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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	eva02_large_in22k	.576
7	focalnet_tiny_lrf_in1k	.562
8	resnet152_imagenet_full	.562
9	vit_relpos_base_patch16_clsgap_224:sw_in1k	.556
10	vit_large_patch14_clip_336:laion2b_ft_in1k	.557
11	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.553
12	dinov2_vitg14_reg_lc	.548
13	resnet50_st_wzc_seed7_2_323	.547
14	resnet50_imagenet_full	.546
15	Res2Net50_26w_4s	.547
16	deit_large_imagenet_full_seed-0	.545
17	dinov2_vitl14_lc	.543
18	resnet50_tutorial	.541
19	resnet50_st_wzc_seed3_2_323	.540
20	dinov2_vitl14_lc_res336	.539
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	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.520
27	antialiased-r50	.521
28	resnet50_ewconplus_wzc_seed5_2__323	.520
29	resnet50-sup	.519
30	resnet_50_v1	.519
31	resnet_50_v1_spiking	.519
32	convnext_xxlarge_clip	.520
33	vit_base_patch16_clip_224:openai_ft_in1k	.518
34	resnet50-SIN_IN	.518
35	voneresnet-50-non_stochastic	.516
36	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.516
37	resnet50_ewconplus_wzc	.515
38	resnet50_ewconplus_wzc_seed2_2__323	.511
39	vit_large_patch14_clip_224:openai_ft_in1k	.507
40	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.503
41	convnext_large_imagenet_full_seed-0	.498
42	convnext_base:clip_laiona_augreg_ft_in1k_384	.491
43	vit_large_patch14_clip_224:laion2b_ft_in1k	.490
44	resnet50-vitoimagevidnet8	.487
45	resnet50-VITO-8deg-cc	.487
46	convnext_base_imagenet_full_seed-0	.485
47	custom_model_cv_18_dagger_408	.484
48	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.482
49	convnext_tiny_sup	.482
50	voneresnet-50	.483
51	convnext_tiny:in12k_ft_in1k	.481
52	dinov2_vitl14_reg_lc	.478
53	ReAlnet01_cornet	.476
54	ReAlnet03_cornet	.476
55	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.475
56	convnext_large:fb_in22k_ft_in1k	.474
57	ReAlnet04_cornet	.471
58	pnasnet_large_halfPrecision	.470
59	resnet50_robust_l2_eps1	.468
60	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.465
61	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.462
62	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.460
63	vit_huge_clip336_laion	.456
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	dinov2_vitg14_lc	.404
87	AdvProp_efficientnet-b6	.403
88	cvt_cvt-21-384-in1k_4_LucyV4	.401
89	vit_large_clip336_openai	.398
90	ViT_L_32_imagenet1k	.396
91	AT_efficientnet-b2	.388
92	AdvProp_efficientnet-b2	.388
93	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.384
94	AdvProp_efficientnet-b4	.378
95	AdvProp_efficientnet-b8	.370
96	resnet-50-robust	.370
97	cvt_cvt-21-224-in1k_4_LucyV4	.363
98	grcnn	.328
99	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.307
100	alexnet	.305
101	convnext_base_fb_in1k	.284
102	AlexNet_SIN_fov12	.248
103	AlexNet_SIN	.238
104	resnet18_fair	.223
105	predictive_coding_pc	.203
106	AlexNet_SIN_fov16	.192
107	hmax	.109
108	alexnet_training_seed_07	.091
109	alexnet_training_seed_10	.081
110	alexnet_training_seed_01	.076
111	alexnet_training_seed_03	.075
112	alexnet_training_seed_02	.063
113	compact_resnet50_robust_V4	.058
114	alexnet_training_seed_09	.057
115	alexnet_training_seed_06	.045
116	dinov2_vitl14_reg_lc_featread
117	resnet50_st_wzc_seed6_2_323
118	resnet50_ewconplus_wzc_seed6_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