--- 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: 1821853979.0 num_examples: 16100 download_size: 22078996 dataset_size: 1821853979.0 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**.