README.md

metadata

license: cc-by-nc-4.0
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_category_id
      dtype: int64
    - name: inat24_category_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
  splits:
    - name: train
      num_bytes: 1821853979
      num_examples: 16100
  download_size: 22078996
  dataset_size: 1821853979
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

INQUIRE-Rerank

INQUIRE is a text-to-image retrieval benchmark designed to challenge multimodal models with expert-level queries about the natural world.

The INQUIRE-Rerank task fixes an initial ranking of 100 images per query using CLIP ViT-H-14 zero-shot retrieval on the entire 5 million image iNat24 dataset. This 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.