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
X
4
X
5
X
6
.851
7
X
8
X
9
X
10
.842
11
X
12
X
13
.829
14
X
15
X
16
.827
17
X
18
.822
19
X
20
X
21
.804
22
.802
23
X
24
X
25
X
26
X
27
X
28
X
29
.790
30
X
31
.780
32
X
33
.778
34
X
35
X
36
X
37
X
38
X
39
.774
40
X
41
X
42
.772
43
.768
44
.767
45
X
46
X
47
X
48
.764
49
X
50
X
51
X
52
.759
53
X
54
X
55
X
56
.756
57
.752
58
.752
59
X
60
.750
61
X
62
X
63
X
64
X
65
.746
66
X
67
.745
68
X
69
.744
70
X
71
X
72
.740
73
X
74
X
75
X
76
.739
77
X
78
X
79
X
80
.733
81
X
82
X
83
X
84
X
85
X
86
X
87
X
88
X
89
X
90
X
91
X
92
X
93
X
94
X
95
X
96
X
97
X
98
X
99
X
100
X
101
.718
102
X
103
.715
104
X
105
.711
106
.709
107
X
108
.707
109
X
110
X
111
X
112
X
113
.702
114
X
115
.700
116
X
117
.698
118
.698
119
.698
120
X
121
X
122
X
123
.688
124
.687
125
X
126
.684
127
X
128
X
129
.680
130
X
131
.672
132
X
133
X
134
X
135
.664
136
X
137
X
138
.654
139
X
140
.653
141
.653
142
.652
143
X
144
X
145
X
146
X
147
X
148
X
149
X
150
X
151
X
152
X
153
X
154
X
155
X
156
X
157
X
158
X
159
X
160
X
161
X
162
X
163
X
164
X
165
X
166
X
167
X
168
X
169
.639
170
.633
171
.632
172
X
173
X
174
X
175
.621
176
.617
177
X
178
X
179
X
180
.610
181
.603
182
.603
183
.602
184
X
185
.591
186
.588
187
X
188
.582
189
.577
190
.577
191
.575
192
.575
193
X
194
X
195
.563
196
.557
197
X
198
X
199
X
200
X
201
X
202
X
203
X
204
X
205
X
206
X
207
X
208
X
209
X
210
.512
211
.508
212
X
213
X
214
.498
215
.477
216
X
217
X
218
.470
219
X
220
.455
221
.455
222
X
223
X
224
.415
225
X
226
X
227
X
228
X
229
X
230
X
231
X
232
X
233
X
234
X
235
.260
236
X
237
X
238
X
239
X
240
X
241
X
242
X
243
X
244
X
245
X
246
X
247
X
248
.001
249
X
250
X
251
X
252
X
253
X
254
X
255
X
256
X
257
X
258
X
259
X
260
X
261
X
262
X
263
X
264
X
265
X
266
X
267
X
268
X
269
X
270
X
271
X
272
X
273
X
274
X
275
X
276
X
277
X
278
X
279
X
280
X
281
X
282
X
283
X
284
X
285
X
286
X
287
X
288
X
289
X
290
X
291
X
292
X
293
X
294
X
295
X
296
X
297
X
298
X
299
X
300
X
301
X
302
X
303
X
304
X
305
X
306
X
307
X
308
X
309
X
310
X
311
X
312
X
313
X
314
X
315
X
316
X
317
X
318
X
319
X
320
X
321
X
322
X
323
X
324
X
325
X
326
X
327
X
328
X
329
X
330
X
331
X
332
X
333
X
334
X
335
X
336
X
337
X
338
X
339
X
340
X
341
X
342
X
343
X
344
X
345
X
346
X
347
X
348
X
349
X
350
X
351
X
352
X
353
X
354
X
355
X
356
X
357
X
358
X
359
X
360
X
361
X
362
X
363
X
364
X
365
X
366
X
367
X
368
X
369
X
370
X

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