README.md

---
license: cc-by-nc-4.0
dataset_info:
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    dtype: image
  - name: query
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  - name: relevant
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  - name: clip_score
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  splits:
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    num_bytes: 1633954421
    num_examples: 16100
  download_size: 1507625576
  dataset_size: 1633954421
configs:
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  data_files:
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    path: data/train-*
size_categories:
- 10K<n<100K
---

# INQUIRE-Rerank

**Please note that this is dataset is preliminary, and will be updated soon.**


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<!-- **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 hope that INQUIRE will both encourage and track advancements in the real scientific utility of AI systems.

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/630b1e44cd26ad7f60d490e2/CIFPqSwwkSSZo0zMoQOCr.jpeg)

**Dataset Details**

The **INQUIRE-Rerank** 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.
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**.

**Dataset Sources**
- Website: [https://inquire-benchmark.github.io/](https://inquire-benchmark.github.io/)
- Repository: [https://github.com/inquire-benchmark/INQUIRE](https://github.com/inquire-benchmark/INQUIRE)