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

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