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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Allen2022_fmri_surface.V2-ridge")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_huge_clip336_laion	.537
2	dinov2_vitl14_lc	.522
3	dinov2_vitl14_lc_res336	.520
4	dinov2_vitg14_reg_lc	.519
5	vit_large_clip336_openai	.518
6	dinov2_vitg14_lc	.518
7	vit_relpos_base_patch16_clsgap_224:sw_in1k	.517
8	convnext_large:fb_in22k_ft_in1k	.513
9	cvt_cvt-13-384-in1k_4_LucyV4	.512
10	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.507
11	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.507
12	focalnet_tiny_lrf_in1k	.505
13	seresnext50_32x4d	.503
14	convnext_large_imagenet_full_seed-0	.502
15	convnext_xlarge:fb_in22k_ft_in1k	.503
16	resnet50_ewconplus_wzc_seed6_2_323	.502
17	deit_small_imagenet_full_seed-0	.503
18	resnet_SIN_IN_FT_IN	.501
19	resnet50-SIN_IN_IN	.501
20	resnet50_st_wzc_seed7_2_323	.500
21	resnet50_imagenet_full	.500
22	pnasnet_large_halfPrecision	.499
23	resnet50-moclr8deg	.499
24	regnety_032	.500
25	resnext101_32x16d_wsl	.499
26	resnext101_32x8d_wsl	.499
27	resnet50_st_wzc_seed6_2_323	.498
28	resnet50_st_wzc_seed3_2_323	.496
29	deit_base_imagenet_full_seed-0	.496
30	resnet50-SIN_IN	.496
31	voneresnet-50-non_stochastic	.495
32	Res2Net50_26w_4s	.495
33	resnet_50_v2	.494
34	antialiased-r50	.494
35	resnet50_ewconplus_wzc_seed5_2__323	.494
36	dinov2_vitl14_reg_lc_featread	.494
37	dinov2_vitl14_reg_lc	.494
38	deit_large_imagenet_full_seed-0	.493
39	cvt_cvt-21-384-in1k_4_LucyV4	.493
40	resnet_50_v1_spiking	.490
41	resnet50-sup	.490
42	resnet_50_v1	.490
43	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.491
44	resnet50_ewconplus_wzc_seed2_2__323	.489
45	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.487
46	voneresnet-50	.487
47	vit_large_patch14_clip_336:laion2b_ft_in1k	.486
48	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.485
49	resnet50-VITO-8deg-cc	.482
50	resnet50-vitoimagevidnet8	.482
51	resnet152_imagenet_full	.482
52	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.482
53	shufflenet_v2_x1_0	.478
54	cvt_cvt-21-224-in1k_4_LucyV4	.478
55	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.477
56	eva02_large_in22k	.475
57	custom_model_cv_18_dagger_408	.474
58	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.473
59	convnext_xxlarge_clip	.474
60	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.473
61	convnext_tiny_sup	.471
62	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.470
63	resnet50_ewconplus_wzc	.469
64	eicircuit_resnet18_bn_20260919	.467
65	convnext_base:clip_laiona_augreg_ft_in1k_384	.468
66	resnet50_tutorial	.463
67	resnet50-SIN	.462
68	vit_large_patch14_clip_224:openai_ft_in1k	.461
69	vit_large_patch14_clip_224:laion2b_ft_in1k	.460
70	resnext101_32x32d_wsl	.460
71	cvt_cvt-13-224-in1k_4_LucyV4	.459
72	densenet-121	.458
73	AdvProp_efficientnet-b6	.457
74	AdvProp_efficientnet-b7	.455
75	convnext_tiny:in12k_ft_in1k	.454
76	resnext101_32x48d_wsl	.451
77	convnext_tiny_imagenet_full_seed-0	.448
78	AdvProp_efficientnet-b8	.443
79	alexnet	.441
80	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.438
81	vit_base_patch16_clip_224:openai_ft_in1k	.438
82	resnet50_robust_l2_eps1	.437
83	effnetb1_272x240	.437
84	AdvProp_efficientnet-b4	.436
85	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.431
86	ReAlnet01_cornet	.424
87	ReAlnet03_cornet	.424
88	grcnn	.422
89	ReAlnet04_cornet	.423
90	efficientnet_b2_imagenet_full	.415
91	convnext_base_fb_in1k	.416
92	convnext_base_imagenet_full_seed-0	.413
93	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.402
94	mobilenet_v2_1_4_224	.400
95	convnext_small_imagenet_full_seed-0	.398
96	convnext_femto_ols:d1_in1k	.396
97	resnet-50-robust	.391
98	efficientnet_b0	.390
99	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.377
100	VOneCORnet-S	.357
101	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.351
102	resnet18_fair	.350
103	AT_efficientnet-b2	.347
104	AdvProp_efficientnet-b2	.347
105	AlexNet_SIN	.323
106	AlexNet_SIN_fov12	.302
107	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.284
108	AlexNet_SIN_fov16	.276
109	alexnet_training_seed_03	.264
110	ReAlnet01_cornet_fov4	.257
111	alexnet_training_seed_06	.252
112	alexnet_training_seed_02	.240
113	pr1_stl10_disentangled_alpha04	.232
114	ViT_L_32_imagenet1k	.221
115	alexnet_training_seed_09	.223
116	alexnet_training_seed_01	.218
117	alexnet_training_seed_07	.210
118	alexnet_training_seed_10	.207
119	predictive_coding_pc	.189
120	gabor_filter_entropy_4x4_v2_forcedCosine	.182
121	gabor_filter_entropy_4x4_v2	.180
122	gabor_filter_mean_abs_4x4_v2	.138
123	compact_resnet50_robust_V4	.076
124	hmax	.055
125	gabor_filter_entropy_4x4	.052

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

0.50.

Note that scores are relative to this ceiling.

Data: Allen2022_fmri_surface.V2

Metric: ridge