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Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_tiny:in12k_ft_in1k	.428
2	convnext_large:fb_in22k_ft_in1k	.423
3	resnet50_ewconplus_wzc_seed3_2_323	.389
4	resnet50_ewconplus_wzc_seed6_2_323	.388
5	resnet50_ewconplus_wzc_2_323	.385
6	resnet50_ewconplus_wzc_seed4_2_323	.383
7	alexnet	.381
8	resnet50_ewconplus_wzc_seed7_2_323	.381
9	resnet50_ewconplus_wzc_seed2_2__323	.380
10	convnext_femto_ols:d1_in1k	.380
11	resnet50_ewconplus_wzc_seed5_2__323	.379
12	VOneCORnet-S	.375
13	resnet50_ewconplus_wzc	.375
14	resnet50_st_wzc_seed7_2_323	.374
15	resnet50_st_wzc_noseed_2_323	.374
16	resnet50_st_wzc_2_323	.373
17	resnet50_st_wzc_seed4_2_323	.372
18	densenet-121	.372
19	resnet50-moclr8deg	.371
20	ReAlnet01_cornet_fov4	.371
21	resnet50_st_wzc_seed6_2_323	.370
22	resnet50_st_wzc_seed3_2_323	.370
23	resnet50_st_wzc_seed5_2_323	.367
24	convnext_base:clip_laiona_augreg_ft_in1k_384	.366
25	shufflenet_v2_x1_0	.366
26	voneresnet-50-non_stochastic	.362
27	voneresnet-50	.362
28	resnet50-SIN_IN	.356
29	ReAlnet04_cornet	.354
30	antialiased-r50	.352
31	ReAlnet03_cornet	.350
32	ReAlnet01_cornet	.350
33	resnet50_robust_l2_eps1	.347
34	vit_large_patch14_clip_224:openai_ft_in1k	.347
35	resnet50-SIN_IN_IN	.345
36	deit3_base_patch16_224_fb_in1k	.344
37	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.344
38	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.341
39	resnet_50_v1_spiking	.339
40	resnet_50_v1	.339
41	resnet50-sup	.339
42	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.338
43	mobilenet_v2_1_4_224	.336
44	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.335
45	AdvProp_efficientnet-b8	.335
46	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.333
47	resnet_SIN_IN_FT_IN	.332
48	AlexNet_SIN	.331
49	AdvProp_efficientnet-b7	.330
50	AT_efficientnet-b2	.329
51	AdvProp_efficientnet-b2	.329
52	efficientnet_b0	.328
53	resnet50-VITO-8deg-cc	.328
54	resnet50-vitoimagevidnet8	.328
55	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.327
56	deepeisnn_ei_imagenet_resnet18	.324
57	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.324
58	vit_base_patch16_clip_224:openai_ft_in1k	.322
59	convnext_xlarge:fb_in22k_ft_in1k	.317
60	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.316
61	predictive_coding_pc	.313
62	resnet50-SIN	.310
63	AdvProp_efficientnet-b4	.308
64	AdvProp_efficientnet-b6	.306
65	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.306
66	custom_model_cv_18_dagger_408	.304
67	vit_large_patch14_clip_224:laion2b_ft_in1k	.304
68	resnet_50_v2	.302
69	pr1_stl10_disentangled_alpha04	.301
70	pnasnet_large_halfPrecision	.293
71	Res2Net50_26w_4s	.287
72	convnext_base_fb_in1k_gram_aligned_dinov3	.286
73	vit_relpos_base_patch16_clsgap_224:sw_in1k	.280
74	vit_large_patch14_clip_336:laion2b_ft_in1k	.274
75	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.270
76	cvt_cvt-21-224-in1k_4_LucyV4	.268
77	cvt_cvt-13-224-in1k_4_LucyV4	.261
78	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.257
79	grcnn	.256
80	cvt_cvt-21-384-in1k_4_LucyV4	.252
81	convnext_tiny_sup	.244
82	resnet50_tutorial	.243
83	resnet-50-robust	.237
84	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.234
85	ViT_L_32_imagenet1k	.222
86	yudixie_wbh_object_class_scaling_0415_imagenet	.218
87	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.208
88	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.203
89	cvt_cvt-13-384-in1k_4_LucyV4	.194
90	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.187
91	resnext101_32x48d_wsl	.183
92	focalnet_tiny_lrf_in1k	.164
93	convnext_base_fb_in22k_ft_in1k	.144
94	deepeisnn_ei_cifar10_resnet18	.141
95	deepeisnn_ei_cifar10_vgg19	.139
96	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.131
97	resnext101_32x8d_wsl	.130
98	AlexNet_SIN_fov12	.117
99	resnet18_fair	.104
100	hmax	.100
101	AlexNet_SIN_fov16	.093
102	convnext_base_fb_in1k	.067
103	compact_resnet50_robust_V4	.054
104	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.050
105	deit3_base_patch16_224_fb_in22k_ft_in1k	.050
106	alexnet_training_seed_02	.044
107	alexnet_training_seed_09	.041
108	alexnet_training_seed_01	.036
109	alexnet_training_seed_06	.035
110	alexnet_training_seed_10	.033
111	alexnet_training_seed_03	.033
112	alexnet_training_seed_07	.031
113	yudixie_wbh_object_class_scaling_0415_run_2	.027
114	yudixie_wbh_object_class_scaling_0415_run_1	.018
115	yudixie_wbh_object_class_scaling_0415_run_0	.012
116	yudixie_wbh_object_class_scaling_0415_run_3	.011
117	resnext101_32x32d_wsl
118	resnext101_32x16d_wsl
119	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288
120	effnetb1_cutmix_augmix_sam_e1_5avg_424x377
121	effnetb1_272x240
122	resnet50_imagenet_full
123	resnet152_imagenet_full
124	efficientnet_b2_imagenet_full
125	deit_small_imagenet_full_seed-0
126	deit_large_imagenet_full_seed-0
127	deit_base_imagenet_full_seed-0
128	convnext_large_imagenet_full_seed-0
129	convnext_tiny_imagenet_full_seed-0
130	convnext_small_imagenet_full_seed-0
131	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

0.85.

Note that scores are relative to this ceiling.

Data: Allen2022_fmri_surface.IT

Metric: rdm