This project is a model leaderboard website for evaluating a variety of models on the COCO dataset. Models are compared compares models across a variety of metrics, computed with supervision.

model-leaderboard/
│
├── data/                  # Directory for storing datasets (e.g., COCO)
├── models/                # Model-specific directories
│   ├── object_detection/   # Houses individual model folders
│   │   ├── yolov8n/        # Example of a model folder
│   │   ├── yolov9t/        # Example of another model folder
│   │   └── yolov10m/       # And so on...
│   └── utils.py            # Shared utility code (minimal cross-model dependencies)
│
├── index.html              # Main page for the static side
├── static/                 # Static files, serving as the backend for the site
│   └── ...
├── download_data.py        # Script for downloading the ground truth dataset
├── build_static_site.py    # Must be run to aggregate results for the static site.
└── requirements.txt        # Dependencies for data download and leaderboard front end

Each model is expected to have:

model_name/
│
├── requirements.txt        # Dependencies for running the model
├── run.py                  # Script that runs model on dataset in /data, compares with ground truth
├── results.json            # Model results, created with run.py
├── (any other scripts)
└── (optional) README.md    # Links to original model page & instructions on how to produce results.py

Each model is expected to be run in a separate python virtual environment, with its own set of dependencies.

Before we automate the models to be run regularly, the naming standards are relaxed. The only requirements is to store results for results.json in the model directory. For consistency, we advice to keep the scripts in run.py.

1. download_data.py: Downloads the dataset (currently configured for COCO) and places it into the data/ directory.

2. build_static_site.py: Aggregates the results of the models to be shown on the GitHub Pages site.

3. run_overnight.sh: An early version of a script to run the entire process, generating model results and comparing to downloaded data. We hacked it together for the first iteration of the leaderboard. Requires uv.

4. gradio_app.py: The initial version of the leaderboard UI. Displays model results in a gradio page.

5. Model-Specific Folders"

   • Each object detection model is housed in its own folder under models/object_detection/. These folders include run.py, which generates evaluation results in the form of a results.json file.

6. utils.py: Contains shared utility code across models.

Run the download_data.py script to download the COCO dataset (or any supported dataset in the future):

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python download_data.py
deactivate

Each model folder contains its own script to run predictions and generate results.

cd models/object_detection/yolov8n
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python run.py  # Or any other scripts
deactivate

Currently, you may use run_overnight.sh to run all models. This is an early version of the script and is due to change.

./run_overnight.sh

Once the results are generated, you can launch the Gradio app to visualize the leaderboard:

source .venv/bin/activate
python gradio_app.py

• The leaderboard is currently configured to work with the COCO dataset, running the benchmark on the test set.
• Model dependencies are handled separately in each model folder, with each model having its own requirements.txt to avoid dependency conflicts.

Feel free to contribute by adding new models, improving the existing evaluation process, or expanding dataset support. To add a new model, simply create a new folder under models/object_detection/ and follow the structure of existing models.

Make sure that the model's run.py script generates a results.json file in the model directory. If there are more scripts, please provide a README.md in the model folder how to run the model. Optionally, you may modify the run_overnight.sh script to help us automate the benchmarking in the future.