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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	vit_huge_clip336_laion	.333
7	convnext_xlarge:fb_in22k_ft_in1k	.331
8	gabor_filter_entropy_4x4_v2	.327
9	AT_efficientnet-b2	.321
10	AdvProp_efficientnet-b2	.321
11	AdvProp_efficientnet-b4	.320
12	gabor_filter_entropy_4x4	.320
13	convnext_femto_ols:d1_in1k	.319
14	vit_large_patch14_clip_336:laion2b_ft_in1k	.317
15	AdvProp_efficientnet-b8	.316
16	vit_large_clip336_openai	.306
17	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.304
18	vit_large_patch14_clip_224:openai_ft_in1k	.302
19	vit_large_patch14_clip_224:laion2b_ft_in1k	.301
20	pnasnet_large_halfPrecision	.296
21	AdvProp_efficientnet-b6	.296
22	AlexNet_SIN_fov12	.295
23	AlexNet_SIN_fov16	.292
24	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.290
25	efficientnet_b0	.287
26	resnet_SIN_IN_FT_IN	.285
27	eva02_large_in22k	.270
28	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.270
29	vit_base_patch16_clip_224:openai_ft_in1k	.264
30	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.261
31	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.259
32	resnet-50-robust	.256
33	dinov2_vitl14_lc	.252
34	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.250
35	deit_large_imagenet_full_seed-0	.246
36	grcnn	.237
37	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.236
38	convnext_tiny_sup	.234
39	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.230
40	resnet50-SIN	.227
41	custom_model_cv_18_dagger_408	.225
42	voneresnet-50	.224
43	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.223
44	dinov2_vitl14_lc_res336	.223
45	compact_resnet50_robust_V4	.223
46	antialiased-r50	.221
47	cvt_cvt-13-384-in1k_4_LucyV4	.219
48	vit_relpos_base_patch16_clsgap_224:sw_in1k	.218
49	mobilenet_v2_1_4_224	.217
50	cvt_cvt-13-224-in1k_4_LucyV4	.217
51	alexnet_training_seed_02	.215
52	convnext_base:clip_laiona_augreg_ft_in1k_384	.214
53	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.214
54	ReAlnet03_cornet	.202
55	ReAlnet04_cornet	.199
56	ReAlnet01_cornet	.197
57	alexnet_training_seed_01	.194
58	alexnet_training_seed_06	.192
59	gabor_filter_entropy_4x4_v2_forcedCosine	.191
60	alexnet_training_seed_03	.188
61	convnext_tiny:in12k_ft_in1k	.182
62	resnet50-SIN_IN	.180
63	resnet50-SIN_IN_IN	.175
64	alexnet_training_seed_09	.172
65	cvt_cvt-21-224-in1k_4_LucyV4	.172
66	seresnext50_32x4d	.171
67	resnet_50_v1_spiking	.167
68	resnet_50_v1	.167
69	resnet50_tutorial	.167
70	resnet50-sup	.167
71	ReAlnet01_cornet_fov4	.166
72	alexnet_training_seed_07	.166
73	resnet50_ewconplus_wzc	.166
74	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.166
75	resnet50_ewconplus_wzc_seed2_2__323	.161
76	resnet50_ewconplus_wzc_seed5_2__323	.159
77	eicircuit_resnet18_bn_20260919	.158
78	cvt_cvt-21-384-in1k_4_LucyV4	.157
79	pr1_stl10_disentangled_alpha04	.156
80	ViT_L_32_imagenet1k	.156
81	convnext_large_clip_laion2b_finelayers	.154
82	Res2Net50_26w_4s	.150
83	alexnet_training_seed_10	.149
84	resnet50_ewconplus_wzc_seed6_2_323	.148
85	dinov2_vitg14_lc	.147
86	focalnet_tiny_lrf_in1k	.145
87	resnet18_fair	.140
88	gabor_filter_mean_abs_4x4_v2	.135
89	resnet50_st_wzc_seed7_2_323	.130
90	shufflenet_v2_x1_0	.130
91	resnet50_st_wzc_seed3_2_323	.128
92	convnext_large_in22k_supervised	.128
93	resnet50-vitoimagevidnet8	.125
94	resnet50-VITO-8deg-cc	.125
95	convnext_xxlarge_clip	.125
96	densenet-121	.118
97	resnet50_robust_l2_eps1	.118
98	voneresnet-50-non_stochastic	.115
99	regnety_032	.114
100	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.110
101	resnet50_st_wzc_seed6_2_323	.107
102	predictive_coding_pc	.105
103	VOneCORnet-S	.101
104	resnext101_32x16d_wsl	.100
105	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.092
106	resnext101_32x48d_wsl	.079
107	resnet50-moclr8deg	.069
108	resnet_50_v2	.057
109	dinov2_vitl14_reg_lc_featread	.050
110	dinov2_vitl14_reg_lc	.050
111	resnext101_32x32d_wsl	.046
112	dinov2_vitg14_reg_lc	.042
113	convnext_base_fb_in1k	.037
114	hmax	.032
115	resnext101_32x8d_wsl
116	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288
117	resnet50_imagenet_full
118	resnet152_imagenet_full
119	effnetb1_cutmix_augmix_sam_e1_5avg_424x377
120	effnetb1_272x240
121	efficientnet_b2_imagenet_full
122	deit_small_imagenet_full_seed-0
123	deit_base_imagenet_full_seed-0
124	convnext_large_imagenet_full_seed-0
125	convnext_small_imagenet_full_seed-0
126	convnext_tiny_imagenet_full_seed-0
127	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