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