Improve dataset card: Add task category, semantic-search tag, links, and sample usage
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nielsr
HF Staff
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README.md
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---
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pretty_name: "Galaxy Image Description Benchmark"
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license: mit
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tags:
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dataset_info:
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features:
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list:
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list:
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dtype: bool
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datasets:
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---
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# Description Benchmark based on GalaxyZoo
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[](https://arxiv.org/abs/2512.11982)
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A small sample of the GZ-DECaLS dataset to cheaply test the ability of vision-language models to describe galaxy images.
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#
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```
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@misc{koblischke2025semantic,
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title={Semantic search for 100M+ galaxy images using AI-generated captions},
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author={Nolan Koblischke and Liam Parker and Francois Lanusse and Irina Espejo Morales and Jo Bovy and Shirley Ho},
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}
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```
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```
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@dataset{walmsley_mike_2020_4573248,
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author = {Walmsley, Mike and
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Lintott, Chris and
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version = {0.0.2},
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doi = {10.5281/zenodo.4573248},
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url = {https://doi.org/10.5281/zenodo.4573248}
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}
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```
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---
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license: mit
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pretty_name: Galaxy Image Description Benchmark
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tags:
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- astronomy
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- galaxy-zoo
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- galaxy-morphology
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- benchmark
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- semantic-search
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task_categories:
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- image-to-text
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dataset_info:
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features:
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- name: image_rgb
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dtype: image
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- name: ra
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dtype: float64
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- name: dec
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dtype: float64
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- name: object_id
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dtype: string
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- name: image_array
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list:
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list:
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list: float32
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- name: decision_tree
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dtype: string
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- name: iauname
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dtype: string
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- name: redshift
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dtype: float64
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- name: elpetro_absmag_r
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dtype: float64
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- name: sersic_nmgy_r
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dtype: float64
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- name: petro_th50
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dtype: float64
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- name: petro_th90
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dtype: float64
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- name: petro_theta
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dtype: float64
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- name: upload_group
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dtype: string
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- name: active_learning_on
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dtype: bool
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- name: in_gzd_ab
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dtype: bool
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- name: png_loc
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dtype: string
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- name: smooth-or-featured_total-votes
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dtype: int64
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- name: smooth-or-featured_smooth
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dtype: int64
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- name: smooth-or-featured_smooth_fraction
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dtype: float64
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- name: smooth-or-featured_featured-or-disk
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dtype: int64
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- name: smooth-or-featured_featured-or-disk_fraction
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dtype: float64
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- name: smooth-or-featured_artifact
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dtype: int64
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- name: smooth-or-featured_artifact_fraction
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dtype: float64
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- name: disk-edge-on_total-votes
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dtype: int64
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- name: disk-edge-on_yes
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dtype: int64
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- name: disk-edge-on_yes_fraction
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dtype: float64
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- name: disk-edge-on_no
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dtype: int64
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- name: disk-edge-on_no_fraction
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dtype: float64
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- name: has-spiral-arms_total-votes
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dtype: int64
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- name: has-spiral-arms_yes
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dtype: int64
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- name: has-spiral-arms_yes_fraction
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dtype: float64
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- name: has-spiral-arms_no
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dtype: int64
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- name: has-spiral-arms_no_fraction
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dtype: float64
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- name: bar_total-votes
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dtype: int64
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- name: bar_strong
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dtype: int64
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- name: bar_strong_fraction
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dtype: float64
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- name: bar_weak
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dtype: int64
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- name: bar_weak_fraction
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dtype: float64
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- name: bar_no
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dtype: int64
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- name: bar_no_fraction
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dtype: float64
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- name: bulge-size_total-votes
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dtype: int64
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- name: bulge-size_dominant
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dtype: int64
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- name: bulge-size_dominant_fraction
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dtype: float64
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- name: bulge-size_large
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dtype: int64
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- name: bulge-size_large_fraction
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dtype: float64
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- name: bulge-size_moderate
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dtype: int64
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- name: bulge-size_moderate_fraction
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dtype: float64
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- name: bulge-size_small
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dtype: int64
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- name: bulge-size_small_fraction
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dtype: float64
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- name: bulge-size_none
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dtype: int64
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- name: bulge-size_none_fraction
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dtype: float64
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- name: how-rounded_total-votes
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dtype: int64
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- name: how-rounded_round
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dtype: int64
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- name: how-rounded_round_fraction
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dtype: float64
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- name: how-rounded_in-between
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dtype: int64
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- name: how-rounded_in-between_fraction
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dtype: float64
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- name: how-rounded_cigar-shaped
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dtype: int64
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- name: how-rounded_cigar-shaped_fraction
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dtype: float64
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- name: edge-on-bulge_total-votes
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dtype: int64
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- name: edge-on-bulge_boxy
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dtype: int64
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- name: edge-on-bulge_boxy_fraction
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dtype: float64
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- name: edge-on-bulge_none
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dtype: int64
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- name: edge-on-bulge_none_fraction
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dtype: float64
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- name: edge-on-bulge_rounded
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dtype: int64
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- name: edge-on-bulge_rounded_fraction
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dtype: float64
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- name: spiral-winding_total-votes
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dtype: int64
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- name: spiral-winding_tight
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dtype: int64
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- name: spiral-winding_tight_fraction
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dtype: float64
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- name: spiral-winding_medium
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dtype: int64
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- name: spiral-winding_medium_fraction
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dtype: float64
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- name: spiral-winding_loose
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dtype: int64
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- name: spiral-winding_loose_fraction
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dtype: float64
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- name: spiral-arm-count_total-votes
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dtype: int64
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- name: spiral-arm-count_1
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dtype: int64
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- name: spiral-arm-count_1_fraction
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dtype: float64
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- name: spiral-arm-count_2
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dtype: int64
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- name: spiral-arm-count_2_fraction
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dtype: float64
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- name: spiral-arm-count_3
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dtype: int64
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- name: spiral-arm-count_3_fraction
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dtype: float64
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- name: spiral-arm-count_4
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dtype: int64
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- name: spiral-arm-count_4_fraction
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dtype: float64
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- name: spiral-arm-count_more-than-4
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dtype: int64
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- name: spiral-arm-count_more-than-4_fraction
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dtype: float64
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- name: spiral-arm-count_cant-tell
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dtype: int64
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- name: spiral-arm-count_cant-tell_fraction
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dtype: float64
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- name: merging_total-votes
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dtype: int64
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- name: merging_none
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dtype: int64
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- name: merging_none_fraction
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dtype: float64
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- name: merging_minor-disturbance
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dtype: int64
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- name: merging_minor-disturbance_fraction
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dtype: float64
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- name: merging_major-disturbance
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dtype: int64
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- name: merging_major-disturbance_fraction
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dtype: float64
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- name: merging_merger
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dtype: int64
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- name: merging_merger_fraction
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dtype: float64
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- name: wrong_size_statistic
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dtype: float64
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- name: wrong_size_warning
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dtype: bool
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datasets:
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- legacy
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---
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# Description Benchmark based on GalaxyZoo
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[](https://arxiv.org/abs/2512.11982)
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[Paper](https://huggingface.co/papers/2512.11982) | [Project Page](https://aion-search.github.io) | [Code](https://github.com/NolanKoblischke/AION-Search)
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A small sample of the GZ-DECaLS dataset to cheaply test the ability of vision-language models to describe galaxy images.
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## Sample Usage
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```python
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from aionsearch import AIONSearchClipModel
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# Load pretrained model from HuggingFace
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model = AIONSearchClipModel.from_pretrained()
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# Project AION image embeddings into shared space
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aion_embedding = # Embedding of an image using github.com/PolymathicAI/AION
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projected_image = model.image_projector(aion_embedding) # (batch, 768) -> (batch, 1024)
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# Project OpenAI text embeddings into shared space
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text_embedding = # Embedding of text using text-embedding-3-large
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projected_text = model.text_projector(text_embedding) # (batch, 3072) -> (batch, 1024)
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# Compute similarity for semantic search
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similarity = projected_image @ projected_text.T
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```
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See [\`examples/quick_start.ipynb\`](https://github.com/NolanKoblischke/AION-Search/blob/main/examples/quick_start.ipynb) for a complete walkthrough that downloads a galaxy image, generates embeddings with AION, and performs text-to-image similarity search.
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+
|
| 241 |
+
## Data source
|
| 242 |
+
The underlying catalog data are derived from the Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies and remain subject to their original data-use policy and required acknowledgements.
|
| 243 |
+
|
| 244 |
+
## Embeddings Citation
|
| 245 |
+
```bibtex
|
| 246 |
@misc{koblischke2025semantic,
|
| 247 |
title={Semantic search for 100M+ galaxy images using AI-generated captions},
|
| 248 |
author={Nolan Koblischke and Liam Parker and Francois Lanusse and Irina Espejo Morales and Jo Bovy and Shirley Ho},
|
|
|
|
| 254 |
}
|
| 255 |
```
|
| 256 |
|
| 257 |
+
## GZ-DECaLS Dataset Citation
|
| 258 |
+
```bibtex
|
| 259 |
@dataset{walmsley_mike_2020_4573248,
|
| 260 |
author = {Walmsley, Mike and
|
| 261 |
Lintott, Chris and
|
|
|
|
| 284 |
version = {0.0.2},
|
| 285 |
doi = {10.5281/zenodo.4573248},
|
| 286 |
url = {https://doi.org/10.5281/zenodo.4573248}
|
| 287 |
+
}
|
|
|
|
|
|