objectID int64 34 955k | embedding listlengths 1.15k 1.15k | model stringclasses 1
value | dim int32 1.15k 1.15k |
|---|---|---|---|
34 | [
0.017077960073947906,
-0.024372605606913567,
0.02606203407049179,
0.006688839755952358,
-0.011745392344892025,
0.01486252062022686,
0.022666024044156075,
-0.03173312544822693,
-0.009416282176971436,
0.010631573386490345,
0.001078681438229978,
-0.017590031027793884,
-0.020200274884700775,
0... | google/siglip2-so400m-patch14-384 | 1,152 |
35 | [
0.014886648394167423,
-0.010906378738582134,
0.01709233783185482,
0.0056167361326515675,
-0.012754037044942379,
-0.0095239682123065,
0.0002819937944877893,
-0.039537135511636734,
-0.046752285212278366,
-0.015796508640050888,
-0.0043584490194916725,
-0.029362503439188004,
-0.01706008240580558... | google/siglip2-so400m-patch14-384 | 1,152 |
37 | [
0.00781311560422182,
-0.000885411340277642,
0.010245701298117638,
-0.010235561057925224,
-0.005610446911305189,
0.01134086586534977,
-0.0010999941732734442,
-0.04449688643217087,
-0.009370161220431328,
0.006270979065448046,
0.00022480885672848672,
-0.003905159654095769,
-0.011947964318096638... | google/siglip2-so400m-patch14-384 | 1,152 |
38 | [
0.01626766286790371,
-0.0021343000698834658,
0.014184742234647274,
-0.006452386733144522,
-0.011596781201660633,
0.00690227746963501,
-0.002691252389922738,
-0.048583660274744034,
-0.013488588854670525,
-0.0003908474463969469,
-0.004220329225063324,
-0.01847013458609581,
-0.00959395524114370... | google/siglip2-so400m-patch14-384 | 1,152 |
39 | [
-0.018884174525737762,
-0.009631042368710041,
0.021367672830820084,
0.003742305561900139,
-0.006238492671400309,
-0.00887278188019991,
0.029489940032362938,
-0.039176009595394135,
-0.01102713868021965,
-0.005630588158965111,
-0.039744291454553604,
-0.015822311863303185,
0.017432378605008125,... | google/siglip2-so400m-patch14-384 | 1,152 |
40 | [
0.029029618948698044,
0.002986599924042821,
-0.008507737889885902,
-0.0033194448333233595,
-0.040037184953689575,
0.0008215222624130547,
0.014425523579120636,
-0.04036840423941612,
-0.012925343587994576,
-0.0010967004345729947,
-0.0059319897554814816,
-0.011625578626990318,
-0.03623552620410... | google/siglip2-so400m-patch14-384 | 1,152 |
41 | [
0.04110347852110863,
-0.005348441191017628,
0.00046067603398114443,
-0.007146668620407581,
-0.035747479647397995,
-0.00199095718562603,
0.00016884913202375174,
-0.039645999670028687,
-0.02040632627904415,
-0.009737040847539902,
0.007381271570920944,
-0.022355923429131508,
-0.0316293388605117... | google/siglip2-so400m-patch14-384 | 1,152 |
42 | [
-0.018458180129528046,
-0.021527621895074844,
-0.019737888127565384,
-0.010076159611344337,
-0.012052838690578938,
-0.009783169254660606,
0.006275635212659836,
-0.03784715011715889,
0.006666572764515877,
0.00698036327958107,
-0.010077274404466152,
0.012527834624052048,
0.004901113919913769,
... | google/siglip2-so400m-patch14-384 | 1,152 |
43 | [-0.018458180129528046,-0.021527621895074844,-0.019737888127565384,-0.010076159611344337,-0.01205283(...TRUNCATED) | google/siglip2-so400m-patch14-384 | 1,152 |
44 | [0.0015702027594670653,-0.02653980255126953,-0.019255293533205986,-0.004744702484458685,-0.014089972(...TRUNCATED) | google/siglip2-so400m-patch14-384 | 1,152 |
End of preview. Expand in Data Studio
metmuseum/openaccess-embeddings-siglip2
Image embeddings for every public-domain artwork in metmuseum/openaccess, produced by google/siglip2-so400m-patch14-384.
| Column | Type | Notes |
|---|---|---|
objectID |
int64 | Primary key — matches objectID in metmuseum/openaccess |
embedding |
list<float32> | L2-normalised, dim = 1152 |
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: dim=1152, expected image size=384px.
Joining with the main dataset
from datasets import load_dataset
meta = load_dataset("metmuseum/openaccess", split="train")
emb = load_dataset("metmuseum/openaccess-embeddings-siglip2", 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-siglip2", 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.
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