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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	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.542
2	vit_large_patch14_clip_224:openai_ft_in1k	.541
3	cvt_cvt-13-384-in1k_4_LucyV4	.540
4	vit_relpos_base_patch16_clsgap_224:sw_in1k	.536
5	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.531
6	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.528
7	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.521
8	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.516
9	resnet50-SIN_IN_IN	.515
10	antialiased-r50	.513
11	resnet_50_v1_spiking	.510
12	resnet_50_v1	.510
13	resnet50-sup	.510
14	resnet50_tutorial	.510
15	AdvProp_efficientnet-b7	.510
16	resnext101_32x48d_wsl	.507
17	resnext101_32x16d_wsl	.506
18	resnet_SIN_IN_FT_IN	.504
19	vit_large_patch14_clip_336:laion2b_ft_in1k	.504
20	resnet50-SIN	.501
21	voneresnet-50	.497
22	convnext_base:clip_laiona_augreg_ft_in1k_384	.494
23	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.493
24	efficientnet_b2_imagenet_full	.492
25	AdvProp_efficientnet-b8	.493
26	focalnet_tiny_lrf_in1k	.489
27	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.489
28	grcnn	.488
29	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.485
30	shufflenet_v2_x1_0	.484
31	resnet50_robust_l2_eps1	.481
32	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.481
33	pnasnet_large_halfPrecision	.481
34	custom_model_cv_18_dagger_408	.480
35	vit_large_patch14_clip_224:laion2b_ft_in1k	.480
36	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.480
37	convnext_xlarge:fb_in22k_ft_in1k	.478
38	AdvProp_efficientnet-b6	.478
39	efficientnet_b0	.477
40	deit_base_imagenet_full_seed-0	.477
41	resnet50-moclr8deg	.475
42	convnext_large:fb_in22k_ft_in1k	.473
43	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.474
44	cvt_cvt-13-224-in1k_4_LucyV4	.472
45	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.469
46	mobilenet_v2_1_4_224	.466
47	cvt_cvt-21-224-in1k_4_LucyV4	.462
48	convnext_tiny_sup	.456
49	vit_base_patch16_clip_224:openai_ft_in1k	.459
50	resnet50-VITO-8deg-cc	.455
51	resnet50-vitoimagevidnet8	.455
52	Res2Net50_26w_4s	.454
53	cvt_cvt-21-384-in1k_4_LucyV4	.455
54	resnext101_32x32d_wsl	.453
55	resnext101_32x8d_wsl	.452
56	AdvProp_efficientnet-b4	.451
57	AdvProp_efficientnet-b2	.448
58	AT_efficientnet-b2	.448
59	effnetb1_272x240	.448
60	convnext_base_imagenet_full_seed-0	.447
61	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.446
62	convnext_femto_ols:d1_in1k	.446
63	resnet50-SIN_IN	.445
64	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.444
65	resnet50_ewconplus_wzc	.443
66	convnext_small_imagenet_full_seed-0	.441
67	densenet-121	.434
68	ReAlnet03_cornet	.431
69	ReAlnet01_cornet	.430
70	ReAlnet04_cornet	.430
71	deit_small_imagenet_full_seed-0	.426
72	deit_large_imagenet_full_seed-0	.424
73	resnet_50_v2	.420
74	resnet50_st_wzc_2_323	.417
75	resnet50_ewconplus_wzc_seed2_2__323	.415
76	alexnet	.413
77	AlexNet_SIN	.412
78	resnet50_ewconplus_wzc_2_323	.413
79	resnet50_ewconplus_wzc_seed5_2__323	.413
80	resnet50_ewconplus_wzc_seed7_2_323	.412
81	resnet50_ewconplus_wzc_seed6_2_323	.412
82	resnet50_ewconplus_wzc_seed4_2_323	.410
83	resnet50_st_wzc_seed6_2_323	.406
84	resnet50_st_wzc_noseed_2_323	.406
85	resnet50_ewconplus_wzc_seed3_2_323	.406
86	resnet50_st_wzc_seed3_2_323	.404
87	compact_resnet50_robust_V4	.404
88	resnet50_st_wzc_seed7_2_323	.403
89	resnet50_st_wzc_seed4_2_323	.397
90	resnet50_st_wzc_seed5_2_323	.396
91	resnet152_imagenet_full	.395
92	AlexNet_SIN_fov12	.379
93	resnet-50-robust	.376
94	resnet18_fair	.371
95	convnext_large_imagenet_full_seed-0	.372
96	convnext_tiny_imagenet_full_seed-0	.367
97	ViT_L_32_imagenet1k	.348
98	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.345
99	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.345
100	convnext_tiny:in12k_ft_in1k	.346
101	AlexNet_SIN_fov16	.333
102	resnet50_imagenet_full	.330
103	alexnet_training_seed_02	.296
104	alexnet_training_seed_01	.294
105	alexnet_training_seed_06	.282
106	alexnet_training_seed_07	.280
107	alexnet_training_seed_09	.279
108	alexnet_training_seed_10	.277
109	pr1_stl10_disentangled_alpha04	.258
110	VOneCORnet-S	.229
111	voneresnet-50-non_stochastic	.210
112	ReAlnet01_cornet_fov4	.209
113	alexnet_training_seed_03	.189
114	predictive_coding_pc	.178
115	convnext_base_fb_in1k	.122
116	hmax	.074

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