CoolFace
Datasetpublic

UniverseTBD/legacysurvey_hsc_embeddings

Legacy Survey ↔ HSC Embeddings (The Platonic Universe) Precomputed cross-survey embeddings for matched sources in Legacy Survey and HSC.Each row is one object with multiple backbone embeddings for both surveys (paired by suffixes _legacysurvey and _hsc). Examples of columns (see Viewer for full list): AstroPT: astropt_15m_hsc, astropt_15m_legacysurvey, astropt_95m_*, astropt_850m_* ConvNeXt: convnext_nano_*, convnext_tiny_*, convnext_base_*, convnext_large_* DINOv2:… See the full description on the dataset page: https://huggingface.co/datasets/UniverseTBD/legacysurvey_hsc_embeddings.

sourceHugging Facecc-by-sa-4.0updated 1y agoView on Hugging Face
0likes169downloads
Dataset Card

Legacy Survey ↔ HSC Embeddings (The Platonic Universe)

Precomputed cross-survey embeddings for matched sources in Legacy Survey and HSC. Each row is one object with multiple backbone embeddings for both surveys (paired by suffixes _legacysurvey and _hsc).

Examples of columns (see Viewer for full list):

  • —AstroPT: astropt_15m_hsc, astropt_15m_legacysurvey, astropt_95m_*, astropt_850m_*
  • —ConvNeXt: convnext_nano_*, convnext_tiny_*, convnext_base_*, convnext_large_*
  • —DINOv2: dino_small_*, dino_base_*, dino_large_*, dino_giant_*
  • —I-JEPA: ijepa_huge_*, ijepa_giant_*
  • —ViT: vit_base_*, vit_large_*, vit_huge_*
  • —Identifier: legacysurvey_object_id

Load in Python

python
from datasets import load_dataset
import numpy as np

ds = load_dataset("UniverseTBD/legacysurvey_hsc_embeddings", split="train")
print("Columns:", ds.column_names[:10], "...")
row = ds[0]