INQUIRE-Benchmark-small

INQUIRE is a text-to-image retrieval benchmark designed to challenge multimodal models with expert-level queries about the natural world. This dataset aims to emulate real world image retrieval and analysis problems faced by scientists working with large-scale image collections. Therefore, we will use this benchmark to improve Sagecontinuum's Text-to-Image Retrieval Systems.

Dataset Details

This dataset was build off of INQUIRE-Rerank with additional modifications to be able to do full-dataset retrieval. Please refer to modify_inquire_rerank.ipynb to see the modifications we did.

INQUIRE-Rerank Details

The INQUIRE-Rerank is created from 250 expert-level queries. This task fixes an initial ranking of 100 images per query, obtained using CLIP ViT-H-14 zero-shot retrieval on the entire 5 million image iNat24 dataset. The challenge is to rerank all 100 images for each query with the goal of assigning high scores to the relevant images (there are potentially many relevant images for each query). This fixed starting point makes reranking evaluation consistent, and saves time from running the initial retrieval yourself. If you're interested in full-dataset retrieval, check out INQUIRE-Fullrank available from the github repo.

Loading the Dataset

To load the dataset using HugginFace datasets, you first need to pip install datasets, then run the following code:

from datasets import load_dataset

inquire = load_dataset("sagecontinuum/INQUIRE-Benchmark-small", split="validation") # or "test"

Additional Details

For additional details, check out INQUIRE's paper and more.

🌐 Website 📖 Paper GitHub

Citations

@article{vendrow2024inquire,
  title={INQUIRE: A Natural World Text-to-Image Retrieval Benchmark}, 
  author={Vendrow, Edward and Pantazis, Omiros and Shepard, Alexander and Brostow, Gabriel and Jones, Kate E and Mac Aodha, Oisin and Beery, Sara and Van Horn, Grant},
  journal={NeurIPS},
  year={2024},
}