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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	convnext_large_imagenet_full_seed-0	.502
14	convnext_xlarge:fb_in22k_ft_in1k	.503
15	resnet50_ewconplus_wzc_seed6_2_323	.502
16	deit_small_imagenet_full_seed-0	.503
17	resnet_SIN_IN_FT_IN	.501
18	resnet50-SIN_IN_IN	.501
19	resnet50_st_wzc_seed7_2_323	.500
20	resnet50_imagenet_full	.500
21	pnasnet_large_halfPrecision	.499
22	resnet50-moclr8deg	.499
23	resnext101_32x16d_wsl	.499
24	resnext101_32x8d_wsl	.499
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	dinov2_vitl14_reg_lc_featread	.494
35	dinov2_vitl14_reg_lc	.494
36	deit_large_imagenet_full_seed-0	.493
37	cvt_cvt-21-384-in1k_4_LucyV4	.493
38	resnet_50_v1_spiking	.490
39	resnet50-sup	.490
40	resnet_50_v1	.490
41	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.491
42	resnet50_ewconplus_wzc_seed2_2__323	.489
43	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.487
44	voneresnet-50	.487
45	vit_large_patch14_clip_336:laion2b_ft_in1k	.486
46	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.485
47	resnet50-VITO-8deg-cc	.482
48	resnet50-vitoimagevidnet8	.482
49	resnet152_imagenet_full	.482
50	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.482
51	shufflenet_v2_x1_0	.478
52	cvt_cvt-21-224-in1k_4_LucyV4	.478
53	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.477
54	eva02_large_in22k	.475
55	custom_model_cv_18_dagger_408	.474
56	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.473
57	convnext_xxlarge_clip	.474
58	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.473
59	convnext_tiny_sup	.471
60	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.470
61	resnet50_ewconplus_wzc	.469
62	convnext_base:clip_laiona_augreg_ft_in1k_384	.468
63	resnet50_tutorial	.463
64	resnet50-SIN	.462
65	vit_large_patch14_clip_224:openai_ft_in1k	.461
66	vit_large_patch14_clip_224:laion2b_ft_in1k	.460
67	resnext101_32x32d_wsl	.460
68	cvt_cvt-13-224-in1k_4_LucyV4	.459
69	densenet-121	.458
70	AdvProp_efficientnet-b6	.457
71	AdvProp_efficientnet-b7	.455
72	convnext_tiny:in12k_ft_in1k	.454
73	resnext101_32x48d_wsl	.451
74	convnext_tiny_imagenet_full_seed-0	.448
75	AdvProp_efficientnet-b8	.443
76	alexnet	.441
77	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.438
78	vit_base_patch16_clip_224:openai_ft_in1k	.438
79	resnet50_robust_l2_eps1	.437
80	effnetb1_272x240	.437
81	AdvProp_efficientnet-b4	.436
82	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.431
83	ReAlnet01_cornet	.424
84	ReAlnet03_cornet	.424
85	grcnn	.422
86	ReAlnet04_cornet	.423
87	efficientnet_b2_imagenet_full	.415
88	convnext_base_fb_in1k	.416
89	convnext_base_imagenet_full_seed-0	.413
90	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.402
91	mobilenet_v2_1_4_224	.400
92	convnext_small_imagenet_full_seed-0	.398
93	convnext_femto_ols:d1_in1k	.396
94	resnet-50-robust	.391
95	efficientnet_b0	.390
96	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.377
97	VOneCORnet-S	.357
98	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.351
99	resnet18_fair	.350
100	AT_efficientnet-b2	.347
101	AdvProp_efficientnet-b2	.347
102	AlexNet_SIN	.323
103	AlexNet_SIN_fov12	.302
104	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.284
105	AlexNet_SIN_fov16	.276
106	alexnet_training_seed_03	.264
107	ReAlnet01_cornet_fov4	.257
108	alexnet_training_seed_06	.252
109	alexnet_training_seed_02	.240
110	pr1_stl10_disentangled_alpha04	.232
111	ViT_L_32_imagenet1k	.221
112	alexnet_training_seed_09	.223
113	alexnet_training_seed_01	.218
114	alexnet_training_seed_07	.210
115	alexnet_training_seed_10	.207
116	predictive_coding_pc	.189
117	compact_resnet50_robust_V4	.076
118	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