arielreplicate / gscorecam-clip-analyzer

Shows what CLIP looks at in an image given text

Cold

Public
743 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=arielreplicate/gscorecam-clip-analyzer

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 arielreplicate/gscorecam-clip-analyzer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "arielreplicate/gscorecam-clip-analyzer:414953a78bfd5f84c7db7ee5c3eac5cd74473698780c4fb1c1da52058b48ce31",
  {
    input: {
      drop: true,
      topk: 300,
      input_text: "background fence",
      input_image: "https://replicate.delivery/pbxt/IAa6qtgBBpgrG7xEqYd7aQRTnS4hfnXzGYqhK1MS3OTICtBm/apple-ipod.jpg",
      clip_version: "RN50x16",
      overlay_output: true
    }
  }
);
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 arielreplicate/gscorecam-clip-analyzer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "arielreplicate/gscorecam-clip-analyzer:414953a78bfd5f84c7db7ee5c3eac5cd74473698780c4fb1c1da52058b48ce31",
    input={
        "drop": True,
        "topk": 300,
        "input_text": "background fence",
        "input_image": "https://replicate.delivery/pbxt/IAa6qtgBBpgrG7xEqYd7aQRTnS4hfnXzGYqhK1MS3OTICtBm/apple-ipod.jpg",
        "clip_version": "RN50x16",
        "overlay_output": True
    }
)
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 arielreplicate/gscorecam-clip-analyzer 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": "414953a78bfd5f84c7db7ee5c3eac5cd74473698780c4fb1c1da52058b48ce31",
    "input": {
      "drop": true,
      "topk": 300,
      "input_text": "background fence",
      "input_image": "https://replicate.delivery/pbxt/IAa6qtgBBpgrG7xEqYd7aQRTnS4hfnXzGYqhK1MS3OTICtBm/apple-ipod.jpg",
      "clip_version": "RN50x16",
      "overlay_output": true
    }
  }' \
  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/arielreplicate/gscorecam-clip-analyzer@sha256:414953a78bfd5f84c7db7ee5c3eac5cd74473698780c4fb1c1da52058b48ce31 \
  -i 'drop=true' \
  -i 'topk=300' \
  -i 'input_text="background fence"' \
  -i 'input_image="https://replicate.delivery/pbxt/IAa6qtgBBpgrG7xEqYd7aQRTnS4hfnXzGYqhK1MS3OTICtBm/apple-ipod.jpg"' \
  -i 'clip_version="RN50x16"' \
  -i 'overlay_output=true'

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 --gpus=all r8.im/arielreplicate/gscorecam-clip-analyzer@sha256:414953a78bfd5f84c7db7ee5c3eac5cd74473698780c4fb1c1da52058b48ce31
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "drop": true,
      "topk": 300,
      "input_text": "background fence",
      "input_image": "https://replicate.delivery/pbxt/IAa6qtgBBpgrG7xEqYd7aQRTnS4hfnXzGYqhK1MS3OTICtBm/apple-ipod.jpg",
      "clip_version": "RN50x16",
      "overlay_output": true
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

22.7 seconds

Tweak itReport

This example was created by a different version, arielreplicate/gscorecam-clip-analyzer:8ffb602b.

Examples

View more examples

Run time and cost

This model costs approximately $0.0054 to run on Replicate, or 185 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 24 seconds. The predict time for this model varies significantly based on the inputs.

Readme

gScoreCAM: What is CLIP looking at?

Show which parts of an image are most correlated with a text juged by CLIP embedded space.

tldr: Based on the observations that CLIP ResNet-50 channels are very noisy compared to typical ImageNet-trained ResNet-50, and most saliency methods obtain pretty low object localization scores with CLIP. By visualizing the top 10% most sensitive (highest-gradient) channels, our gScoreCAM obtains the state of the art weakly supervised localization results using CLIP (in both ResNet and ViT versions).

Official Implementation for the paper gScoreCAM: What is CLIP looking at? (2022) by Peijie Chen, Qi Li, Saad Biaz, Trung Bui, and Anh Nguyen. Oral paper at ACCV 2022.

If you use this software, please consider citing:

@inproceedings{chen2022gScoreCAM,
  title={gScoreCAM: What is CLIP looking at?},
  author={Peijie Chen, Qi Li, Saad Biaz, Trung Bui, and Anh Nguyen},
  booktitle={Proceedings of the Asian Conference on Computer Vision (ACCV)},
  year={2022}
}

:star2: Interactive Colab demo :star2: