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Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnext101_32x32d_wsl	.592
2	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.592
3	resnext101_32x48d_wsl	.589
4	resnext101_32x16d_wsl	.587
5	resnet_50_v2	.585
6	eva02_large_in22k	.576
7	focalnet_tiny_lrf_in1k	.562
8	resnet152_imagenet_full	.562
9	vit_relpos_base_patch16_clsgap_224:sw_in1k	.556
10	vit_large_patch14_clip_336:laion2b_ft_in1k	.557
11	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.553
12	seresnext50_32x4d	.551
13	dinov2_vitg14_reg_lc	.548
14	resnet50_st_wzc_seed7_2_323	.547
15	resnet50_imagenet_full	.546
16	Res2Net50_26w_4s	.547
17	deit_large_imagenet_full_seed-0	.545
18	regnety_032	.543
19	dinov2_vitl14_lc	.543
20	resnet50_tutorial	.541
21	resnet50_st_wzc_seed3_2_323	.540
22	dinov2_vitl14_lc_res336	.539
23	deit_base_imagenet_full_seed-0	.538
24	deit_small_imagenet_full_seed-0	.532
25	resnet50-SIN_IN_IN	.528
26	resnet50-moclr8deg	.528
27	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.523
28	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.520
29	antialiased-r50	.521
30	resnet50_ewconplus_wzc_seed5_2__323	.520
31	resnet50-sup	.519
32	resnet_50_v1	.519
33	resnet_50_v1_spiking	.519
34	convnext_xxlarge_clip	.520
35	vit_base_patch16_clip_224:openai_ft_in1k	.518
36	resnet50-SIN_IN	.518
37	voneresnet-50-non_stochastic	.516
38	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.516
39	resnet50_ewconplus_wzc	.515
40	resnet50_ewconplus_wzc_seed2_2__323	.511
41	vit_large_patch14_clip_224:openai_ft_in1k	.507
42	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.503
43	convnext_large_imagenet_full_seed-0	.498
44	convnext_base:clip_laiona_augreg_ft_in1k_384	.491
45	vit_large_patch14_clip_224:laion2b_ft_in1k	.490
46	resnet50-vitoimagevidnet8	.487
47	resnet50-VITO-8deg-cc	.487
48	convnext_base_imagenet_full_seed-0	.485
49	custom_model_cv_18_dagger_408	.484
50	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.482
51	convnext_tiny_sup	.482
52	voneresnet-50	.483
53	convnext_tiny:in12k_ft_in1k	.481
54	dinov2_vitl14_reg_lc	.478
55	ReAlnet01_cornet	.476
56	ReAlnet03_cornet	.476
57	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.475
58	convnext_large:fb_in22k_ft_in1k	.474
59	ReAlnet04_cornet	.471
60	pnasnet_large_halfPrecision	.470
61	resnet50_robust_l2_eps1	.468
62	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.465
63	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.462
64	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.460
65	eicircuit_resnet18_bn_20260919	.458
66	vit_huge_clip336_laion	.456
67	convnext_small_imagenet_full_seed-0	.455
68	densenet-121	.454
69	VOneCORnet-S	.451
70	resnet50-SIN	.449
71	efficientnet_b2_imagenet_full	.447
72	effnetb1_272x240	.444
73	shufflenet_v2_x1_0	.444
74	resnext101_32x8d_wsl	.438
75	efficientnet_b0	.438
76	resnet_SIN_IN_FT_IN	.429
77	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.426
78	ReAlnet01_cornet_fov4	.426
79	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.422
80	convnext_tiny_imagenet_full_seed-0	.422
81	cvt_cvt-13-224-in1k_4_LucyV4	.422
82	cvt_cvt-13-384-in1k_4_LucyV4	.421
83	mobilenet_v2_1_4_224	.415
84	AdvProp_efficientnet-b7	.413
85	pr1_stl10_disentangled_alpha04	.413
86	convnext_xlarge:fb_in22k_ft_in1k	.409
87	convnext_femto_ols:d1_in1k	.407
88	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.407
89	dinov2_vitg14_lc	.404
90	AdvProp_efficientnet-b6	.403
91	cvt_cvt-21-384-in1k_4_LucyV4	.401
92	vit_large_clip336_openai	.398
93	ViT_L_32_imagenet1k	.396
94	AT_efficientnet-b2	.388
95	AdvProp_efficientnet-b2	.388
96	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.384
97	AdvProp_efficientnet-b4	.378
98	AdvProp_efficientnet-b8	.370
99	resnet-50-robust	.370
100	cvt_cvt-21-224-in1k_4_LucyV4	.363
101	grcnn	.328
102	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.307
103	alexnet	.305
104	convnext_base_fb_in1k	.284
105	AlexNet_SIN_fov12	.248
106	AlexNet_SIN	.238
107	resnet18_fair	.223
108	predictive_coding_pc	.203
109	AlexNet_SIN_fov16	.192
110	hmax	.109
111	alexnet_training_seed_07	.091
112	alexnet_training_seed_10	.081
113	alexnet_training_seed_01	.076
114	alexnet_training_seed_03	.075
115	gabor_filter_mean_abs_4x4_v2	.072
116	alexnet_training_seed_02	.063
117	compact_resnet50_robust_V4	.058
118	alexnet_training_seed_09	.057
119	alexnet_training_seed_06	.045
120	gabor_filter_entropy_4x4_v2	.040
121	gabor_filter_entropy_4x4_v2_forcedCosine	.039
122	gabor_filter_entropy_4x4	.017
123	dinov2_vitl14_reg_lc_featread
124	resnet50_st_wzc_seed6_2_323
125	resnet50_ewconplus_wzc_seed6_2_323

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

0.46.

Note that scores are relative to this ceiling.

Data: Allen2022_fmri_surface.IT

Metric: ridge