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("tong.Coggan2024_fMRI.V2-rdm")
score = benchmark(my_model)

Model scores

Min Alignment Max Alignment

Rank

Model

Score

1
.947
2
.941
3
.906
4
.636
5
.630
6
.626
7
.578
8
.571
9
.570
10
.492
11
.490
12
.485
13
.458
14
.455
15
.438
16
.415
17
.407
18
.383
19
.372
20
.369
21
.361
22
.361
23
.335
24
.335
25
.327
26
.313
27
.311
28
.303
29
.285
30
.281
31
.278
32
.273
33
.269
34
.268
35
.263
36
.262
37
.255
38
.254
39
.252
40
.247
41
.231
42
.227
43
.226
44
.220
45
.220
46
.216
47
.212
48
.206
49
.206
50
.203
51
.201
52
.200
53
.199
54
.197
55
.194
56
.189
57
.181
58
.178
59
.178
60
.175
61
.172
62
.170
63
.167
64
.167
65
.164
66
.163
67
.163
68
.161
69
.159
70
.159
71
.155
72
.149
73
.148
74
.147
75
.145
76
.143
77
.142
78
.141
79
.138
80
.138
81
.137
82
.135
83
.134
84
.133
85
.133
86
.133
87
.131
88
.130
89
.121
90
.121
91
.121
92
.121
93
.121
94
.120
95
.119
96
.118
97
.118
98
.117
99
.116
100
.114
101
.112
102
.111
103
.111
104
.107
105
.107
106
.107
107
.107
108
.106
109
.105
110
.105
111
.104
112
.101
113
.100
114
.100
115
.100
116
.097
117
.097
118
.097
119
.097
120
.096
121
.094
122
.094
123
.094
124
.092
125
.089
126
.089
127
.088
128
.087
129
.087
130
.086
131
.085
132
.084
133
.081
134
.080
135
.080
136
.080
137
.079
138
.078
139
.077
140
.076
141
.074
142
.074
143
.073
144
.073
145
.072
146
.072
147
.072
148
.071
149
.069
150
.067
151
.064
152
.063
153
.063
154
.062
155
.061
156
.061
157
.061
158
.061
159
.061
160
.061
161
.059
162
.058
163
.057
164
.057
165
.057
166
.056
167
.056
168
.055
169
.054
170
.053
171
.052
172
.051
173
.050
174
.048
175
.047
176
.047
177
.047
178
.045
179
.044
180
.044
181
.043
182
.043
183
.041
184
.040
185
.040
186
.040
187
.040
188
.040
189
.038
190
.038
191
.037
192
.037
193
.037
194
.037
195
.037
196
.036
197
.036
198
.036
199
.036
200
.035
201
.035
202
.034
203
.033
204
.033
205
.033
206
.033
207
.032
208
.032
209
.032
210
.031
211
.031
212
.031
213
.030
214
.030
215
.030
216
.030
217
.029
218
.029
219
.028
220
.028
221
.027
222
.026
223
.026
224
.026
225
.025
226
.025
227
.025
228
.025
229
.024
230
.024
231
.024
232
.023
233
.023
234
.023
235
.022
236
.022
237
.021
238
.021
239
.020
240
.020
241
.020
242
.018
243
.018
244
.016
245
.016
246
.016
247
.016
248
.015
249
.015
250
.014
251
.014
252
.014
253
.014
254
.014
255
.013
256
.012
257
.011
258
.011
259
.010
260
.010
261
.010
262
.009
263
.008
264
.008
265
.006
266
.005
267
.005
268
.005
269
.004
270
.004
271
.004
272
.003
273
.003
274
.003
275
.003
276
.003
277
.002
278
.002
279
.002
280
.001
281
.001
282
.001
283
.001
284
.001
285
.000
286
.000
287
.000
288
.000
289
.000
290
.000
291
.000
292
.000
293
.001
294
.001
295
.001
296
.001
297
.002
298
.002
299
.003
300
.003
301
.005
302
303
1.0
304
1.0
305
1.0
306
1.0
307
308
309
310
311
312
313
314

Benchmark bibtex

@inproceedings{santurkar2019computer,
    title={Computer Vision with a Single (Robust) Classifier},
    author={Shibani Santurkar and Dimitris Tsipras and Brandon Tran and Andrew Ilyas and Logan Engstrom and Aleksander Madry},
    booktitle={ArXiv preprint arXiv:1906.09453},
    year={2019}
}

Ceiling

0.45.

Note that scores are relative to this ceiling.

Data: tong.Coggan2024_fMRI.V2

Metric: rdm