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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Allen2022_fmri_surface.V4-rdm")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.301
2	convnext_xlarge:fb_in22k_ft_in1k	.298
3	AT_efficientnet-b2	.287
4	AdvProp_efficientnet-b2	.287
5	AlexNet_SIN	.284
6	resnet50_st_wzc_seed7_2_323	.272
7	vit_large_patch14_clip_336:laion2b_ft_in1k	.271
8	vit_huge_clip336_laion	.269
9	AdvProp_efficientnet-b8	.267
10	alexnet	.264
11	custom_model_cv_18_dagger_408	.263
12	resnet50_st_wzc_seed3_2_323	.262
13	AdvProp_efficientnet-b4	.260
14	pnasnet_large_halfPrecision	.260
15	cvt_cvt-13-224-in1k_4_LucyV4	.259
16	cvt_cvt-21-224-in1k_4_LucyV4	.257
17	convnext_large:fb_in22k_ft_in1k	.256
18	efficientnet_b0	.256
19	ReAlnet01_cornet	.250
20	vit_relpos_base_patch16_clsgap_224:sw_in1k	.249
21	ReAlnet04_cornet	.247
22	persistence_memory	.243
23	ReAlnet03_cornet	.243
24	ReAlnet01_cornet_fov4	.234
25	vit_large_clip336_openai	.233
26	resnet50_ewconplus_wzc_seed5_2__323	.232
27	resnet50_ewconplus_wzc_seed6_2_323	.230
28	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.230
29	voneresnet-50	.225
30	vit_large_patch14_clip_224:openai_ft_in1k	.225
31	deepeisnn_ei_imagenet_resnet18	.224
32	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.222
33	resnet50-SIN	.220
34	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.220
35	deit3_base_patch16_224_fb_in22k_ft_in1k	.220
36	resnet50_ewconplus_wzc	.220
37	AdvProp_efficientnet-b7	.219
38	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.218
39	dinov2_vitl14_lc_res336	.217
40	vit_base_patch16_clip_224:openai_ft_in1k	.217
41	convnext_tiny_sup	.216
42	dinov2_vitg14_lc	.215
43	pr1_stl10_disentangled_alpha04	.212
44	gabor_filter_entropy_4x4_v2	.208
45	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.204
46	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.204
47	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.201
48	AlexNet_SIN_fov12	.201
49	focalnet_tiny_lrf_in1k	.200
50	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.199
51	VOneCORnet-S	.198
52	convnext_femto_ols:d1_in1k	.198
53	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.193
54	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.191
55	cvt_cvt-21-384-in1k_4_LucyV4	.191
56	vonealexnet	.190
57	gabor_filter_entropy_4x4	.190
58	resnet50-moclr8deg	.189
59	cvt_cvt-13-384-in1k_4_LucyV4	.185
60	resnet50_ewconplus_wzc_seed2_2__323	.184
61	deepeisnn_ei_cifar10_vgg19	.178
62	mobilenet_v2_1_4_224	.178
63	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.178
64	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.177
65	eva02_large_in22k	.176
66	resnet50-SIN_IN	.172
67	Res2Net50_26w_4s	.165
68	grcnn	.164
69	resnet50_st_wzc_seed6_2_323	.164
70	AlexNet_SIN_fov16	.163
71	eicircuit_resnet18_bn_20260919	.161
72	deepeisnn_ei_cifar10_resnet18	.161
73	resnet50-VITO-8deg-cc	.160
74	resnet50-vitoimagevidnet8	.160
75	vit_large_patch14_clip_224:laion2b_ft_in1k	.160
76	antialiased-r50	.158
77	convnext_base:clip_laiona_augreg_ft_in1k_384	.152
78	deit_large_imagenet_full_seed-0	.152
79	yudixie_wbh_object_class_scaling_0415_imagenet	.150
80	resnet-50-robust	.146
81	seresnext50_32x4d	.144
82	dinov2_vitl14_lc	.143
83	persistence_mem	.143
84	predictive_coding_pc	.142
85	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.137
86	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.137
87	deit3_base_patch16_224_fb_in1k	.137
88	resnet_SIN_IN_FT_IN	.136
89	resnet50-SIN_IN_IN	.136
90	shufflenet_v2_x1_0	.130
91	resnet_50_v1_spiking	.128
92	resnet_50_v1	.128
93	resnet50_tutorial	.128
94	resnet50-sup	.128
95	ViT_L_32_imagenet1k	.127
96	voneresnet-50-non_stochastic	.122
97	convnext_tiny:in12k_ft_in1k	.120
98	convnext_large_clip_laion2b_finelayers	.115
99	convnext_large_in22k_supervised	.114
100	dinov2_vitl14_reg_lc_featread	.113
101	dinov2_vitl14_reg_lc	.113
102	gabor_filter_entropy_4x4_v2_forcedCosine	.112
103	convnext_xxlarge_clip	.109
104	gabor_filter_mean_abs_4x4_v2	.108
105	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.106
106	resnet18_fair	.106
107	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.105
108	densenet-121	.104
109	resnet50_robust_l2_eps1	.097
110	regnety_032	.097
111	hmax	.095
112	yudixie_wbh_object_class_scaling_0415_run_2	.090
113	alexnet_training_seed_02	.090
114	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.089
115	alexnet_training_seed_06	.088
116	resnext101_32x48d_wsl	.087
117	dinov2_vitg14_reg_lc	.081
118	alexnet_training_seed_03	.080
119	alexnet_training_seed_01	.076
120	alexnet_training_seed_09	.070
121	resnet_50_v2	.068
122	compact_resnet50_robust_V4	.063
123	alexnet_training_seed_07	.062
124	alexnet_training_seed_10	.052
125	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.040
126	convnext_base_fb_in22k_ft_in1k	.040
127	yudixie_wbh_object_class_scaling_0415_run_0	.034
128	convnext_base_fb_in1k_gram_aligned_dinov3	.033
129	convnext_base_fb_in1k	.033
130	yudixie_wbh_object_class_scaling_0415_run_1	.020
131	yudixie_wbh_object_class_scaling_0415_run_3	.013
132	resnext101_32x16d_wsl
133	resnext101_32x8d_wsl
134	AdvProp_efficientnet-b6
135	effnetb1_272x240
136	resnext101_32x32d_wsl
137	resnet50_imagenet_full
138	resnet152_imagenet_full
139	efficientnet_b2_imagenet_full
140	deit_small_imagenet_full_seed-0
141	deit_base_imagenet_full_seed-0
142	convnext_large_imagenet_full_seed-0
143	convnext_tiny_imagenet_full_seed-0
144	convnext_small_imagenet_full_seed-0
145	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

0.57.

Note that scores are relative to this ceiling.

Data: Allen2022_fmri_surface.V4

Metric: rdm