
      "output_format": "Final-probs-plot",
      "iou_thresholds": "0.0, 0.1"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

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This output was created using a different version of the model, winycg/anchor_net:8f850419.

Examples

View more examples

Run time and cost

This model costs approximately $0.053 to run on Replicate, or 18 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 4 minutes. The predict time for this model varies significantly based on the inputs.

Readme

This project provides source code for the paper “Localizing Semantic Patches for Accelerating Image Classification.”

It introduce a two-step image classification pipeline: Given an image a lightweight CNN proposes k relevant patches then, the each patch is classified by a stronger CNN and the probabilities are gradually aggregated to a final prediciton

@inproceedings{yang2022localizing, title={Localizing Semantic Patches for Accelerating Image Classification}, author={Chuanguang Yang, Zhulin An, Yongjun Xu}, booktitle={IEEE International Conference on Multimedia and Expo}, year={2022} }