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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_ecoset_full	.391
5	resnet50_ewconplus_wzc_seed6_2_323	.388
6	alexnet	.381
7	resnet50_ewconplus_wzc_seed2_2__323	.380
8	convnext_femto_ols:d1_in1k	.380
9	resnet50_ewconplus_wzc_seed5_2__323	.379
10	VOneCORnet-S	.375
11	resnet50_ewconplus_wzc	.375
12	resnet50_simclr_linhead_01norm	.375
13	deit_large_imagenet_full_seed-0	.375
14	resnet50_st_wzc_seed7_2_323	.374
15	densenet-121	.372
16	resnet50_imagenet_full	.372
17	resnet50-moclr8deg	.371
18	ReAlnet01_cornet_fov4	.371
19	resnet50_st_wzc_seed6_2_323	.370
20	resnet50_st_wzc_seed3_2_323	.370
21	convnext_base:clip_laiona_augreg_ft_in1k_384	.366
22	shufflenet_v2_x1_0	.366
23	resnet18_ecoset_full	.366
24	mehrer2020_alexnet_training_seed_01_fov4	.365
25	voneresnet-50-non_stochastic	.362
26	voneresnet-50	.362
27	ReAlnet02_cornet	.358
28	convnext_base_imagenet_full_seed-0	.357
29	resnet50-SIN_IN	.356
30	ReAlnet04_cornet	.354
31	antialiased-r50	.352
32	resnet101_ecoset_full	.352
33	ReAlnet10_cornet	.351
34	ReAlnet06_cornet	.351
35	ReAlnet08_cornet	.351
36	ReAlnet03_cornet	.350
37	ReAlnet01_cornet	.350
38	ReAlnet05_cornet	.349
39	ReAlnet09_cornet	.349
40	resnet50_robust_l2_eps1	.347
41	vit_large_patch14_clip_224:openai_ft_in1k	.347
42	resnet50-SIN_IN_IN	.345
43	ReAlnet07_cornet	.344
44	deit3_base_patch16_224_fb_in1k	.344
45	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.344
46	resnet34_ecoset_full	.344
47	deit_base_imagenet_full_seed-0	.343
48	convnext_small_imagenet_full_seed-0	.343
49	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.341
50	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.341
51	resnet152_ecoset_full	.340
52	eicircuit_resnet18_bn_20260919	.339
53	resnet_50_v1_spiking	.339
54	resnet_50_v1	.339
55	resnet50-sup	.339
56	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.338
57	mobilenet_v2_1_4_224	.336
58	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.335
59	AdvProp_efficientnet-b8	.335
60	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.333
61	resnet_SIN_IN_FT_IN	.332
62	resnet101_imagenet_full	.331
63	AlexNet_SIN	.331
64	resnet18_imagenet_full	.330
65	AdvProp_efficientnet-b7	.330
66	AT_efficientnet-b2	.329
67	AdvProp_efficientnet-b2	.329
68	efficientnet_b0	.328
69	resnet50-VITO-8deg-cc	.328
70	resnet50-vitoimagevidnet8	.328
71	deit_small_imagenet_full_seed-0	.327
72	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.327
73	convnext_large_imagenet_full_seed-0	.325
74	deepeisnn_ei_imagenet_resnet18	.324
75	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.324
76	vit_base_patch16_clip_224:openai_ft_in1k	.322
77	convnext_tiny_imagenet_full_seed-0	.317
78	resnet50_simsiam_linhead	.317
79	convnext_xlarge:fb_in22k_ft_in1k	.317
80	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.316
81	predictive_coding_pc	.313
82	resnet50-SIN	.310
83	ReAlnet01_cornet_fov12	.308
84	AdvProp_efficientnet-b4	.308
85	AdvProp_efficientnet-b6	.306
86	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.306
87	custom_model_cv_18_dagger_408	.304
88	vit_large_patch14_clip_224:laion2b_ft_in1k	.304
89	mehrer2020_alexnet_training_seed_06	.304
90	mehrer2020_alexnet_training_seed_05	.303
91	mehrer2020_alexnet_training_seed_03	.302
92	AlexNet_SIN_fov4	.302
93	mehrer2020_alexnet_training_seed_02	.302
94	resnet_50_v2	.302
95	pr1_stl10_disentangled_alpha04	.301
96	mehrer2020_alexnet_training_seed_07	.300
97	resnet50_imagenet_100_seed-0	.300
98	efficientnet_b1_imagenet_full	.299
99	resnet34_imagenet_full	.299
100	resnet152_imagenet_full	.299
101	pnasnet_large_halfPrecision	.293
102	mehrer2020_alexnet_training_seed_08	.293
103	ReAlnet01_cornet_fov16	.292
104	mehrer2020_alexnet_training_seed_09	.291
105	mehrer2020_alexnet_training_seed_10	.290
106	efficientnet_b0_imagenet_full	.290
107	Res2Net50_26w_4s	.287
108	convnext_base_fb_in1k_gram_aligned_dinov3	.286
109	vit_relpos_base_patch16_clsgap_224:sw_in1k	.280
110	dinov2_vitl14_lc	.278
111	resnet50_byol_linhead	.277
112	vit_large_patch14_clip_336:laion2b_ft_in1k	.274
113	seresnext50_32x4d	.273
114	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.270
115	cvt_cvt-21-224-in1k_4_LucyV4	.268
116	mehrer2020_alexnet_training_seed_04	.266
117	eva02_large_in22k	.263
118	cvt_cvt-13-224-in1k_4_LucyV4	.261
119	dinov2_vitl14_lc_res336	.261
120	mehrer2020_alexnet_training_seed_01	.259
121	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.257
122	grcnn	.256
123	cvt_cvt-21-384-in1k_4_LucyV4	.252
124	resnet50_vicreg_linhead	.251
125	convnext_tiny_sup	.244
126	resnet50_tutorial	.243
127	vit_huge_clip336_laion	.239
128	resnet50_barlowtwins_linhead	.239
129	resnet-50-robust	.237
130	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.234
131	ViT_L_32_imagenet1k	.222
132	ijepa_vith14_linhead	.218
133	yudixie_wbh_object_class_scaling_0415_imagenet	.218
134	dinov2_vitg14_lc	.210
135	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.208
136	efficientnet_b2_imagenet_full	.207
137	dinov2_vitl14_reg_lc_featread	.206
138	dinov2_vitl14_reg_lc	.206
139	convnext_small_imagenet_100_seed-0	.204
140	vit_large_clip336_openai	.204
141	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.203
142	cvt_cvt-13-384-in1k_4_LucyV4	.194
143	convnext_large_clip_laion2b_finelayers	.193
144	mehrer2020_alexnet_training_seed_01_fov12	.189
145	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.187
146	resnext101_32x16d_wsl	.186
147	resnext101_32x48d_wsl	.183
148	dinov2_vitg14_reg_lc	.173
149	convnext_xxlarge_clip	.168
150	regnety_032	.167
151	focalnet_tiny_lrf_in1k	.164
152	convnext_base_fb_in22k_ft_in1k	.144
153	deepeisnn_ei_cifar10_resnet18	.141
154	deit_small_imagenet_100_seed-0	.140
155	deepeisnn_ei_cifar10_vgg19	.139
156	resnext101_32x32d_wsl	.133
157	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.131
158	resnext101_32x8d_wsl	.130
159	mehrer2020_alexnet_training_seed_01_fov16	.128
160	convnext_large_in22k_supervised	.123
161	AlexNet_SIN_fov12	.117
162	resnet18_fair	.104
163	hmax	.100
164	AlexNet_SIN_fov16	.093
165	convnext_base_fb_in1k	.067
166	gabor_filter_entropy_4x4	.060
167	gabor_filter_entropy_4x4_v2	.054
168	compact_resnet50_robust_V4	.054
169	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.050
170	deit3_base_patch16_224_fb_in22k_ft_in1k	.050
171	gabor_filter_mean_abs_4x4_v2	.044
172	alexnet_training_seed_02	.044
173	alexnet_training_seed_09	.041
174	alexnet_training_seed_01	.036
175	alexnet_training_seed_06	.035
176	alexnet_training_seed_10	.033
177	alexnet_training_seed_03	.033
178	alexnet_training_seed_07	.031
179	gabor_filter_entropy_4x4_v2_forcedCosine	.028
180	yudixie_wbh_object_class_scaling_0415_run_2	.027
181	yudixie_wbh_object_class_scaling_0415_run_1	.018
182	yudixie_wbh_object_class_scaling_0415_run_0	.012
183	yudixie_wbh_object_class_scaling_0415_run_3	.011
184	effnetb1_272x240

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