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