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
.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
.730
88
.729
89
.729
90
.728
91
.726
92
.724
93
.723
94
.722
95
.722
96
.722
97
.720
98
.718
99
.718
100
.718
101
.718
102
.718
103
.718
104
.715
105
.715
106
.711
107
.709
108
.708
109
.707
110
.706
111
.705
112
.704
113
.704
114
.704
115
.703
116
.703
117
.703
118
.702
119
.702
120
.702
121
.702
122
.702
123
.702
124
.702
125
.701
126
.700
127
.699
128
.698
129
.698
130
.698
131
.697
132
.697
133
.694
134
.692
135
.688
136
.687
137
.687
138
.684
139
.684
140
.681
141
.680
142
.675
143
.672
144
.671
145
.670
146
.670
147
.664
148
.663
149
.662
150
.654
151
.653
152
.653
153
.653
154
.652
155
.648
156
.648
157
.648
158
.648
159
.648
160
.647
161
.646
162
.646
163
.645
164
.645
165
.645
166
.645
167
.645
168
.644
169
.644
170
.644
171
.644
172
.644
173
.644
174
.644
175
.644
176
.643
177
.642
178
.642
179
.641
180
.641
181
.641
182
.641
183
.640
184
.639
185
.633
186
.632
187
.630
188
.628
189
.622
190
.621
191
.617
192
.616
193
.616
194
.615
195
.610
196
.603
197
.603
198
.602
199
.592
200
.591
201
.588
202
.583
203
.582
204
.577
205
.575
206
.575
207
.568
208
.566
209
.563
210
.557
211
.548
212
.538
213
.535
214
.533
215
.533
216
.533
217
.526
218
.519
219
.519
220
.519
221
.519
222
.519
223
.512
224
.508
225
.507
226
.500
227
.498
228
.477
229
.476
230
.471
231
.470
232
.470
233
.463
234
.455
235
.455
236
.437
237
.419
238
.415
239
.414
240
.413
241
.403
242
.402
243
.399
244
.360
245
.355
246
.340
247
.340
248
.340
249
.340
250
.293
251
.283
252
.260
253
.039
254
.019
255
.007
256
.007
257
.005
258
.002
259
.001
260
.001
261
.001
262
.001
263
.001
264
.001
265
.001
266
.001
267
.001
268
.001
269
.001
270
.001
271
.001
272
.001
273
.001
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
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

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