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: 1633954421
num_examples: 16100
download_size: 1507625576
dataset_size: 1633954421
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
size_categories:
- 10K<n<100K
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.
