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
.857
3
.854
4
.854
5
.853
6
.852
7
.851
8
.851
9
.850
10
.845
11
.845
12
.842
13
.841
14
.839
15
.835
16
.829
17
.828
18
.828
19
.827
20
.827
21
.824
22
.822
23
.809
24
.809
25
.805
26
.805
27
.804
28
.802
29
.799
30
.798
31
.795
32
.793
33
.792
34
.792
35
.790
36
.783
37
.781
38
.780
39
.780
40
.778
41
.777
42
.777
43
.777
44
.776
45
.775
46
.774
47
.774
48
.772
49
.772
50
.768
51
.767
52
.766
53
.766
54
.764
55
.764
56
.762
57
.761
58
.760
59
.759
60
.758
61
.758
62
.757
63
.756
64
.752
65
.752
66
.751
67
.751
68
.750
69
.750
70
.750
71
.749
72
.749
73
.749
74
.748
75
.747
76
.746
77
.745
78
.745
79
.744
80
.744
81
.744
82
.741
83
.740
84
.740
85
.739
86
.739
87
.739
88
.739
89
.736
90
.735
91
.735
92
.733
93
.733
94
.732
95
.732
96
.732
97
.732
98
.732
99
.731
100
.730
101
.730
102
.729
103
.729
104
.728
105
.728
106
.726
107
.726
108
.724
109
.723
110
.722
111
.722
112
.722
113
.722
114
.720
115
.718
116
.718
117
.718
118
.718
119
.718
120
.718
121
.715
122
.715
123
.711
124
.709
125
.708
126
.707
127
.706
128
.705
129
.704
130
.704
131
.704
132
.703
133
.703
134
.703
135
.702
136
.702
137
.702
138
.702
139
.702
140
.702
141
.702
142
.701
143
.700
144
.699
145
.698
146
.698
147
.698
148
.697
149
.697
150
.694
151
.692
152
.688
153
.687
154
.687
155
.684
156
.684
157
.682
158
.681
159
.680
160
.675
161
.672
162
.671
163
.670
164
.670
165
.670
166
.664
167
.663
168
.662
169
.654
170
.653
171
.653
172
.653
173
.652
174
.648
175
.648
176
.648
177
.648
178
.648
179
.647
180
.646
181
.646
182
.645
183
.645
184
.645
185
.645
186
.645
187
.644
188
.644
189
.644
190
.644
191
.644
192
.644
193
.644
194
.644
195
.643
196
.642
197
.642
198
.641
199
.641
200
.641
201
.641
202
.640
203
.639
204
.633
205
.632
206
.630
207
.628
208
.622
209
.621
210
.617
211
.616
212
.616
213
.615
214
.610
215
.603
216
.603
217
.602
218
.592
219
.591
220
.588
221
.583
222
.582
223
.577
224
.575
225
.575
226
.575
227
.568
228
.566
229
.563
230
.557
231
.548
232
.538
233
.535
234
.533
235
.533
236
.533
237
.526
238
.519
239
.519
240
.519
241
.519
242
.519
243
.512
244
.508
245
.507
246
.500
247
.498
248
.477
249
.476
250
.471
251
.470
252
.470
253
.463
254
.455
255
.455
256
.437
257
.419
258
.415
259
.414
260
.413
261
.403
262
.402
263
.399
264
.360
265
.355
266
.340
267
.340
268
.340
269
.340
270
.293
271
.283
272
.260
273
.039
274
.019
275
.007
276
.007
277
.005
278
.002
279
.001
280
.001
281
.001
282
.001
283
.001
284
.001
285
.001
286
.001
287
.001
288
.001
289
.001
290
.001
291
.001
292
293
294
295
296
297
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
444
445
446
447
448
449
450

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