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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Allen2022_fmri_surface.V1-rdm")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	alexnet	.359
2	convnext_large:fb_in22k_ft_in1k	.356
3	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.347
4	AdvProp_efficientnet-b7	.346
5	AlexNet_SIN	.340
6	convnext_xlarge:fb_in22k_ft_in1k	.331
7	AT_efficientnet-b2	.321
8	AdvProp_efficientnet-b2	.321
9	AdvProp_efficientnet-b4	.320
10	convnext_femto_ols:d1_in1k	.319
11	vit_large_patch14_clip_336:laion2b_ft_in1k	.317
12	AdvProp_efficientnet-b8	.316
13	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.304
14	vit_large_patch14_clip_224:openai_ft_in1k	.302
15	vit_large_patch14_clip_224:laion2b_ft_in1k	.301
16	pnasnet_large_halfPrecision	.296
17	AdvProp_efficientnet-b6	.296
18	AlexNet_SIN_fov12	.295
19	AlexNet_SIN_fov16	.292
20	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.290
21	efficientnet_b0	.287
22	resnet_SIN_IN_FT_IN	.285
23	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.270
24	vit_base_patch16_clip_224:openai_ft_in1k	.264
25	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.261
26	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.259
27	resnet-50-robust	.256
28	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.250
29	grcnn	.237
30	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.236
31	convnext_tiny_sup	.234
32	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.230
33	resnet50-SIN	.227
34	custom_model_cv_18_dagger_408	.225
35	voneresnet-50	.224
36	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.223
37	compact_resnet50_robust_V4	.223
38	antialiased-r50	.221
39	cvt_cvt-13-384-in1k_4_LucyV4	.219
40	vit_relpos_base_patch16_clsgap_224:sw_in1k	.218
41	mobilenet_v2_1_4_224	.217
42	cvt_cvt-13-224-in1k_4_LucyV4	.217
43	alexnet_training_seed_02	.215
44	convnext_base:clip_laiona_augreg_ft_in1k_384	.214
45	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.214
46	ReAlnet03_cornet	.202
47	ReAlnet04_cornet	.199
48	ReAlnet01_cornet	.197
49	alexnet_training_seed_01	.194
50	alexnet_training_seed_06	.192
51	alexnet_training_seed_03	.188
52	convnext_tiny:in12k_ft_in1k	.182
53	resnet50-SIN_IN	.180
54	resnet50-SIN_IN_IN	.175
55	alexnet_training_seed_09	.172
56	cvt_cvt-21-224-in1k_4_LucyV4	.172
57	resnet50_ewconplus_wzc_2_323	.171
58	resnet_50_v1_spiking	.167
59	resnet_50_v1	.167
60	resnet50_tutorial	.167
61	resnet50-sup	.167
62	ReAlnet01_cornet_fov4	.166
63	alexnet_training_seed_07	.166
64	resnet50_ewconplus_wzc	.166
65	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.166
66	resnet50_st_wzc_2_323	.164
67	resnet50_ewconplus_wzc_seed2_2__323	.161
68	resnet50_ewconplus_wzc_seed5_2__323	.159
69	cvt_cvt-21-384-in1k_4_LucyV4	.157
70	pr1_stl10_disentangled_alpha04	.156
71	ViT_L_32_imagenet1k	.156
72	Res2Net50_26w_4s	.150
73	resnet50_ewconplus_wzc_seed3_2_323	.150
74	alexnet_training_seed_10	.149
75	resnet50_ewconplus_wzc_seed6_2_323	.148
76	resnet50_st_wzc_noseed_2_323	.147
77	focalnet_tiny_lrf_in1k	.145
78	resnet50_ewconplus_wzc_seed7_2_323	.142
79	resnet18_fair	.140
80	resnet50_st_wzc_seed7_2_323	.130
81	shufflenet_v2_x1_0	.130
82	resnet50_ewconplus_wzc_seed4_2_323	.130
83	resnet50_st_wzc_seed4_2_323	.129
84	resnet50_st_wzc_seed3_2_323	.128
85	resnet50-vitoimagevidnet8	.125
86	resnet50-VITO-8deg-cc	.125
87	densenet-121	.118
88	resnet50_st_wzc_seed5_2_323	.118
89	resnet50_robust_l2_eps1	.118
90	voneresnet-50-non_stochastic	.115
91	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.110
92	resnet50_st_wzc_seed6_2_323	.107
93	predictive_coding_pc	.105
94	VOneCORnet-S	.101
95	resnext101_32x16d_wsl	.100
96	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.092
97	resnext101_32x48d_wsl	.079
98	resnet50-moclr8deg	.069
99	resnet_50_v2	.057
100	resnext101_32x32d_wsl	.046
101	convnext_base_fb_in1k	.037
102	hmax	.032
103	resnext101_32x8d_wsl
104	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288
105	resnet50_imagenet_full
106	resnet152_imagenet_full
107	effnetb1_cutmix_augmix_sam_e1_5avg_424x377
108	effnetb1_272x240
109	efficientnet_b2_imagenet_full
110	deit_large_imagenet_full_seed-0
111	deit_small_imagenet_full_seed-0
112	deit_base_imagenet_full_seed-0
113	convnext_large_imagenet_full_seed-0
114	convnext_small_imagenet_full_seed-0
115	convnext_tiny_imagenet_full_seed-0
116	convnext_base_imagenet_full_seed-0

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

0.64.

Note that scores are relative to this ceiling.

Data: Allen2022_fmri_surface.V1

Metric: rdm