objectID int64 | embedding list | model string | dim int32 |
|---|---|---|---|
34 | [
0.005130688659846783,
0.0018449067138135433,
0.03329966217279434,
0.021693214774131775,
-0.034105334430933,
0.04504737630486488,
-0.00454078009352088,
-0.04554551839828491,
0.007425097282975912,
-0.002896029967814684,
0.0017570636700838804,
0.002548291813582182,
-0.00811859592795372,
0.027... | google/siglip2-so400m-patch16-naflex | 1,152 |
35 | [
0.002404336351901293,
-0.005104158539324999,
0.02051590569317341,
0.021022023633122444,
0.0002734659647103399,
-0.0041047800332307816,
-0.010934400372207165,
-0.05537363886833191,
-0.024426626041531563,
-0.029455997049808502,
-0.022639835253357887,
-0.01868709921836853,
-0.02660229802131653,... | google/siglip2-so400m-patch16-naflex | 1,152 |
37 | [
0.014342314563691616,
-0.005803319625556469,
0.019716203212738037,
0.01112497691065073,
-0.01586393639445305,
0.012873479165136814,
-0.03255832567811012,
-0.05286579206585884,
0.026675350964069366,
-0.004997428506612778,
-0.012391074560582638,
0.00030971571686677635,
0.0014419154031202197,
... | google/siglip2-so400m-patch16-naflex | 1,152 |
38 | [
0.029771126806735992,
0.0032655561808496714,
0.034441933035850525,
0.006291836965829134,
-0.006508705206215382,
0.014998350292444229,
-0.03315143287181854,
-0.06205962225794792,
0.016004059463739395,
-0.013372767716646194,
-0.008721962571144104,
0.004390249028801918,
-0.0008224803023040295,
... | google/siglip2-so400m-patch16-naflex | 1,152 |
39 | [
-0.008534097112715244,
-0.007881637662649155,
0.02751763164997101,
0.01961069367825985,
-0.023017985746264458,
-0.011585119180381298,
0.03203153237700462,
-0.05231168493628502,
-0.009044461883604527,
-0.030277224257588387,
-0.030875613912940025,
-0.034206002950668335,
0.046797070652246475,
... | google/siglip2-so400m-patch16-naflex | 1,152 |
40 | [
0.03033110499382019,
0.005426880903542042,
0.018369458615779877,
0.011002136394381523,
-0.03940509259700775,
0.002591310767456889,
-0.030480964109301567,
-0.06259843707084656,
-0.006534794345498085,
-0.01013291534036398,
-0.00035268577630631626,
-0.011611484922468662,
-0.02051123045384884,
... | google/siglip2-so400m-patch16-naflex | 1,152 |
41 | [
0.028825340792536736,
-0.004412636626511812,
0.010179968550801277,
-0.00218245224095881,
-0.033007506281137466,
0.00015186284144874662,
-0.04153585061430931,
-0.05101238191127777,
-0.01749148964881897,
-0.004087349865585566,
-0.0016116804908961058,
0.0027525059413164854,
-0.03097848594188690... | google/siglip2-so400m-patch16-naflex | 1,152 |
42 | [
-0.016106825321912766,
-0.0020632226951420307,
0.003605248173698783,
-0.01014593057334423,
-0.02209089882671833,
-0.009807241149246693,
-0.032319508492946625,
-0.060892876237630844,
-0.0015550252282992005,
-0.01521286740899086,
-0.01488377247005701,
-0.012428438290953636,
-0.0563375279307365... | google/siglip2-so400m-patch16-naflex | 1,152 |
43 | [-0.01610684208571911,-0.002063171938061714,0.003605178091675043,-0.010145935229957104,-0.0220908150(...TRUNCATED) | google/siglip2-so400m-patch16-naflex | 1,152 |
44 | [0.0006635655299760401,-0.011909408494830132,0.005214465316385031,-0.004063892178237438,-0.017925109(...TRUNCATED) | google/siglip2-so400m-patch16-naflex | 1,152 |
End of preview. Expand in Data Studio
metmuseum/openaccess-embeddings-siglip2-naflex
Image embeddings for every public-domain artwork in metmuseum/openaccess, produced by google/siglip2-so400m-patch16-naflex.
| 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=256px.
Joining with the main dataset
from datasets import load_dataset
meta = load_dataset("metmuseum/openaccess", split="train")
emb = load_dataset("metmuseum/openaccess-embeddings-siglip2-naflex", 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-naflex", 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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