objectID int64 34 955k | embedding listlengths 1.28k 1.28k | model stringclasses 1
value | dim int32 1.28k 1.28k |
|---|---|---|---|
34 | [
0.023432942107319832,
-0.02650412917137146,
-0.0034973707515746355,
0.034308988600969315,
-0.012943293899297714,
-0.01154786255210638,
0.0213041752576828,
-0.007750748191028833,
-0.037800777703523636,
-0.02226492576301098,
0.02590986341238022,
-0.057879507541656494,
0.013686470687389374,
0... | laion/CLIP-ViT-bigG-14-laion2b_s39b_b160k | 1,280 |
35 | [
0.02198607102036476,
-0.0016596257919445634,
-0.013230178505182266,
0.017936745658516884,
-0.004672721028327942,
-0.01079269777983427,
0.03551117330789566,
-0.027330871671438217,
0.019556937739253044,
0.013145489618182182,
0.018768196925520897,
-0.05242640897631645,
-0.004373508505523205,
... | laion/CLIP-ViT-bigG-14-laion2b_s39b_b160k | 1,280 |
37 | [
0.015250771306455135,
-0.02735118940472603,
-0.013550868257880211,
0.007402605842798948,
0.013235434889793396,
0.002909219590947032,
0.01880788244307041,
-0.02393045462667942,
0.023575203493237495,
0.009739289991557598,
-0.03364070877432823,
-0.042035046964883804,
0.023679424077272415,
0.0... | laion/CLIP-ViT-bigG-14-laion2b_s39b_b160k | 1,280 |
38 | [
0.009891790337860584,
-0.029506320133805275,
-0.014967714436352253,
0.015486819669604301,
-0.0020132693462073803,
0.019207805395126343,
0.016290461644530296,
-0.03214818239212036,
0.017455194145441055,
0.006097801029682159,
-0.02155284211039543,
-0.02922757714986801,
0.012088416144251823,
... | laion/CLIP-ViT-bigG-14-laion2b_s39b_b160k | 1,280 |
39 | [
0.04959122836589813,
-0.034225404262542725,
-0.0312807634472847,
0.02676331251859665,
0.017365988343954086,
-0.003367743920534849,
0.009597627446055412,
-0.02465919964015484,
0.030281253159046173,
-0.021473471075296402,
-0.022487271577119827,
-0.04552324116230011,
0.00224133487790823,
0.03... | laion/CLIP-ViT-bigG-14-laion2b_s39b_b160k | 1,280 |
40 | [
0.03140822425484657,
0.009088490158319473,
-0.014816813170909882,
0.02273097261786461,
-0.014775008894503117,
-0.0014003481483086944,
0.01210768148303032,
-0.043336477130651474,
-0.0027955039404332638,
0.03179843723773956,
0.024479340761899948,
-0.09733344614505768,
0.025726687163114548,
-... | laion/CLIP-ViT-bigG-14-laion2b_s39b_b160k | 1,280 |
41 | [
0.03152276948094368,
0.006117468234151602,
-0.012164602056145668,
0.02220107987523079,
-0.009119036607444286,
-0.016712071374058723,
0.03424280509352684,
-0.039518795907497406,
0.014133649878203869,
0.008205241523683071,
0.006417430471628904,
-0.07919608801603317,
0.018440643325448036,
-0.... | laion/CLIP-ViT-bigG-14-laion2b_s39b_b160k | 1,280 |
42 | [0.02044248767197132,-0.014504254795610905,0.011645158752799034,-0.00472006481140852,0.0530826821923(...TRUNCATED) | laion/CLIP-ViT-bigG-14-laion2b_s39b_b160k | 1,280 |
43 | [0.020442580804228783,-0.014504284597933292,0.01164509728550911,-0.004720060154795647,0.053082592785(...TRUNCATED) | laion/CLIP-ViT-bigG-14-laion2b_s39b_b160k | 1,280 |
44 | [-0.011610254645347595,-0.009151593782007694,0.01903832145035267,0.012820248492062092,0.034949332475(...TRUNCATED) | laion/CLIP-ViT-bigG-14-laion2b_s39b_b160k | 1,280 |
End of preview. Expand in Data Studio
metmuseum/openaccess-embeddings-openclip-vitg14
Image embeddings for every public-domain artwork in metmuseum/openaccess, produced by laion/CLIP-ViT-bigG-14-laion2B-s39B-b160K.
| Column | Type | Notes |
|---|---|---|
objectID |
int64 | Primary key — matches objectID in metmuseum/openaccess |
embedding |
list<float32> | L2-normalised, dim = 1280 |
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=1280, expected image size=224px.
Joining with the main dataset
from datasets import load_dataset
meta = load_dataset("metmuseum/openaccess", split="train")
emb = load_dataset("metmuseum/openaccess-embeddings-openclip-vitg14", 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-openclip-vitg14", 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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