Sample stimuli

sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9

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
.839
13
.835
14
.829
15
.828
16
.828
17
.827
18
.827
19
.824
20
.822
21
.809
22
.809
23
.805
24
.805
25
.804
26
.802
27
.799
28
.798
29
.795
30
.793
31
.792
32
.792
33
.790
34
.781
35
.780
36
.780
37
.778
38
.777
39
.777
40
.777
41
.776
42
.775
43
.774
44
.774
45
.772
46
.772
47
.768
48
.767
49
.766
50
.766
51
.764
52
.764
53
.762
54
.761
55
.760
56
.759
57
.758
58
.758
59
.757
60
.756
61
.752
62
.752
63
.751
64
.751
65
.750
66
.750
67
.750
68
.749
69
.749
70
.749
71
.748
72
.748
73
.747
74
.746
75
.746
76
.745
77
.745
78
.745
79
.745
80
.744
81
.744
82
.744
83
.741
84
.740
85
.740
86
.739
87
.739
88
.739
89
.739
90
.737
91
.736
92
.735
93
.735
94
.735
95
.733
96
.733
97
.732
98
.732
99
.732
100
.732
101
.732
102
.731
103
.731
104
.731
105
.731
106
.730
107
.730
108
.729
109
.729
110
.728
111
.728
112
.726
113
.726
114
.724
115
.723
116
.722
117
.722
118
.722
119
.720
120
.718
121
.718
122
.718
123
.718
124
.718
125
.718
126
.715
127
.715
128
.711
129
.709
130
.708
131
.707
132
.706
133
.705
134
.704
135
.704
136
.704
137
.703
138
.703
139
.703
140
.702
141
.702
142
.702
143
.702
144
.702
145
.702
146
.702
147
.701
148
.700
149
.699
150
.698
151
.698
152
.698
153
.697
154
.697
155
.694
156
.692
157
.688
158
.687
159
.687
160
.684
161
.684
162
.682
163
.681
164
.680
165
.675
166
.672
167
.671
168
.670
169
.670
170
.664
171
.663
172
.662
173
.654
174
.653
175
.653
176
.653
177
.652
178
.648
179
.648
180
.648
181
.648
182
.648
183
.647
184
.646
185
.646
186
.645
187
.645
188
.645
189
.645
190
.645
191
.644
192
.644
193
.644
194
.644
195
.644
196
.644
197
.644
198
.644
199
.643
200
.642
201
.642
202
.641
203
.641
204
.641
205
.641
206
.640
207
.639
208
.633
209
.632
210
.630
211
.628
212
.622
213
.621
214
.617
215
.616
216
.616
217
.615
218
.610
219
.603
220
.603
221
.602
222
.592
223
.591
224
.588
225
.583
226
.582
227
.577
228
.575
229
.575
230
.575
231
.568
232
.566
233
.563
234
.557
235
.548
236
.538
237
.535
238
.533
239
.533
240
.533
241
.526
242
.519
243
.519
244
.519
245
.519
246
.519
247
.512
248
.508
249
.507
250
.500
251
.498
252
.477
253
.476
254
.471
255
.470
256
.470
257
.463
258
.455
259
.455
260
.437
261
.419
262
.415
263
.414
264
.413
265
.403
266
.402
267
.399
268
.360
269
.355
270
.340
271
.340
272
.340
273
.340
274
.293
275
.283
276
.260
277
.039
278
.019
279
.007
280
.007
281
.005
282
.002
283
.001
284
.001
285
.001
286
.001
287
.001
288
.001
289
.001
290
.001
291
.001
292
.001
293
.001
294
.001
295
.001
296
.001
297
.001
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443

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