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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_seed5_2_323	.273
7	resnet50_st_wzc_seed7_2_323	.272
8	resnet50_st_wzc_2_323	.271
9	vit_large_patch14_clip_336:laion2b_ft_in1k	.271
10	AdvProp_efficientnet-b8	.267
11	alexnet	.264
12	custom_model_cv_18_dagger_408	.263
13	resnet50_st_wzc_seed3_2_323	.262
14	AdvProp_efficientnet-b4	.260
15	pnasnet_large_halfPrecision	.260
16	cvt_cvt-13-224-in1k_4_LucyV4	.259
17	cvt_cvt-21-224-in1k_4_LucyV4	.257
18	convnext_large:fb_in22k_ft_in1k	.256
19	efficientnet_b0	.256
20	ReAlnet01_cornet	.250
21	vit_relpos_base_patch16_clsgap_224:sw_in1k	.249
22	ReAlnet04_cornet	.247
23	ReAlnet03_cornet	.243
24	ReAlnet01_cornet_fov4	.234
25	resnet50_ewconplus_wzc_2_323	.234
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	resnet50_ewconplus_wzc_seed3_2_323	.226
30	voneresnet-50	.225
31	vit_large_patch14_clip_224:openai_ft_in1k	.225
32	deepeisnn_ei_imagenet_resnet18	.224
33	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.222
34	resnet50-SIN	.220
35	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.220
36	deit3_base_patch16_224_fb_in22k_ft_in1k	.220
37	resnet50_ewconplus_wzc	.220
38	AdvProp_efficientnet-b7	.219
39	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.218
40	vit_base_patch16_clip_224:openai_ft_in1k	.217
41	convnext_tiny_sup	.216
42	pr1_stl10_disentangled_alpha04	.212
43	resnet50_ewconplus_wzc_seed7_2_323	.212
44	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.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	resnet50_ewconplus_wzc_seed4_2_323	.187
56	cvt_cvt-13-384-in1k_4_LucyV4	.185
57	resnet50_ewconplus_wzc_seed2_2__323	.184
58	deepeisnn_ei_cifar10_vgg19	.178
59	mobilenet_v2_1_4_224	.178
60	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.178
61	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.177
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	resnet50_st_wzc_seed4_2_323	.159
72	antialiased-r50	.158
73	convnext_base:clip_laiona_augreg_ft_in1k_384	.152
74	yudixie_wbh_object_class_scaling_0415_imagenet	.150
75	resnet-50-robust	.146
76	predictive_coding_pc	.142
77	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.137
78	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.137
79	deit3_base_patch16_224_fb_in1k	.137
80	resnet_SIN_IN_FT_IN	.136
81	resnet50-SIN_IN_IN	.136
82	resnet50_st_wzc_noseed_2_323	.132
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	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.106
92	resnet18_fair	.106
93	densenet-121	.104
94	resnet50_robust_l2_eps1	.097
95	hmax	.095
96	yudixie_wbh_object_class_scaling_0415_run_2	.090
97	alexnet_training_seed_02	.090
98	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.089
99	alexnet_training_seed_06	.088
100	resnext101_32x48d_wsl	.087
101	alexnet_training_seed_03	.080
102	alexnet_training_seed_01	.076
103	alexnet_training_seed_09	.070
104	resnet_50_v2	.068
105	compact_resnet50_robust_V4	.063
106	alexnet_training_seed_07	.062
107	alexnet_training_seed_10	.052
108	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.040
109	convnext_base_fb_in22k_ft_in1k	.040
110	yudixie_wbh_object_class_scaling_0415_run_0	.034
111	convnext_base_fb_in1k_gram_aligned_dinov3	.033
112	convnext_base_fb_in1k	.033
113	yudixie_wbh_object_class_scaling_0415_run_1	.020
114	yudixie_wbh_object_class_scaling_0415_run_3	.013
115	resnext101_32x16d_wsl
116	resnext101_32x8d_wsl
117	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288
118	effnetb1_cutmix_augmix_sam_e1_5avg_424x377
119	AdvProp_efficientnet-b6
120	effnetb1_272x240
121	resnext101_32x32d_wsl
122	resnet50_imagenet_full
123	resnet152_imagenet_full
124	efficientnet_b2_imagenet_full
125	deit_large_imagenet_full_seed-0
126	deit_small_imagenet_full_seed-0
127	deit_base_imagenet_full_seed-0
128	convnext_large_imagenet_full_seed-0
129	convnext_tiny_imagenet_full_seed-0
130	convnext_small_imagenet_full_seed-0
131	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