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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	mehrer2020_alexnet_training_seed_05	.299
3	convnext_xlarge:fb_in22k_ft_in1k	.298
4	mehrer2020_alexnet_training_seed_01	.297
5	mehrer2020_alexnet_training_seed_03	.296
6	mehrer2020_alexnet_training_seed_04	.295
7	mehrer2020_alexnet_training_seed_02	.295
8	mehrer2020_alexnet_training_seed_06	.289
9	mehrer2020_alexnet_training_seed_09	.288
10	AT_efficientnet-b2	.287
11	AdvProp_efficientnet-b2	.287
12	mehrer2020_alexnet_training_seed_07	.285
13	AlexNet_SIN	.284
14	mehrer2020_alexnet_training_seed_08	.284
15	resnet152_imagenet_full	.277
16	resnet50_st_wzc_seed7_2_323	.272
17	vit_large_patch14_clip_336:laion2b_ft_in1k	.271
18	mehrer2020_alexnet_training_seed_01_fov4	.270
19	vit_huge_clip336_laion	.269
20	AdvProp_efficientnet-b8	.267
21	resnet101_imagenet_full	.265
22	resnet152_ecoset_full	.264
23	alexnet	.264
24	custom_model_cv_18_dagger_408	.263
25	resnet50_st_wzc_seed3_2_323	.262
26	ijepa_vith14_linhead	.260
27	AdvProp_efficientnet-b4	.260
28	pnasnet_large_halfPrecision	.260
29	cvt_cvt-13-224-in1k_4_LucyV4	.259
30	cvt_cvt-21-224-in1k_4_LucyV4	.257
31	ReAlnet07_cornet	.256
32	convnext_large:fb_in22k_ft_in1k	.256
33	efficientnet_b0	.256
34	ReAlnet01_cornet	.250
35	resnet101_ecoset_full	.249
36	vit_relpos_base_patch16_clsgap_224:sw_in1k	.249
37	ReAlnet06_cornet	.249
38	ReAlnet10_cornet	.248
39	ReAlnet04_cornet	.247
40	ReAlnet08_cornet	.246
41	ReAlnet02_cornet	.245
42	persistence_memory	.243
43	ReAlnet05_cornet	.243
44	ReAlnet03_cornet	.243
45	ReAlnet09_cornet	.239
46	ReAlnet01_cornet_fov4	.234
47	vit_large_clip336_openai	.233
48	resnet50_ewconplus_wzc_seed5_2__323	.232
49	resnet50_byol_linhead	.231
50	resnet50_ewconplus_wzc_seed6_2_323	.230
51	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.230
52	efficientnet_b0_imagenet_full	.229
53	voneresnet-50	.225
54	vith14_supervised_linhead	.225
55	vit_large_patch14_clip_224:openai_ft_in1k	.225
56	convnext_tiny_imagenet_full_seed-0	.224
57	deepeisnn_ei_imagenet_resnet18	.224
58	deit_small_imagenet_full_seed-0	.223
59	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.222
60	resnet50-SIN	.220
61	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.220
62	deit3_base_patch16_224_fb_in22k_ft_in1k	.220
63	resnet50_ewconplus_wzc	.220
64	convnext_large_imagenet_full_seed-0	.220
65	AdvProp_efficientnet-b7	.219
66	deit_base_imagenet_full_seed-0	.218
67	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.218
68	dinov2_vitl14_lc_res336	.217
69	AlexNet_SIN_fov4	.217
70	vit_base_patch16_clip_224:openai_ft_in1k	.217
71	convnext_tiny_sup	.216
72	dinov2_vitg14_lc	.215
73	ReAlnet01_cornet_fov12	.214
74	convnext_base_imagenet_full_seed-0	.213
75	pr1_stl10_disentangled_alpha04	.212
76	AdvProp_efficientnet-b6	.209
77	gabor_filter_entropy_4x4_v2	.208
78	ReAlnet01_cornet_fov16	.207
79	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.204
80	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.204
81	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.201
82	AlexNet_SIN_fov12	.201
83	convnext_small_imagenet_full_seed-0	.201
84	mehrer2020_alexnet_training_seed_10	.200
85	focalnet_tiny_lrf_in1k	.200
86	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.199
87	VOneCORnet-S	.198
88	convnext_femto_ols:d1_in1k	.198
89	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.193
90	resnet50_supervised_linhead	.191
91	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.191
92	cvt_cvt-21-384-in1k_4_LucyV4	.191
93	resnet50_imagenet_100_seed-0	.190
94	vonealexnet	.190
95	gabor_filter_entropy_4x4	.190
96	resnet50-moclr8deg	.189
97	cvt_cvt-13-384-in1k_4_LucyV4	.185
98	resnet50_ewconplus_wzc_seed2_2__323	.184
99	resnet50_simsiam_linhead	.182
100	efficientnet_b1_imagenet_full	.181
101	resnet34_ecoset_full	.179
102	deepeisnn_ei_cifar10_vgg19	.178
103	mobilenet_v2_1_4_224	.178
104	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.178
105	efficientnet_b2_imagenet_full	.177
106	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.177
107	resnet50_barlowtwins_linhead	.176
108	eva02_large_in22k	.176
109	resnet50_simclr_linhead_01norm	.175
110	resnet50-SIN_IN	.172
111	resnet18_ecoset_full	.171
112	resnet50_cpc_linhead	.167
113	Res2Net50_26w_4s	.165
114	grcnn	.164
115	resnet50_vicreg_linhead	.164
116	resnet50_st_wzc_seed6_2_323	.164
117	AlexNet_SIN_fov16	.163
118	eicircuit_resnet18_bn_20260919	.161
119	resnet50_imagenet_full	.161
120	deepeisnn_ei_cifar10_resnet18	.161
121	resnet50-VITO-8deg-cc	.160
122	resnet50-vitoimagevidnet8	.160
123	vit_large_patch14_clip_224:laion2b_ft_in1k	.160
124	antialiased-r50	.158
125	convnext_small_imagenet_100_seed-0	.157
126	mehrer2020_alexnet_training_seed_01_fov16	.155
127	convnext_base:clip_laiona_augreg_ft_in1k_384	.152
128	deit_large_imagenet_full_seed-0	.152
129	resnet18_imagenet_full	.151
130	yudixie_wbh_object_class_scaling_0415_imagenet	.150
131	resnet34_imagenet_full	.150
132	mehrer2020_alexnet_training_seed_01_fov12	.147
133	resnet-50-robust	.146
134	seresnext50_32x4d	.144
135	dinov2_vitl14_lc	.143
136	persistence_mem	.143
137	predictive_coding_pc	.142
138	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.137
139	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.137
140	deit3_base_patch16_224_fb_in1k	.137
141	resnet_SIN_IN_FT_IN	.136
142	resnet50-SIN_IN_IN	.136
143	resnet50_ecoset_full	.132
144	shufflenet_v2_x1_0	.130
145	resnet_50_v1_spiking	.128
146	resnet_50_v1	.128
147	resnet50_tutorial	.128
148	resnet50-sup	.128
149	ViT_L_32_imagenet1k	.127
150	voneresnet-50-non_stochastic	.122
151	convnext_tiny:in12k_ft_in1k	.120
152	deit_small_imagenet_100_seed-0	.117
153	resnet50_imagenet_10_seed-0	.115
154	convnext_large_clip_laion2b_finelayers	.115
155	resnet50_ijepacnn_linhead	.115
156	convnext_large_in22k_supervised	.114
157	dinov2_vitl14_reg_lc_featread	.113
158	dinov2_vitl14_reg_lc	.113
159	gabor_filter_entropy_4x4_v2_forcedCosine	.112
160	convnext_xxlarge_clip	.109
161	gabor_filter_mean_abs_4x4_v2	.108
162	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.106
163	resnet18_fair	.106
164	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.105
165	densenet-121	.104
166	resnet50_robust_l2_eps1	.097
167	regnety_032	.097
168	hmax	.095
169	resnext101_32x32d_wsl	.094
170	resnext101_32x8d_wsl	.090
171	yudixie_wbh_object_class_scaling_0415_run_2	.090
172	alexnet_training_seed_02	.090
173	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.089
174	alexnet_training_seed_06	.088
175	resnext101_32x48d_wsl	.087
176	resnext101_32x16d_wsl	.082
177	dinov2_vitg14_reg_lc	.081
178	alexnet_training_seed_03	.080
179	alexnet_training_seed_01	.076
180	alexnet_training_seed_09	.070
181	resnet_50_v2	.068
182	convnext_small_imagenet_10_seed-0	.064
183	compact_resnet50_robust_V4	.063
184	deit_small_imagenet_10_seed-0	.063
185	alexnet_training_seed_07	.062
186	convnext_small_imagenet_1_seed-0	.057
187	deit_small_imagenet_1_seed-0	.056
188	vith14_untrained_linhead	.056
189	resnet50_untrained_linhead	.053
190	alexnet_training_seed_10	.052
191	resnet50_imagenet_1_seed-0	.049
192	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.040
193	convnext_base_fb_in22k_ft_in1k	.040
194	yudixie_wbh_object_class_scaling_0415_run_0	.034
195	convnext_base_fb_in1k_gram_aligned_dinov3	.033
196	convnext_base_fb_in1k	.033
197	yudixie_wbh_object_class_scaling_0415_run_1	.020
198	yudixie_wbh_object_class_scaling_0415_run_3	.013
199	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

0.57.

Note that scores are relative to this ceiling.

Data: Allen2022_fmri_surface.V4

Metric: rdm