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

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