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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	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.204
45	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.204
46	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.201
47	AlexNet_SIN_fov12	.201
48	focalnet_tiny_lrf_in1k	.200
49	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.199
50	VOneCORnet-S	.198
51	convnext_femto_ols:d1_in1k	.198
52	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.193
53	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.191
54	cvt_cvt-21-384-in1k_4_LucyV4	.191
55	vonealexnet	.190
56	resnet50-moclr8deg	.189
57	cvt_cvt-13-384-in1k_4_LucyV4	.185
58	resnet50_ewconplus_wzc_seed2_2__323	.184
59	deepeisnn_ei_cifar10_vgg19	.178
60	mobilenet_v2_1_4_224	.178
61	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.178
62	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.177
63	eva02_large_in22k	.176
64	resnet50-SIN_IN	.172
65	Res2Net50_26w_4s	.165
66	grcnn	.164
67	resnet50_st_wzc_seed6_2_323	.164
68	AlexNet_SIN_fov16	.163
69	deepeisnn_ei_cifar10_resnet18	.161
70	resnet50-VITO-8deg-cc	.160
71	resnet50-vitoimagevidnet8	.160
72	vit_large_patch14_clip_224:laion2b_ft_in1k	.160
73	antialiased-r50	.158
74	convnext_base:clip_laiona_augreg_ft_in1k_384	.152
75	deit_large_imagenet_full_seed-0	.152
76	yudixie_wbh_object_class_scaling_0415_imagenet	.150
77	resnet-50-robust	.146
78	dinov2_vitl14_lc	.143
79	persistence_mem	.143
80	predictive_coding_pc	.142
81	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.137
82	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.137
83	deit3_base_patch16_224_fb_in1k	.137
84	resnet_SIN_IN_FT_IN	.136
85	resnet50-SIN_IN_IN	.136
86	shufflenet_v2_x1_0	.130
87	resnet_50_v1_spiking	.128
88	resnet_50_v1	.128
89	resnet50_tutorial	.128
90	resnet50-sup	.128
91	ViT_L_32_imagenet1k	.127
92	voneresnet-50-non_stochastic	.122
93	convnext_tiny:in12k_ft_in1k	.120
94	convnext_large_clip_laion2b_finelayers	.115
95	convnext_large_in22k_supervised	.114
96	dinov2_vitl14_reg_lc_featread	.113
97	dinov2_vitl14_reg_lc	.113
98	convnext_xxlarge_clip	.109
99	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.106
100	resnet18_fair	.106
101	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.105
102	densenet-121	.104
103	resnet50_robust_l2_eps1	.097
104	hmax	.095
105	yudixie_wbh_object_class_scaling_0415_run_2	.090
106	alexnet_training_seed_02	.090
107	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.089
108	alexnet_training_seed_06	.088
109	resnext101_32x48d_wsl	.087
110	dinov2_vitg14_reg_lc	.081
111	alexnet_training_seed_03	.080
112	alexnet_training_seed_01	.076
113	alexnet_training_seed_09	.070
114	resnet_50_v2	.068
115	compact_resnet50_robust_V4	.063
116	alexnet_training_seed_07	.062
117	alexnet_training_seed_10	.052
118	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.040
119	convnext_base_fb_in22k_ft_in1k	.040
120	yudixie_wbh_object_class_scaling_0415_run_0	.034
121	convnext_base_fb_in1k_gram_aligned_dinov3	.033
122	convnext_base_fb_in1k	.033
123	yudixie_wbh_object_class_scaling_0415_run_1	.020
124	yudixie_wbh_object_class_scaling_0415_run_3	.013
125	resnext101_32x16d_wsl
126	resnext101_32x8d_wsl
127	AdvProp_efficientnet-b6
128	effnetb1_272x240
129	resnext101_32x32d_wsl
130	resnet50_imagenet_full
131	resnet152_imagenet_full
132	efficientnet_b2_imagenet_full
133	deit_small_imagenet_full_seed-0
134	deit_base_imagenet_full_seed-0
135	convnext_large_imagenet_full_seed-0
136	convnext_tiny_imagenet_full_seed-0
137	convnext_small_imagenet_full_seed-0
138	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