metadata
license: cc-by-sa-4.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: astropt_15m_hsc
list: float32
length: 384
- name: astropt_95m_hsc
list: float32
length: 768
- name: astropt_850m_hsc
list: float32
length: 2048
- name: convnext_nano_hsc
list: float32
length: 640
- name: convnext_tiny_hsc
list: float32
length: 768
- name: convnext_base_hsc
list: float32
length: 1024
- name: convnext_large_hsc
list: float32
length: 1536
- name: dino_small_hsc
list: float32
length: 384
- name: dino_base_hsc
list: float32
length: 768
- name: dino_large_hsc
list: float32
length: 1024
- name: dino_giant_hsc
list: float32
length: 1536
- name: ijepa_huge_hsc
list: float32
length: 1280
- name: ijepa_giant_hsc
list: float32
length: 1408
- name: vit_base_hsc
list: float32
length: 768
- name: vit_large_hsc
list: float32
length: 1024
- name: vit_huge_hsc
list: float32
length: 1280
- name: specformer_base_desi
list: float64
length: 768
splits:
- name: train
num_bytes: 1487887360
num_examples: 20465
download_size: 1676612642
dataset_size: 1487887360
DESI–HSC Embeddings
This dataset contains precomputed embeddings for cross-survey sources (DESI ↔ HSC).
Each row includes one object ID and multiple embedding vectors from different backbone models (e.g. DINO, ViT, AstroPT).
Load in Python
from datasets import load_dataset
import numpy as np
ds = load_dataset("UniverseTBD/desi_hsc_embeddings", split="train")
print("Columns:", ds.column_names)
# Choose one HSC image embedding and the DESI spectral embedding:
col_a = "specformer_base_desi" # DESI spectra (SpecFormer)
col_b = "dino_large_hsc" # HSC images (DINOv2 Large)
assert col_a in ds.column_names and col_b in ds.column_names