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("FreemanZiemba2013.V2-pls")
score = benchmark(my_model)

Model scores

Min Alignment Max Alignment

Rank

Model

Score

1
.402
2
.402
3
.397
4
.393
5
.391
6
.384
7
.380
8
.373
9
.369
10
.365
11
.364
12
.362
13
.361
14
.360
15
.360
16
.360
17
.360
18
.357
19
.356
20
.356
21
.356
22
.353
23
.353
24
.353
25
.353
26
.353
27
.353
28
.353
29
.353
30
.353
31
.353
32
.353
33
.353
34
.353
35
.349
36
.346
37
.346
38
.345
39
.342
40
.342
41
.341
42
.341
43
.341
44
.341
45
.340
46
.340
47
.339
48
.339
49
.339
50
.339
51
.339
52
.338
53
.337
54
.337
55
.336
56
.336
57
.336
58
.335
59
.335
60
.335
61
.334
62
.334
63
.334
64
.334
65
.333
66
.333
67
.332
68
.332
69
.332
70
.332
71
.332
72
.332
73
.331
74
.331
75
.330
76
.330
77
.329
78
.329
79
.329
80
.328
81
.328
82
.328
83
.327
84
.327
85
.327
86
.326
87
.326
88
.325
89
.325
90
.324
91
.324
92
.324
93
.324
94
.323
95
.323
96
.323
97
.322
98
.322
99
.322
100
.322
101
.322
102
.321
103
.321
104
.321
105
.321
106
.321
107
.321
108
.320
109
.320
110
.320
111
.320
112
.320
113
.320
114
.320
115
.320
116
.320
117
.320
118
.320
119
.320
120
.319
121
.319
122
.319
123
.318
124
.318
125
.318
126
.318
127
.318
128
.318
129
.317
130
.317
131
.317
132
.317
133
.317
134
.317
135
.316
136
.316
137
.315
138
.315
139
.315
140
.314
141
.313
142
.313
143
.313
144
.313
145
.313
146
.312
147
.312
148
.312
149
.312
150
.312
151
.312
152
.312
153
.311
154
.311
155
.311
156
.311
157
.311
158
.311
159
.311
160
.311
161
.310
162
.310
163
.309
164
.309
165
.309
166
.309
167
.308
168
.308
169
.308
170
.308
171
.308
172
.308
173
.308
174
.307
175
.307
176
.307
177
.307
178
.307
179
.307
180
.307
181
.306
182
.306
183
.306
184
.306
185
.306
186
.306
187
.306
188
.306
189
.306
190
.306
191
.306
192
.305
193
.305
194
.305
195
.305
196
.305
197
.305
198
.304
199
.304
200
.304
201
.304
202
.304
203
.304
204
.304
205
.304
206
.303
207
.303
208
.303
209
.302
210
.302
211
.302
212
.302
213
.302
214
.301
215
.301
216
.301
217
.301
218
.301
219
.301
220
.301
221
.300
222
.300
223
.300
224
.299
225
.299
226
.299
227
.299
228
.299
229
.298
230
.298
231
.298
232
.298
233
.297
234
.297
235
.296
236
.296
237
.296
238
.295
239
.295
240
.295
241
.295
242
.295
243
.295
244
.295
245
.295
246
.295
247
.295
248
.295
249
.294
250
.294
251
.294
252
.294
253
.294
254
.293
255
.293
256
.293
257
.293
258
.293
259
.293
260
.293
261
.293
262
.293
263
.292
264
.291
265
.291
266
.291
267
.291
268
.291
269
.290
270
.290
271
.289
272
.289
273
.289
274
.288
275
.288
276
.288
277
.288
278
.287
279
.287
280
.287
281
.287
282
.286
283
.286
284
.286
285
.286
286
.285
287
.285
288
.285
289
.285
290
.285
291
.284
292
.284
293
.284
294
.283
295
.283
296
.282
297
.282
298
.281
299
.281
300
.280
301
.279
302
.278
303
.278
304
.278
305
.278
306
.278
307
.276
308
.275
309
.275
310
.274
311
.274
312
.273
313
.273
314
.273
315
.272
316
.272
317
.272
318
.271
319
.270
320
.270
321
.270
322
.269
323
.269
324
.267
325
.267
326
.266
327
.265
328
.265
329
.265
330
.265
331
.265
332
.263
333
.263
334
.263
335
.262
336
.262
337
.261
338
.261
339
.261
340
.260
341
.260
342
.260
343
.259
344
.259
345
.258
346
.257
347
.257
348
.256
349
.255
350
.255
351
.255
352
.254
353
.254
354
.252
355
.252
356
.251
357
.251
358
.250
359
.250
360
.249
361
.249
362
.249
363
.248
364
.248
365
.248
366
.247
367
.246
368
.246
369
.246
370
.246
371
.243
372
.243
373
.243
374
.242
375
.238
376
.238
377
.238
378
.238
379
.238
380
.238
381
.238
382
.238
383
.238
384
.238
385
.238
386
.237
387
.237
388
.234
389
.234
390
.233
391
.232
392
.232
393
.232
394
.231
395
.231
396
.230
397
.230
398
.230
399
.229
400
.229
401
.228
402
.228
403
.227
404
.226
405
.226
406
.223
407
.222
408
.220
409
.220
410
.218
411
.218
412
.218
413
.218
414
.218
415
.218
416
.218
417
.218
418
.217
419
.217
420
.216
421
.216
422
.215
423
.204
424
.202
425
.201
426
.200
427
.198
428
.193
429
.189
430
.182
431
.180
432
.176
433
.174
434
.165
435
.165
436
.150
437
.132
438
.130
439
.128
440
.126
441
.126
442
.120
443
.116
444
.111
445
.107
446
.056
447
.029
448
.024
449
.008
450
.007
451
.005
452
.003
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467

Benchmark bibtex

@Article{Freeman2013,
                author={Freeman, Jeremy
                and Ziemba, Corey M.
                and Heeger, David J.
                and Simoncelli, Eero P.
                and Movshon, J. Anthony},
                title={A functional and perceptual signature of the second visual area in primates},
                journal={Nature Neuroscience},
                year={2013},
                month={Jul},
                day={01},
                volume={16},
                number={7},
                pages={974-981},
                abstract={The authors examined neuronal responses in V1 and V2 to synthetic texture stimuli that replicate higher-order statistical dependencies found in natural images. V2, but not V1, responded differentially to these textures, in both macaque (single neurons) and human (fMRI). Human detection of naturalistic structure in the same images was predicted by V2 responses, suggesting a role for V2 in representing natural image structure.},
                issn={1546-1726},
                doi={10.1038/nn.3402},
                url={https://doi.org/10.1038/nn.3402}
                }
            

Ceiling

0.82.

Note that scores are relative to this ceiling.

Data: FreemanZiemba2013.V2

315 stimuli recordings from 103 sites in V2

Metric: pls