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
.644
166
.644
167
.644
168
.644
169
.644
170
.644
171
.644
172
.644
173
.643
174
.642
175
.642
176
.641
177
.641
178
.641
179
.641
180
.640
181
.639
182
.633
183
.632
184
.630
185
.628
186
.622
187
.621
188
.617
189
.616
190
.616
191
.615
192
.610
193
.603
194
.603
195
.602
196
.592
197
.591
198
.588
199
.583
200
.582
201
.577
202
.577
203
.575
204
.575
205
.568
206
.566
207
.563
208
.557
209
.548
210
.538
211
.538
212
.535
213
.533
214
.533
215
.533
216
.526
217
.519
218
.519
219
.519
220
.519
221
.519
222
.512
223
.508
224
.507
225
.500
226
.498
227
.477
228
.476
229
.471
230
.470
231
.470
232
.463
233
.455
234
.455
235
.437
236
.419
237
.415
238
.414
239
.413
240
.403
241
.402
242
.399
243
.360
244
.355
245
.340
246
.293
247
.283
248
.260
249
.039
250
.019
251
.007
252
.007
253
.005
254
.002
255
.001
256
.001
257
.001
258
.001
259
.001
260
.001
261
.001
262
.001
263
.001
264
.001
265
.001
266
.001
267
268
269
270
271
272
273
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

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