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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	vit_huge_clip336_laion	.504
2	custom_model_cv_18_dagger_408	.499
3	dinov2_vitg14_reg_lc	.486
4	vit_large_clip336_openai	.479
5	dinov2_vitg14_lc	.472
6	dinov2_vitl14_lc_res336	.472
7	convnext_xxlarge_clip	.458
8	shufflenet_v2_x1_0	.455
9	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.454
10	seresnext50_32x4d	.453
11	resnet50_st_wzc_seed3_2_323	.449
12	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.449
13	cvt_cvt-21-224-in1k_4_LucyV4	.448
14	convnext_xlarge:fb_in22k_ft_in1k	.449
15	resnet50_st_wzc_seed7_2_323	.447
16	cvt_cvt-13-384-in1k_4_LucyV4	.448
17	cvt_cvt-21-384-in1k_4_LucyV4	.448
18	pnasnet_large_halfPrecision	.445
19	resnet152_imagenet_full	.443
20	vit_relpos_base_patch16_clsgap_224:sw_in1k	.440
21	regnety_032	.439
22	cvt_cvt-13-224-in1k_4_LucyV4	.438
23	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.438
24	vit_large_patch14_clip_224:openai_ft_in1k	.438
25	convnext_tiny:in12k_ft_in1k	.434
26	resnet_50_v2	.431
27	resnet50-moclr8deg	.430
28	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.429
29	deit_small_imagenet_full_seed-0	.428
30	eva02_large_in22k	.427
31	resnet50_ewconplus_wzc	.425
32	ReAlnet01_cornet	.423
33	dinov2_vitl14_reg_lc_featread	.423
34	dinov2_vitl14_reg_lc	.423
35	resnet50-vitoimagevidnet8	.423
36	resnet50-VITO-8deg-cc	.423
37	vit_large_patch14_clip_336:laion2b_ft_in1k	.422
38	voneresnet-50	.421
39	ReAlnet04_cornet	.419
40	ReAlnet03_cornet	.418
41	resnet50_ewconplus_wzc_seed6_2_323	.417
42	resnext101_32x32d_wsl	.416
43	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.413
44	resnext101_32x48d_wsl	.410
45	convnext_large:fb_in22k_ft_in1k	.409
46	resnet50-SIN_IN	.406
47	focalnet_tiny_lrf_in1k	.404
48	resnet_SIN_IN_FT_IN	.405
49	resnet50-SIN_IN_IN	.405
50	AdvProp_efficientnet-b2	.405
51	AT_efficientnet-b2	.405
52	alexnet	.401
53	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.402
54	efficientnet_b0	.400
55	resnet_50_v1_spiking	.398
56	resnet_50_v1	.398
57	resnet50-sup	.398
58	resnet50_tutorial	.398
59	resnet50-SIN	.399
60	Res2Net50_26w_4s	.397
61	resnext101_32x16d_wsl	.397
62	antialiased-r50	.396
63	resnext101_32x8d_wsl	.396
64	VOneCORnet-S	.394
65	deit_large_imagenet_full_seed-0	.394
66	vit_base_patch16_clip_224:openai_ft_in1k	.395
67	deit_base_imagenet_full_seed-0	.393
68	efficientnet_b2_imagenet_full	.391
69	convnext_large_imagenet_full_seed-0	.390
70	vit_large_patch14_clip_224:laion2b_ft_in1k	.390
71	convnext_tiny_sup	.388
72	convnext_tiny_imagenet_full_seed-0	.388
73	resnet50_ewconplus_wzc_seed5_2__323	.383
74	effnetb1_272x240	.380
75	convnext_base_imagenet_full_seed-0	.379
76	AdvProp_efficientnet-b8	.379
77	resnet50_ewconplus_wzc_seed2_2__323	.373
78	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.371
79	dinov2_vitl14_lc	.371
80	AdvProp_efficientnet-b4	.370
81	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.371
82	resnet50_imagenet_full	.367
83	convnext_small_imagenet_full_seed-0	.365
84	convnext_femto_ols:d1_in1k	.363
85	resnet50_st_wzc_seed6_2_323	.363
86	mobilenet_v2_1_4_224	.362
87	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.360
88	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.363
89	convnext_base:clip_laiona_augreg_ft_in1k_384	.359
90	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.359
91	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.351
92	AlexNet_SIN	.352
93	voneresnet-50-non_stochastic	.350
94	AdvProp_efficientnet-b7	.347
95	eicircuit_resnet18_bn_20260919	.346
96	convnext_base_fb_in1k	.338
97	grcnn	.333
98	AdvProp_efficientnet-b6	.329
99	resnet18_fair	.328
100	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.326
101	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.325
102	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.320
103	resnet50_robust_l2_eps1	.319
104	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.313
105	ReAlnet01_cornet_fov4	.306
106	ViT_L_32_imagenet1k	.285
107	densenet-121	.282
108	AlexNet_SIN_fov12	.269
109	resnet-50-robust	.242
110	pr1_stl10_disentangled_alpha04	.235
111	predictive_coding_pc	.219
112	AlexNet_SIN_fov16	.210
113	alexnet_training_seed_01	.179
114	alexnet_training_seed_06	.176
115	alexnet_training_seed_09	.172
116	alexnet_training_seed_10	.168
117	alexnet_training_seed_02	.165
118	alexnet_training_seed_03	.161
119	alexnet_training_seed_07	.159
120	gabor_filter_mean_abs_4x4_v2	.127
121	gabor_filter_entropy_4x4_v2	.111
122	gabor_filter_entropy_4x4_v2_forcedCosine	.111
123	hmax	.054
124	compact_resnet50_robust_V4	.042
125	gabor_filter_entropy_4x4	.039

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