metmuseum/openaccess-embeddings-apple-mobileclip
metmuseum/openaccess-embeddings-apple-mobileclip Image embeddings for every public-domain artwork in metmuseum/openaccess, produced by apple/MobileCLIP-S2-OpenCLIP. Column Type Notes objectID int64 Primary key — matches objectID in metmuseum/openaccess embedding list<float32> L2-normalised, dim = 512 model string Source model id dim int32 Embedding dimension Image bytes are not stored here; join against the main dataset to recover them. Embedding spec:… See the full description on the dataset page: https://huggingface.co/datasets/metmuseum/openaccess-embeddings-apple-mobileclip.
metmuseum/openaccess-embeddings-apple-mobileclip
Image embeddings for every public-domain artwork in `metmuseum/openaccess`, produced by apple/MobileCLIP-S2-OpenCLIP.
Image bytes are not stored here; join against the main dataset to recover them.
Embedding spec: dim=512, expected image size=256px.
Joining with the main dataset
from datasets import load_dataset
meta = load_dataset("metmuseum/openaccess", split="train")
emb = load_dataset("metmuseum/openaccess-embeddings-apple-mobileclip", split="train")
# Build an objectID -> embedding lookup, then attach to the metadata rows.
lookup = {r["objectID"]: r["embedding"] for r in emb}
joined = meta.map(lambda r: {"embedding": lookup.get(r["objectID"])})
print(joined[0].keys())Nearest-neighbour example
import numpy as np
from datasets import load_dataset
emb = load_dataset("metmuseum/openaccess-embeddings-apple-mobileclip", split="train")
ids = np.array(emb["objectID"])
mat = np.array(emb["embedding"], dtype=np.float32) # already L2-normalised
query = mat[0]
scores = mat @ query
top = np.argsort(-scores)[:10]
print(list(zip(ids[top].tolist(), scores[top].tolist())))Generated by `et-openaccess-embeddings`.