# Installation
pip install -e .

# Download artifacts, they contain the trained specificity model
# https://drive.google.com/file/d/1K9HtrRovywFrJTMFWz6AoCW6DLFRtPhh/view?usp=sharing
# Unzip the artifacts
tar -xzf artifacts.tar.gz

# Run experiments and plot results
python scripts/benchmark_run.py wildlife --num-queries 100 --num-filters 2 3 4 --num-kv-caches 32 64 128 --num-embeddings 1000 --sample-sizes 1 2 4 8 16 32 64
python scripts/benchmark_plot.py artwork wildlife ecommerce --num-queries 100

# download and extract imagenet: https://www.kaggle.com/datasets/thbdh5765/ilsvrc2012

# Prepare
python scripts/wikidata_embed.py <path/to/imagenet>/ILSVRC/Data/CLS-LOC/train --sample 500_000 --batch-size 100 --recursive

# Train model
python scripts/threshold_train.py wordnet

@misc{urban2026selectivityestimationsemanticfilters,
      title={Selectivity Estimation for Semantic Filters on Image Data}, 
      author={Matthias Urban and Vu Huy Nguyen and Gabriele Sanmartino and Paolo Papotti and Carsten Binnig},
      year={2026},
      eprint={2606.04610},
      archivePrefix={arXiv},
      primaryClass={cs.DB},
      url={https://arxiv.org/abs/2606.04610}, 
}