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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_relpos_base_patch16_clsgap_224:sw_in1k	.517
2	convnext_large:fb_in22k_ft_in1k	.513
3	cvt_cvt-13-384-in1k_4_LucyV4	.512
4	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.507
5	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.507
6	resnet50_ewconplus_wzc_seed7_2_323	.507
7	focalnet_tiny_lrf_in1k	.505
8	resnet50_ewconplus_wzc_seed4_2_323	.503
9	resnet50_st_wzc_seed5_2_323	.503
10	convnext_large_imagenet_full_seed-0	.502
11	convnext_xlarge:fb_in22k_ft_in1k	.503
12	resnet50_ewconplus_wzc_seed6_2_323	.502
13	deit_small_imagenet_full_seed-0	.503
14	resnet50_st_wzc_2_323	.502
15	resnet_SIN_IN_FT_IN	.501
16	resnet50-SIN_IN_IN	.501
17	resnet50_st_wzc_seed7_2_323	.500
18	resnet50_imagenet_full	.500
19	pnasnet_large_halfPrecision	.499
20	resnet50-moclr8deg	.499
21	resnet50_st_wzc_seed4_2_323	.499
22	resnext101_32x16d_wsl	.499
23	resnext101_32x8d_wsl	.499
24	resnet50_ewconplus_wzc_seed3_2_323	.498
25	resnet50_st_wzc_seed6_2_323	.498
26	resnet50_st_wzc_seed3_2_323	.496
27	deit_base_imagenet_full_seed-0	.496
28	resnet50-SIN_IN	.496
29	voneresnet-50-non_stochastic	.495
30	Res2Net50_26w_4s	.495
31	resnet_50_v2	.494
32	antialiased-r50	.494
33	resnet50_ewconplus_wzc_seed5_2__323	.494
34	deit_large_imagenet_full_seed-0	.493
35	cvt_cvt-21-384-in1k_4_LucyV4	.493
36	resnet_50_v1_spiking	.490
37	resnet50-sup	.490
38	resnet_50_v1	.490
39	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.491
40	resnet50_ewconplus_wzc_seed2_2__323	.489
41	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.487
42	voneresnet-50	.487
43	vit_large_patch14_clip_336:laion2b_ft_in1k	.486
44	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.485
45	resnet50_st_wzc_noseed_2_323	.483
46	resnet50-VITO-8deg-cc	.482
47	resnet50-vitoimagevidnet8	.482
48	resnet152_imagenet_full	.482
49	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.482
50	shufflenet_v2_x1_0	.478
51	cvt_cvt-21-224-in1k_4_LucyV4	.478
52	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.477
53	custom_model_cv_18_dagger_408	.474
54	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.473
55	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.473
56	convnext_tiny_sup	.471
57	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.470
58	resnet50_ewconplus_wzc	.469
59	convnext_base:clip_laiona_augreg_ft_in1k_384	.468
60	resnet50_tutorial	.463
61	resnet50-SIN	.462
62	vit_large_patch14_clip_224:openai_ft_in1k	.461
63	vit_large_patch14_clip_224:laion2b_ft_in1k	.460
64	resnext101_32x32d_wsl	.460
65	cvt_cvt-13-224-in1k_4_LucyV4	.459
66	densenet-121	.458
67	AdvProp_efficientnet-b6	.457
68	AdvProp_efficientnet-b7	.455
69	convnext_tiny:in12k_ft_in1k	.454
70	resnext101_32x48d_wsl	.451
71	convnext_tiny_imagenet_full_seed-0	.448
72	AdvProp_efficientnet-b8	.443
73	alexnet	.441
74	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.438
75	vit_base_patch16_clip_224:openai_ft_in1k	.438
76	resnet50_robust_l2_eps1	.437
77	effnetb1_272x240	.437
78	AdvProp_efficientnet-b4	.436
79	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.431
80	ReAlnet01_cornet	.424
81	ReAlnet03_cornet	.424
82	grcnn	.422
83	ReAlnet04_cornet	.423
84	efficientnet_b2_imagenet_full	.415
85	convnext_base_fb_in1k	.416
86	convnext_base_imagenet_full_seed-0	.413
87	resnet50_ewconplus_wzc_2_323	.408
88	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.402
89	mobilenet_v2_1_4_224	.400
90	convnext_small_imagenet_full_seed-0	.398
91	convnext_femto_ols:d1_in1k	.396
92	resnet-50-robust	.391
93	efficientnet_b0	.390
94	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.377
95	VOneCORnet-S	.357
96	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.351
97	resnet18_fair	.350
98	AT_efficientnet-b2	.347
99	AdvProp_efficientnet-b2	.347
100	AlexNet_SIN	.323
101	AlexNet_SIN_fov12	.302
102	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.284
103	AlexNet_SIN_fov16	.276
104	alexnet_training_seed_03	.264
105	ReAlnet01_cornet_fov4	.257
106	alexnet_training_seed_06	.252
107	alexnet_training_seed_02	.240
108	pr1_stl10_disentangled_alpha04	.232
109	ViT_L_32_imagenet1k	.221
110	alexnet_training_seed_09	.223
111	alexnet_training_seed_01	.218
112	alexnet_training_seed_07	.210
113	alexnet_training_seed_10	.207
114	predictive_coding_pc	.189
115	compact_resnet50_robust_V4	.076
116	hmax	.055

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