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

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