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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Allen2022_fmri_surface.V4-ridge")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	ijepa_vith14_linhead	.523
2	resnet50_vicreg_linhead	.510
3	resnet50_barlowtwins_linhead	.508
4	resnet50_byol_linhead	.506
5	vit_huge_clip336_laion	.504
6	custom_model_cv_18_dagger_408	.499
7	resnet50_simclr_linhead_01norm	.489
8	dinov2_vitg14_reg_lc	.486
9	resnet50_simsiam_linhead	.483
10	vit_large_clip336_openai	.479
11	dinov2_vitg14_lc	.472
12	dinov2_vitl14_lc_res336	.472
13	convnext_xxlarge_clip	.458
14	shufflenet_v2_x1_0	.455
15	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.454
16	seresnext50_32x4d	.453
17	resnet50_st_wzc_seed3_2_323	.449
18	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.449
19	cvt_cvt-21-224-in1k_4_LucyV4	.448
20	convnext_xlarge:fb_in22k_ft_in1k	.449
21	resnet50_st_wzc_seed7_2_323	.447
22	cvt_cvt-13-384-in1k_4_LucyV4	.448
23	cvt_cvt-21-384-in1k_4_LucyV4	.448
24	pnasnet_large_halfPrecision	.445
25	resnet152_imagenet_full	.443
26	vit_relpos_base_patch16_clsgap_224:sw_in1k	.440
27	regnety_032	.439
28	cvt_cvt-13-224-in1k_4_LucyV4	.438
29	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.438
30	vit_large_patch14_clip_224:openai_ft_in1k	.438
31	resnet152_ecoset_full	.435
32	resnet101_imagenet_full	.434
33	convnext_tiny:in12k_ft_in1k	.434
34	resnet_50_v2	.431
35	resnet50-moclr8deg	.430
36	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.429
37	resnet101_ecoset_full	.426
38	deit_small_imagenet_full_seed-0	.428
39	eva02_large_in22k	.427
40	resnet50_ewconplus_wzc	.425
41	ReAlnet07_cornet	.424
42	ReAlnet10_cornet	.424
43	ReAlnet01_cornet	.423
44	ReAlnet05_cornet	.423
45	dinov2_vitl14_reg_lc_featread	.423
46	dinov2_vitl14_reg_lc	.423
47	ReAlnet08_cornet	.422
48	resnet50-vitoimagevidnet8	.423
49	resnet50-VITO-8deg-cc	.423
50	vit_large_patch14_clip_336:laion2b_ft_in1k	.422
51	voneresnet-50	.421
52	ReAlnet09_cornet	.420
53	ReAlnet02_cornet	.419
54	ReAlnet04_cornet	.419
55	ReAlnet03_cornet	.418
56	resnet50_ewconplus_wzc_seed6_2_323	.417
57	resnext101_32x32d_wsl	.416
58	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.413
59	resnext101_32x48d_wsl	.410
60	convnext_large:fb_in22k_ft_in1k	.409
61	resnet50-SIN_IN	.406
62	focalnet_tiny_lrf_in1k	.404
63	resnet_SIN_IN_FT_IN	.405
64	resnet50-SIN_IN_IN	.405
65	AdvProp_efficientnet-b2	.405
66	AT_efficientnet-b2	.405
67	ReAlnet01_cornet_fov12	.404
68	alexnet	.401
69	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.402
70	efficientnet_b0	.400
71	resnet_50_v1_spiking	.398
72	resnet_50_v1	.398
73	resnet50-sup	.398
74	resnet50_tutorial	.398
75	resnet50-SIN	.399
76	Res2Net50_26w_4s	.397
77	resnext101_32x16d_wsl	.397
78	antialiased-r50	.396
79	resnext101_32x8d_wsl	.396
80	VOneCORnet-S	.394
81	deit_large_imagenet_full_seed-0	.394
82	vit_base_patch16_clip_224:openai_ft_in1k	.395
83	deit_base_imagenet_full_seed-0	.393
84	efficientnet_b2_imagenet_full	.391
85	convnext_large_imagenet_full_seed-0	.390
86	vit_large_patch14_clip_224:laion2b_ft_in1k	.390
87	convnext_tiny_sup	.388
88	convnext_tiny_imagenet_full_seed-0	.388
89	resnet50_ewconplus_wzc_seed5_2__323	.383
90	efficientnet_b1_imagenet_full	.382
91	effnetb1_272x240	.380
92	resnet50_ecoset_full	.380
93	convnext_base_imagenet_full_seed-0	.379
94	efficientnet_b0_imagenet_full	.378
95	AdvProp_efficientnet-b8	.379
96	resnet50_ewconplus_wzc_seed2_2__323	.373
97	ReAlnet01_cornet_fov16	.371
98	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.371
99	dinov2_vitl14_lc	.371
100	AdvProp_efficientnet-b4	.370
101	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.371
102	resnet50_imagenet_full	.367
103	convnext_small_imagenet_full_seed-0	.365
104	convnext_femto_ols:d1_in1k	.363
105	resnet50_st_wzc_seed6_2_323	.363
106	mobilenet_v2_1_4_224	.362
107	resnet18_imagenet_full	.361
108	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.360
109	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.363
110	convnext_base:clip_laiona_augreg_ft_in1k_384	.359
111	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.359
112	mehrer2020_alexnet_training_seed_04	.352
113	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.351
114	resnet34_ecoset_full	.353
115	AlexNet_SIN	.352
116	resnet34_imagenet_full	.351
117	voneresnet-50-non_stochastic	.350
118	mehrer2020_alexnet_training_seed_05	.348
119	AdvProp_efficientnet-b7	.347
120	eicircuit_resnet18_bn_20260919	.346
121	mehrer2020_alexnet_training_seed_01	.345
122	resnet50_imagenet_100_seed-0	.346
123	mehrer2020_alexnet_training_seed_02	.344
124	mehrer2020_alexnet_training_seed_03	.339
125	convnext_base_fb_in1k	.338
126	grcnn	.333
127	AdvProp_efficientnet-b6	.329
128	resnet18_fair	.328
129	mehrer2020_alexnet_training_seed_09	.327
130	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.326
131	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.325
132	mehrer2020_alexnet_training_seed_06	.325
133	mehrer2020_alexnet_training_seed_08	.322
134	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.320
135	resnet50_robust_l2_eps1	.319
136	mehrer2020_alexnet_training_seed_07	.312
137	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.313
138	convnext_small_imagenet_100_seed-0	.304
139	ReAlnet01_cornet_fov4	.306
140	ViT_L_32_imagenet1k	.285
141	densenet-121	.282
142	mehrer2020_alexnet_training_seed_10	.282
143	mehrer2020_alexnet_training_seed_01_fov12	.279
144	AlexNet_SIN_fov12	.269
145	mehrer2020_alexnet_training_seed_01_fov16	.264
146	deit_small_imagenet_100_seed-0	.260
147	resnet-50-robust	.242
148	pr1_stl10_disentangled_alpha04	.235
149	mehrer2020_alexnet_training_seed_01_fov4	.235
150	predictive_coding_pc	.219
151	AlexNet_SIN_fov16	.210
152	AlexNet_SIN_fov4	.191
153	alexnet_training_seed_01	.179
154	alexnet_training_seed_06	.176
155	alexnet_training_seed_09	.172
156	alexnet_training_seed_10	.168
157	alexnet_training_seed_02	.165
158	alexnet_training_seed_03	.161
159	alexnet_training_seed_07	.159
160	gabor_filter_mean_abs_4x4_v2	.127
161	gabor_filter_entropy_4x4_v2	.111
162	gabor_filter_entropy_4x4_v2_forcedCosine	.111
163	hmax	.054
164	compact_resnet50_robust_V4	.042
165	gabor_filter_entropy_4x4	.039
166	resnet18_ecoset_full
167	ReAlnet06_cornet

Benchmark bibtex

@article{allen_massive_2022,
    title = {A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence},
    volume = {25},
    issn = {1097-6256},
    doi = {10.1038/s41593-021-00962-x},
    journal = {Nature Neuroscience},
    author = {Allen, Emily J. and St-Yves, Ghislain and Wu, Yihan and Breedlove, Jesse L.
              and Prince, Jacob S. and Dowdle, Logan T. and Nau, Matthias and Caron, Brad
              and Pestilli, Franco and Charest, Ian and Hutchinson, J. Benjamin
              and Naselaris, Thomas and Kay, Kendrick},
    year = {2022},
    pages = {116--126},
}

Ceiling

0.51.

Note that scores are relative to this ceiling.

Data: Allen2022_fmri_surface.V4

Metric: ridge