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metadata
title: Omniseal Leaderboard
emoji: 🦀
colorFrom: red
colorTo: green
sdk: docker
pinned: false
short_description: Leaderboard for watermarking models

Docker Build Instructions

Prerequisites

  • Docker installed on your system
  • Git repository cloned locally

Build Steps (conda)

  1. Initialize conda environment
cd backend
conda env create -f environment.yml -y
conda activate omniseal-benchmark-backend
  1. Build frontend (outputs html, js, css into frontend/dist). Note you only need this if you are updating the frontend, the repository would already have a build checked in at frontend/dist
cd frontend
npm install
npm run build -- --mode prod
  1. Run backend server from project root. This would serve frontend files from port http://localhost:7860
gunicorn --chdir backend -b 0.0.0.0:7860 app:app --reload
  1. Server will be running on http://localhost:7860

Build Steps (Docker, huggingface)

  1. Build the Docker image from project root:
docker build -t omniseal-benchmark .

OR

docker buildx build -t omniseal-benchmark .
  1. Run the container (this runs in auto-reload mode when you update python files in the backend directory). Note the -v argument make it so the backend could hot reload:
docker run -p 7860:7860 -v $(pwd)/backend:/app/backend omniseal-benchmark
  1. Access the application at http://localhost:7860

Local Development

When updating the backend, you can run it in whichever build steps above to take advantage of hot-reload so you don't have to restart the server.

For the frontend:

  1. Create a .env.local file in the frontend directory. Set VITE_API_SERVER_URL to where your backend server is running. When running locally it will be VITE_API_SERVER_URL=http://localhost:7860. This overrides the configuration in .env so the frontend will connect with your backend URL of choice.

  2. Run the development server with hot-reload:

cd frontend
npm install
npm run dev

Local datasets

By default, datasets are loaded over the network based on backend/config.py. Please see the file there and modify if loading different datasets.

ABS_DATASET_DOMAIN, ABS_DATASET_PATH controls where datasets are loaded from and are used in DATASET_CONFIGS and EXAMPLE_CONFIGS. Any datasets and examples to be added would need to update the above constants to be visualized in the UI.

There is commented out code that sets the ABS_DATASET_DOMAIN to the backend/data directory. You can see the data formats of the csv / json files required to render the leaderboard as well as examples there.

In the data directory, by default this matches the path structure for loading over the network. Each dataset should be placed under data/omnisealbench as a directory, e.g. data/omnisealbench/sav_val_full_v2 and in the directory have files:

  • {type}_benchmark.csv for leaderboard tables
  • {type}_attacks_variations.csv for leaderboard chart
  • examples_eval_results.json for examples

Please see reference csv and json files for what these need to look like.