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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Allen2022_fmri_surface.IT-ridge")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnext101_32x32d_wsl	.592
2	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.592
3	resnext101_32x48d_wsl	.589
4	resnext101_32x16d_wsl	.587
5	resnet_50_v2	.585
6	focalnet_tiny_lrf_in1k	.562
7	resnet152_imagenet_full	.562
8	vit_relpos_base_patch16_clsgap_224:sw_in1k	.556
9	vit_large_patch14_clip_336:laion2b_ft_in1k	.557
10	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.553
11	resnet50_st_wzc_seed7_2_323	.547
12	resnet50_imagenet_full	.546
13	Res2Net50_26w_4s	.547
14	resnet50_st_wzc_2_323	.545
15	deit_large_imagenet_full_seed-0	.545
16	resnet50_st_wzc_noseed_2_323	.542
17	resnet50_tutorial	.541
18	resnet50_st_wzc_seed3_2_323	.540
19	resnet50_st_wzc_seed5_2_323	.540
20	resnet50_st_wzc_seed4_2_323	.540
21	deit_base_imagenet_full_seed-0	.538
22	deit_small_imagenet_full_seed-0	.532
23	resnet50-SIN_IN_IN	.528
24	resnet50-moclr8deg	.528
25	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.523
26	resnet50_ewconplus_wzc_2_323	.523
27	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.520
28	antialiased-r50	.521
29	resnet50_ewconplus_wzc_seed5_2__323	.520
30	resnet50-sup	.519
31	resnet_50_v1	.519
32	resnet_50_v1_spiking	.519
33	resnet50_ewconplus_wzc_seed3_2_323	.519
34	resnet50_ewconplus_wzc_seed7_2_323	.519
35	vit_base_patch16_clip_224:openai_ft_in1k	.518
36	resnet50-SIN_IN	.518
37	voneresnet-50-non_stochastic	.516
38	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.516
39	resnet50_ewconplus_wzc	.515
40	resnet50_ewconplus_wzc_seed2_2__323	.511
41	vit_large_patch14_clip_224:openai_ft_in1k	.507
42	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.503
43	convnext_large_imagenet_full_seed-0	.498
44	convnext_base:clip_laiona_augreg_ft_in1k_384	.491
45	vit_large_patch14_clip_224:laion2b_ft_in1k	.490
46	resnet50-vitoimagevidnet8	.487
47	resnet50-VITO-8deg-cc	.487
48	convnext_base_imagenet_full_seed-0	.485
49	custom_model_cv_18_dagger_408	.484
50	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.482
51	convnext_tiny_sup	.482
52	voneresnet-50	.483
53	convnext_tiny:in12k_ft_in1k	.481
54	ReAlnet01_cornet	.476
55	ReAlnet03_cornet	.476
56	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.475
57	convnext_large:fb_in22k_ft_in1k	.474
58	ReAlnet04_cornet	.471
59	pnasnet_large_halfPrecision	.470
60	resnet50_robust_l2_eps1	.468
61	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.465
62	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.462
63	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.460
64	convnext_small_imagenet_full_seed-0	.455
65	densenet-121	.454
66	VOneCORnet-S	.451
67	resnet50-SIN	.449
68	efficientnet_b2_imagenet_full	.447
69	effnetb1_272x240	.444
70	shufflenet_v2_x1_0	.444
71	resnext101_32x8d_wsl	.438
72	efficientnet_b0	.438
73	resnet_SIN_IN_FT_IN	.429
74	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.426
75	ReAlnet01_cornet_fov4	.426
76	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.422
77	convnext_tiny_imagenet_full_seed-0	.422
78	cvt_cvt-13-224-in1k_4_LucyV4	.422
79	cvt_cvt-13-384-in1k_4_LucyV4	.421
80	mobilenet_v2_1_4_224	.415
81	AdvProp_efficientnet-b7	.413
82	pr1_stl10_disentangled_alpha04	.413
83	convnext_xlarge:fb_in22k_ft_in1k	.409
84	convnext_femto_ols:d1_in1k	.407
85	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.407
86	AdvProp_efficientnet-b6	.403
87	cvt_cvt-21-384-in1k_4_LucyV4	.401
88	ViT_L_32_imagenet1k	.396
89	AT_efficientnet-b2	.388
90	AdvProp_efficientnet-b2	.388
91	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.384
92	AdvProp_efficientnet-b4	.378
93	AdvProp_efficientnet-b8	.370
94	resnet-50-robust	.370
95	cvt_cvt-21-224-in1k_4_LucyV4	.363
96	grcnn	.328
97	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.307
98	alexnet	.305
99	convnext_base_fb_in1k	.284
100	AlexNet_SIN_fov12	.248
101	AlexNet_SIN	.238
102	resnet18_fair	.223
103	predictive_coding_pc	.203
104	AlexNet_SIN_fov16	.192
105	hmax	.109
106	alexnet_training_seed_07	.091
107	alexnet_training_seed_10	.081
108	alexnet_training_seed_01	.076
109	alexnet_training_seed_03	.075
110	alexnet_training_seed_02	.063
111	compact_resnet50_robust_V4	.058
112	alexnet_training_seed_09	.057
113	alexnet_training_seed_06	.045
114	resnet50_st_wzc_seed6_2_323
115	resnet50_ewconplus_wzc_seed6_2_323
116	resnet50_ewconplus_wzc_seed4_2_323

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

0.46.

Note that scores are relative to this ceiling.

Data: Allen2022_fmri_surface.IT

Metric: ridge