README.md

---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: query
    dtype: string
  - name: relevant
    dtype: int64
  - name: clip_score
    dtype: float64
  - name: inat24_image_id
    dtype: int64
  - name: inat24_file_name
    dtype: string
  - name: supercategory
    dtype: string
  - name: category
    dtype: string
  - name: iconic_group
    dtype: string
  - name: inat24_species_id
    dtype: int64
  - name: inat24_species_name
    dtype: string
  - name: latitude
    dtype: float64
  - name: longitude
    dtype: float64
  - name: location_uncertainty
    dtype: float64
  - name: date
    dtype: string
  - name: license
    dtype: string
  - name: rights_holder
    dtype: string
  - name: query_id
    dtype: int64
  splits:
  - name: validation
    num_bytes: 369572974.0
    num_examples: 4000
  - name: test
    num_bytes: 1513809798.0
    num_examples: 16000
  download_size: 1879445739
  dataset_size: 1883382772.0
configs:
- config_name: default
  data_files:
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---
# INQUIRE-Benchmark-small


<!-- **INQUIRE: A Natural World Text-to-Image Retrieval Benchmark** -->

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**](https://huggingface.co/datasets/evendrow/INQUIRE-Rerank) with additional modifications to be able to do full-dataset retrieval. Please refer to [modify_inquire_rerank.ipynb](https://huggingface.co/datasets/sagecontinuum/INQUIRE-Benchmark-small/blob/main/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](https://inquire-benchmark.github.io/) 
[📖 Paper](https://arxiv.org/abs/2411.02537) 
[GitHub](https://github.com/inquire-benchmark/INQUIRE) 

**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},
}
```