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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Allen2022_fmri_surface.V1-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	.558
2	vit_large_clip336_openai	.552
3	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.542
4	vit_large_patch14_clip_224:openai_ft_in1k	.541
5	cvt_cvt-13-384-in1k_4_LucyV4	.540
6	vit_relpos_base_patch16_clsgap_224:sw_in1k	.536
7	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.531
8	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.528
9	regnety_032	.523
10	seresnext50_32x4d	.521
11	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.521
12	eva02_large_in22k	.518
13	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.516
14	resnet50-SIN_IN_IN	.515
15	antialiased-r50	.513
16	resnet_50_v1_spiking	.510
17	resnet_50_v1	.510
18	resnet50-sup	.510
19	resnet50_tutorial	.510
20	AdvProp_efficientnet-b7	.510
21	resnext101_32x48d_wsl	.507
22	resnext101_32x16d_wsl	.506
23	resnet_SIN_IN_FT_IN	.504
24	vit_large_patch14_clip_336:laion2b_ft_in1k	.504
25	convnext_xxlarge_clip	.504
26	dinov2_vitl14_lc	.504
27	resnet50-SIN	.501
28	dinov2_vitg14_reg_lc	.502
29	voneresnet-50	.497
30	convnext_base:clip_laiona_augreg_ft_in1k_384	.494
31	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.493
32	dinov2_vitl14_lc_res336	.494
33	efficientnet_b2_imagenet_full	.492
34	AdvProp_efficientnet-b8	.493
35	focalnet_tiny_lrf_in1k	.489
36	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.489
37	grcnn	.488
38	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.485
39	shufflenet_v2_x1_0	.484
40	resnet50_robust_l2_eps1	.481
41	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.481
42	pnasnet_large_halfPrecision	.481
43	custom_model_cv_18_dagger_408	.480
44	vit_large_patch14_clip_224:laion2b_ft_in1k	.480
45	dinov2_vitl14_reg_lc_featread	.479
46	dinov2_vitl14_reg_lc	.479
47	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.480
48	convnext_xlarge:fb_in22k_ft_in1k	.478
49	AdvProp_efficientnet-b6	.478
50	efficientnet_b0	.477
51	deit_base_imagenet_full_seed-0	.477
52	resnet50-moclr8deg	.475
53	convnext_large:fb_in22k_ft_in1k	.473
54	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.474
55	cvt_cvt-13-224-in1k_4_LucyV4	.472
56	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.469
57	mobilenet_v2_1_4_224	.466
58	cvt_cvt-21-224-in1k_4_LucyV4	.462
59	convnext_tiny_sup	.456
60	vit_base_patch16_clip_224:openai_ft_in1k	.459
61	resnet50-VITO-8deg-cc	.455
62	resnet50-vitoimagevidnet8	.455
63	Res2Net50_26w_4s	.454
64	cvt_cvt-21-384-in1k_4_LucyV4	.455
65	resnext101_32x32d_wsl	.453
66	resnext101_32x8d_wsl	.452
67	AdvProp_efficientnet-b4	.451
68	AdvProp_efficientnet-b2	.448
69	AT_efficientnet-b2	.448
70	effnetb1_272x240	.448
71	convnext_base_imagenet_full_seed-0	.447
72	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.446
73	convnext_femto_ols:d1_in1k	.446
74	resnet50-SIN_IN	.445
75	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.444
76	resnet50_ewconplus_wzc	.443
77	persistence_memory	.443
78	convnext_small_imagenet_full_seed-0	.441
79	densenet-121	.434
80	ReAlnet03_cornet	.431
81	ReAlnet01_cornet	.430
82	ReAlnet04_cornet	.430
83	deit_small_imagenet_full_seed-0	.426
84	deit_large_imagenet_full_seed-0	.424
85	resnet_50_v2	.420
86	persistence_mem	.416
87	resnet50_ewconplus_wzc_seed2_2__323	.415
88	alexnet	.413
89	AlexNet_SIN	.412
90	resnet50_ewconplus_wzc_seed5_2__323	.413
91	resnet50_ewconplus_wzc_seed6_2_323	.412
92	resnet50_st_wzc_seed6_2_323	.406
93	resnet50_st_wzc_seed3_2_323	.404
94	compact_resnet50_robust_V4	.404
95	resnet50_st_wzc_seed7_2_323	.403
96	resnet152_imagenet_full	.395
97	AlexNet_SIN_fov12	.379
98	resnet-50-robust	.376
99	resnet18_fair	.371
100	convnext_large_imagenet_full_seed-0	.372
101	convnext_tiny_imagenet_full_seed-0	.367
102	eicircuit_resnet18_bn_20260919	.357
103	ViT_L_32_imagenet1k	.348
104	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.345
105	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.345
106	convnext_tiny:in12k_ft_in1k	.346
107	AlexNet_SIN_fov16	.333
108	resnet50_imagenet_full	.330
109	vonealexnet	.324
110	alexnet_training_seed_02	.296
111	alexnet_training_seed_01	.294
112	alexnet_training_seed_06	.282
113	alexnet_training_seed_07	.280
114	alexnet_training_seed_09	.279
115	alexnet_training_seed_10	.277
116	pr1_stl10_disentangled_alpha04	.258
117	VOneCORnet-S	.229
118	gabor_filter_entropy_4x4_v2_forcedCosine	.219
119	gabor_filter_entropy_4x4_v2	.217
120	voneresnet-50-non_stochastic	.210
121	ReAlnet01_cornet_fov4	.209
122	alexnet_training_seed_03	.189
123	predictive_coding_pc	.178
124	gabor_filter_mean_abs_4x4_v2	.154
125	convnext_base_fb_in1k	.122
126	hmax	.074
127	gabor_filter_entropy_4x4	.064
128	dinov2_vitg14_lc

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.V1

Metric: ridge