Sample stimuli










How to use
from brainscore_vision import load_benchmark benchmark = load_benchmark("ImageNet-top1") score = benchmark(my_model)
Model scores
Min Alignment
Max Alignment
Rank |
Model |
Score |
---|---|---|
1 |
.863
|
|
2 |
.854
|
|
3 |
.853
|
|
4 |
.852
|
|
5 |
.851
|
|
6 |
.851
|
|
7 |
.850
|
|
8 |
.845
|
|
9 |
.845
|
|
10 |
.842
|
|
11 |
.841
|
|
12 |
.835
|
|
13 |
.829
|
|
14 |
.828
|
|
15 |
.828
|
|
16 |
.827
|
|
17 |
.827
|
|
18 |
.822
|
|
19 |
.809
|
|
20 |
.805
|
|
21 |
.804
|
|
22 |
.802
|
|
23 |
.799
|
|
24 |
.798
|
|
25 |
.795
|
|
26 |
.793
|
|
27 |
.792
|
|
28 |
.792
|
|
29 |
.790
|
|
30 |
.781
|
|
31 |
.780
|
|
32 |
.780
|
|
33 |
.778
|
|
34 |
.777
|
|
35 |
.777
|
|
36 |
.777
|
|
37 |
.776
|
|
38 |
.775
|
|
39 |
.774
|
|
40 |
.774
|
|
41 |
.772
|
|
42 |
.772
|
|
43 |
.768
|
|
44 |
.767
|
|
45 |
.766
|
|
46 |
.766
|
|
47 |
.764
|
|
48 |
.764
|
|
49 |
.762
|
|
50 |
.761
|
|
51 |
.760
|
|
52 |
.759
|
|
53 |
.758
|
|
54 |
.758
|
|
55 |
.757
|
|
56 |
.756
|
|
57 |
.752
|
|
58 |
.752
|
|
59 |
.751
|
|
60 |
.750
|
|
61 |
.750
|
|
62 |
.749
|
|
63 |
.749
|
|
64 |
.748
|
|
65 |
.746
|
|
66 |
.745
|
|
67 |
.745
|
|
68 |
.744
|
|
69 |
.744
|
|
70 |
.744
|
|
71 |
.741
|
|
72 |
.740
|
|
73 |
.740
|
|
74 |
.739
|
|
75 |
.739
|
|
76 |
.739
|
|
77 |
.736
|
|
78 |
.735
|
|
79 |
.735
|
|
80 |
.733
|
|
81 |
.733
|
|
82 |
.732
|
|
83 |
.732
|
|
84 |
.732
|
|
85 |
.731
|
|
86 |
.730
|
|
87 |
.729
|
|
88 |
.729
|
|
89 |
.728
|
|
90 |
.726
|
|
91 |
.724
|
|
92 |
.723
|
|
93 |
.722
|
|
94 |
.722
|
|
95 |
.722
|
|
96 |
.720
|
|
97 |
.718
|
|
98 |
.718
|
|
99 |
.718
|
|
100 |
.718
|
|
101 |
.718
|
|
102 |
.718
|
|
103 |
.715
|
|
104 |
.715
|
|
105 |
.711
|
|
106 |
.709
|
|
107 |
.708
|
|
108 |
.707
|
|
109 |
.706
|
|
110 |
.705
|
|
111 |
.704
|
|
112 |
.704
|
|
113 |
.704
|
|
114 |
.703
|
|
115 |
.703
|
|
116 |
.703
|
|
117 |
.702
|
|
118 |
.702
|
|
119 |
.702
|
|
120 |
.702
|
|
121 |
.702
|
|
122 |
.702
|
|
123 |
.702
|
|
124 |
.701
|
|
125 |
.700
|
|
126 |
.699
|
|
127 |
.698
|
|
128 |
.698
|
|
129 |
.698
|
|
130 |
.697
|
|
131 |
.697
|
|
132 |
.694
|
|
133 |
.692
|
|
134 |
.688
|
|
135 |
.687
|
|
136 |
.687
|
|
137 |
.684
|
|
138 |
.684
|
|
139 |
.681
|
|
140 |
.680
|
|
141 |
.675
|
|
142 |
.672
|
|
143 |
.671
|
|
144 |
.670
|
|
145 |
.670
|
|
146 |
.664
|
|
147 |
.663
|
|
148 |
.662
|
|
149 |
.654
|
|
150 |
.653
|
|
151 |
.653
|
|
152 |
.653
|
|
153 |
.652
|
|
154 |
.648
|
|
155 |
.648
|
|
156 |
.648
|
|
157 |
.648
|
|
158 |
.648
|
|
159 |
.647
|
|
160 |
.646
|
|
161 |
.646
|
|
162 |
.645
|
|
163 |
.645
|
|
164 |
.644
|
|
165 |
.644
|
|
166 |
.644
|
|
167 |
.644
|
|
168 |
.644
|
|
169 |
.644
|
|
170 |
.644
|
|
171 |
.644
|
|
172 |
.643
|
|
173 |
.642
|
|
174 |
.642
|
|
175 |
.641
|
|
176 |
.641
|
|
177 |
.641
|
|
178 |
.641
|
|
179 |
.640
|
|
180 |
.639
|
|
181 |
.633
|
|
182 |
.632
|
|
183 |
.630
|
|
184 |
.628
|
|
185 |
.622
|
|
186 |
.621
|
|
187 |
.617
|
|
188 |
.616
|
|
189 |
.616
|
|
190 |
.615
|
|
191 |
.610
|
|
192 |
.603
|
|
193 |
.603
|
|
194 |
.602
|
|
195 |
.592
|
|
196 |
.591
|
|
197 |
.588
|
|
198 |
.583
|
|
199 |
.582
|
|
200 |
.577
|
|
201 |
.577
|
|
202 |
.575
|
|
203 |
.575
|
|
204 |
.568
|
|
205 |
.566
|
|
206 |
.563
|
|
207 |
.557
|
|
208 |
.548
|
|
209 |
.538
|
|
210 |
.538
|
|
211 |
.535
|
|
212 |
.533
|
|
213 |
.533
|
|
214 |
.533
|
|
215 |
.526
|
|
216 |
.519
|
|
217 |
.519
|
|
218 |
.519
|
|
219 |
.519
|
|
220 |
.519
|
|
221 |
.512
|
|
222 |
.508
|
|
223 |
.507
|
|
224 |
.500
|
|
225 |
.498
|
|
226 |
.477
|
|
227 |
.476
|
|
228 |
.471
|
|
229 |
.470
|
|
230 |
.470
|
|
231 |
.463
|
|
232 |
.455
|
|
233 |
.455
|
|
234 |
.437
|
|
235 |
.419
|
|
236 |
.415
|
|
237 |
.414
|
|
238 |
.413
|
|
239 |
.403
|
|
240 |
.402
|
|
241 |
.399
|
|
242 |
.360
|
|
243 |
.355
|
|
244 |
.340
|
|
245 |
.293
|
|
246 |
.283
|
|
247 |
.260
|
|
248 |
.039
|
|
249 |
.019
|
|
250 |
.007
|
|
251 |
.007
|
|
252 |
.005
|
|
253 |
.002
|
|
254 |
.001
|
|
255 |
.001
|
|
256 |
.001
|
|
257 |
.001
|
|
258 |
.001
|
|
259 |
.001
|
|
260 |
.001
|
|
261 |
.001
|
|
262 |
.001
|
|
263 |
.001
|
|
264 |
.001
|
|
265 |
.001
|
|
266 |
|
|
267 |
|
|
268 |
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|
269 |
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|
270 |
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271 |
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272 |
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273 |
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274 |
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275 |
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|
276 |
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277 |
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278 |
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279 |
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280 |
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281 |
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282 |
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283 |
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284 |
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285 |
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286 |
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287 |
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288 |
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289 |
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290 |
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291 |
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292 |
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293 |
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294 |
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295 |
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296 |
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297 |
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298 |
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299 |
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300 |
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301 |
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302 |
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303 |
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304 |
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305 |
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306 |
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307 |
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308 |
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309 |
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310 |
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311 |
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312 |
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313 |
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314 |
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315 |
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316 |
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317 |
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318 |
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319 |
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320 |
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321 |
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322 |
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323 |
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324 |
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325 |
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326 |
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327 |
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328 |
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329 |
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330 |
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331 |
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332 |
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333 |
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334 |
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335 |
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336 |
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337 |
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338 |
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339 |
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340 |
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341 |
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342 |
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343 |
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344 |
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345 |
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346 |
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347 |
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348 |
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349 |
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350 |
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351 |
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352 |
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353 |
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354 |
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355 |
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356 |
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357 |
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358 |
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359 |
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360 |
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361 |
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362 |
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363 |
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364 |
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365 |
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366 |
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367 |
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368 |
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369 |
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370 |
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371 |
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372 |
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373 |
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374 |
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375 |
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376 |
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377 |
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378 |
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379 |
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380 |
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381 |
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382 |
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383 |
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384 |
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385 |
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386 |
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Benchmark bibtex
@INPROCEEDINGS{5206848, author={J. {Deng} and W. {Dong} and R. {Socher} and L. {Li} and {Kai Li} and {Li Fei-Fei}}, booktitle={2009 IEEE Conference on Computer Vision and Pattern Recognition}, title={ImageNet: A large-scale hierarchical image database}, year={2009}, volume={}, number={}, pages={248-255}, }
Ceiling
1.00.Note that scores are relative to this ceiling.
Data: ImageNet
Metric: top1