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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	ReAlnet03_cornet	.243
23	ReAlnet01_cornet_fov4	.234
24	vit_large_clip336_openai	.233
25	resnet50_ewconplus_wzc_seed5_2__323	.232
26	resnet50_ewconplus_wzc_seed6_2_323	.230
27	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.230
28	voneresnet-50	.225
29	vit_large_patch14_clip_224:openai_ft_in1k	.225
30	deepeisnn_ei_imagenet_resnet18	.224
31	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.222
32	resnet50-SIN	.220
33	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.220
34	deit3_base_patch16_224_fb_in22k_ft_in1k	.220
35	resnet50_ewconplus_wzc	.220
36	AdvProp_efficientnet-b7	.219
37	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.218
38	dinov2_vitl14_lc_res336	.217
39	vit_base_patch16_clip_224:openai_ft_in1k	.217
40	convnext_tiny_sup	.216
41	dinov2_vitg14_lc	.215
42	pr1_stl10_disentangled_alpha04	.212
43	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.204
44	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.204
45	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.201
46	AlexNet_SIN_fov12	.201
47	focalnet_tiny_lrf_in1k	.200
48	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.199
49	VOneCORnet-S	.198
50	convnext_femto_ols:d1_in1k	.198
51	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.193
52	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.191
53	cvt_cvt-21-384-in1k_4_LucyV4	.191
54	resnet50-moclr8deg	.189
55	cvt_cvt-13-384-in1k_4_LucyV4	.185
56	resnet50_ewconplus_wzc_seed2_2__323	.184
57	deepeisnn_ei_cifar10_vgg19	.178
58	mobilenet_v2_1_4_224	.178
59	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.178
60	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.177
61	eva02_large_in22k	.176
62	resnet50-SIN_IN	.172
63	Res2Net50_26w_4s	.165
64	grcnn	.164
65	resnet50_st_wzc_seed6_2_323	.164
66	AlexNet_SIN_fov16	.163
67	deepeisnn_ei_cifar10_resnet18	.161
68	resnet50-VITO-8deg-cc	.160
69	resnet50-vitoimagevidnet8	.160
70	vit_large_patch14_clip_224:laion2b_ft_in1k	.160
71	antialiased-r50	.158
72	convnext_base:clip_laiona_augreg_ft_in1k_384	.152
73	deit_large_imagenet_full_seed-0	.152
74	yudixie_wbh_object_class_scaling_0415_imagenet	.150
75	resnet-50-robust	.146
76	dinov2_vitl14_lc	.143
77	predictive_coding_pc	.142
78	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.137
79	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.137
80	deit3_base_patch16_224_fb_in1k	.137
81	resnet_SIN_IN_FT_IN	.136
82	resnet50-SIN_IN_IN	.136
83	shufflenet_v2_x1_0	.130
84	resnet_50_v1_spiking	.128
85	resnet_50_v1	.128
86	resnet50_tutorial	.128
87	resnet50-sup	.128
88	ViT_L_32_imagenet1k	.127
89	voneresnet-50-non_stochastic	.122
90	convnext_tiny:in12k_ft_in1k	.120
91	convnext_large_clip_laion2b_finelayers	.115
92	convnext_large_in22k_supervised	.114
93	dinov2_vitl14_reg_lc_featread	.113
94	dinov2_vitl14_reg_lc	.113
95	convnext_xxlarge_clip	.109
96	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.106
97	resnet18_fair	.106
98	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.105
99	densenet-121	.104
100	resnet50_robust_l2_eps1	.097
101	hmax	.095
102	yudixie_wbh_object_class_scaling_0415_run_2	.090
103	alexnet_training_seed_02	.090
104	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.089
105	alexnet_training_seed_06	.088
106	resnext101_32x48d_wsl	.087
107	dinov2_vitg14_reg_lc	.081
108	alexnet_training_seed_03	.080
109	alexnet_training_seed_01	.076
110	alexnet_training_seed_09	.070
111	resnet_50_v2	.068
112	compact_resnet50_robust_V4	.063
113	alexnet_training_seed_07	.062
114	alexnet_training_seed_10	.052
115	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.040
116	convnext_base_fb_in22k_ft_in1k	.040
117	yudixie_wbh_object_class_scaling_0415_run_0	.034
118	convnext_base_fb_in1k_gram_aligned_dinov3	.033
119	convnext_base_fb_in1k	.033
120	yudixie_wbh_object_class_scaling_0415_run_1	.020
121	yudixie_wbh_object_class_scaling_0415_run_3	.013
122	resnext101_32x16d_wsl
123	resnext101_32x8d_wsl
124	AdvProp_efficientnet-b6
125	effnetb1_272x240
126	resnext101_32x32d_wsl
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

0.57.

Note that scores are relative to this ceiling.

Data: Allen2022_fmri_surface.V4

Metric: rdm