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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	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.414
4	resnet50_ewconplus_wzc_seed6_2_323	.388
5	alexnet	.381
6	resnet50_ewconplus_wzc_seed2_2__323	.380
7	convnext_femto_ols:d1_in1k	.380
8	resnet50_ewconplus_wzc_seed5_2__323	.379
9	VOneCORnet-S	.375
10	resnet50_ewconplus_wzc	.375
11	deit_large_imagenet_full_seed-0	.375
12	resnet50_st_wzc_seed7_2_323	.374
13	densenet-121	.372
14	resnet50-moclr8deg	.371
15	ReAlnet01_cornet_fov4	.371
16	resnet50_st_wzc_seed6_2_323	.370
17	resnet50_st_wzc_seed3_2_323	.370
18	convnext_base:clip_laiona_augreg_ft_in1k_384	.366
19	shufflenet_v2_x1_0	.366
20	voneresnet-50-non_stochastic	.362
21	voneresnet-50	.362
22	resnet50-SIN_IN	.356
23	ReAlnet04_cornet	.354
24	antialiased-r50	.352
25	ReAlnet03_cornet	.350
26	ReAlnet01_cornet	.350
27	resnet50_robust_l2_eps1	.347
28	vit_large_patch14_clip_224:openai_ft_in1k	.347
29	resnet50-SIN_IN_IN	.345
30	deit3_base_patch16_224_fb_in1k	.344
31	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.344
32	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.341
33	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.341
34	resnet_50_v1_spiking	.339
35	resnet_50_v1	.339
36	resnet50-sup	.339
37	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.338
38	mobilenet_v2_1_4_224	.336
39	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.335
40	AdvProp_efficientnet-b8	.335
41	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.333
42	resnet_SIN_IN_FT_IN	.332
43	AlexNet_SIN	.331
44	AdvProp_efficientnet-b7	.330
45	AT_efficientnet-b2	.329
46	AdvProp_efficientnet-b2	.329
47	efficientnet_b0	.328
48	resnet50-VITO-8deg-cc	.328
49	resnet50-vitoimagevidnet8	.328
50	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.327
51	deepeisnn_ei_imagenet_resnet18	.324
52	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.324
53	vit_base_patch16_clip_224:openai_ft_in1k	.322
54	convnext_xlarge:fb_in22k_ft_in1k	.317
55	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.316
56	predictive_coding_pc	.313
57	resnet50-SIN	.310
58	AdvProp_efficientnet-b4	.308
59	AdvProp_efficientnet-b6	.306
60	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.306
61	custom_model_cv_18_dagger_408	.304
62	vit_large_patch14_clip_224:laion2b_ft_in1k	.304
63	resnet_50_v2	.302
64	pr1_stl10_disentangled_alpha04	.301
65	pnasnet_large_halfPrecision	.293
66	Res2Net50_26w_4s	.287
67	convnext_base_fb_in1k_gram_aligned_dinov3	.286
68	vit_relpos_base_patch16_clsgap_224:sw_in1k	.280
69	dinov2_vitl14_lc	.278
70	vit_large_patch14_clip_336:laion2b_ft_in1k	.274
71	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.270
72	cvt_cvt-21-224-in1k_4_LucyV4	.268
73	eva02_large_in22k	.263
74	cvt_cvt-13-224-in1k_4_LucyV4	.261
75	dinov2_vitl14_lc_res336	.261
76	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.257
77	grcnn	.256
78	cvt_cvt-21-384-in1k_4_LucyV4	.252
79	convnext_tiny_sup	.244
80	resnet50_tutorial	.243
81	vit_huge_clip336_laion	.239
82	resnet-50-robust	.237
83	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.234
84	ViT_L_32_imagenet1k	.222
85	yudixie_wbh_object_class_scaling_0415_imagenet	.218
86	dinov2_vitg14_lc	.210
87	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.208
88	dinov2_vitl14_reg_lc_featread	.206
89	dinov2_vitl14_reg_lc	.206
90	vit_large_clip336_openai	.204
91	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.203
92	cvt_cvt-13-384-in1k_4_LucyV4	.194
93	convnext_large_clip_laion2b_finelayers	.193
94	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.187
95	resnext101_32x48d_wsl	.183
96	dinov2_vitg14_reg_lc	.173
97	convnext_xxlarge_clip	.168
98	focalnet_tiny_lrf_in1k	.164
99	convnext_base_fb_in22k_ft_in1k	.144
100	deepeisnn_ei_cifar10_resnet18	.141
101	deepeisnn_ei_cifar10_vgg19	.139
102	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.131
103	resnext101_32x8d_wsl	.130
104	convnext_large_in22k_supervised	.123
105	AlexNet_SIN_fov12	.117
106	resnet18_fair	.104
107	hmax	.100
108	AlexNet_SIN_fov16	.093
109	convnext_base_fb_in1k	.067
110	compact_resnet50_robust_V4	.054
111	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.050
112	deit3_base_patch16_224_fb_in22k_ft_in1k	.050
113	alexnet_training_seed_02	.044
114	alexnet_training_seed_09	.041
115	alexnet_training_seed_01	.036
116	alexnet_training_seed_06	.035
117	alexnet_training_seed_10	.033
118	alexnet_training_seed_03	.033
119	alexnet_training_seed_07	.031
120	yudixie_wbh_object_class_scaling_0415_run_2	.027
121	yudixie_wbh_object_class_scaling_0415_run_1	.018
122	yudixie_wbh_object_class_scaling_0415_run_0	.012
123	yudixie_wbh_object_class_scaling_0415_run_3	.011
124	resnext101_32x32d_wsl
125	resnext101_32x16d_wsl
126	effnetb1_272x240
127	resnet50_imagenet_full
128	resnet152_imagenet_full
129	efficientnet_b2_imagenet_full
130	deit_small_imagenet_full_seed-0
131	deit_base_imagenet_full_seed-0
132	convnext_large_imagenet_full_seed-0
133	convnext_tiny_imagenet_full_seed-0
134	convnext_small_imagenet_full_seed-0
135	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