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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Allen2022_fmri_surface.V2-rdm")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_large:fb_in22k_ft_in1k	.353
2	AdvProp_efficientnet-b6	.330
3	AlexNet_SIN	.319
4	AdvProp_efficientnet-b8	.313
5	AdvProp_efficientnet-b4	.310
6	vit_large_patch14_clip_336:laion2b_ft_in1k	.308
7	AdvProp_efficientnet-b7	.306
8	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.300
9	resnet50_st_wzc_2_323	.296
10	alexnet	.295
11	resnet50-SIN	.293
12	pnasnet_large_halfPrecision	.293
13	resnet50_st_wzc_seed6_2_323	.292
14	deit3_base_patch16_224_fb_in1k	.292
15	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.292
16	resnet50_st_wzc_seed5_2_323	.291
17	deepeisnn_ei_cifar10_resnet18	.287
18	resnet50_st_wzc_seed4_2_323	.285
19	voneresnet-50	.283
20	resnet50_st_wzc_seed3_2_323	.283
21	resnet50_st_wzc_seed7_2_323	.280
22	resnet50_ewconplus_wzc_seed5_2__323	.277
23	deepeisnn_ei_cifar10_vgg19	.277
24	resnet50_ewconplus_wzc_seed6_2_323	.275
25	resnet_SIN_IN_FT_IN	.272
26	resnet50-SIN_IN_IN	.272
27	antialiased-r50	.271
28	resnet50_robust_l2_eps1	.271
29	AT_efficientnet-b2	.270
30	AdvProp_efficientnet-b2	.270
31	resnet50-SIN_IN	.268
32	resnet50_ewconplus_wzc_seed2_2__323	.267
33	resnet50_st_wzc_noseed_2_323	.267
34	vit_large_patch14_clip_224:laion2b_ft_in1k	.264
35	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.264
36	AlexNet_SIN_fov12	.263
37	resnet50_ewconplus_wzc	.263
38	resnet50_ewconplus_wzc_seed3_2_323	.262
39	resnet50_ewconplus_wzc_seed4_2_323	.261
40	vit_large_patch14_clip_224:openai_ft_in1k	.260
41	AlexNet_SIN_fov16	.257
42	voneresnet-50-non_stochastic	.256
43	resnet50_ewconplus_wzc_seed7_2_323	.256
44	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.256
45	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.256
46	focalnet_tiny_lrf_in1k	.255
47	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.252
48	deit3_base_patch16_224_fb_in22k_ft_in1k	.252
49	cvt_cvt-21-224-in1k_4_LucyV4	.251
50	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.248
51	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.246
52	convnext_femto_ols:d1_in1k	.246
53	resnet50_ewconplus_wzc_2_323	.246
54	grcnn	.241
55	Res2Net50_26w_4s	.236
56	deepeisnn_ei_imagenet_resnet18	.236
57	resnet_50_v1_spiking	.233
58	resnet_50_v1	.233
59	resnet50-sup	.233
60	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.232
61	efficientnet_b0	.231
62	convnext_xlarge:fb_in22k_ft_in1k	.230
63	mobilenet_v2_1_4_224	.228
64	yudixie_wbh_object_class_scaling_0415_run_3	.228
65	resnet-50-robust	.227
66	custom_model_cv_18_dagger_408	.224
67	resnet50-moclr8deg	.215
68	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.209
69	convnext_base:clip_laiona_augreg_ft_in1k_384	.209
70	yudixie_wbh_object_class_scaling_0415_run_0	.208
71	yudixie_wbh_object_class_scaling_0415_run_1	.206
72	resnet50-vitoimagevidnet8	.204
73	resnet50-VITO-8deg-cc	.204
74	cvt_cvt-13-384-in1k_4_LucyV4	.203
75	densenet-121	.197
76	vit_base_patch16_clip_224:openai_ft_in1k	.195
77	convnext_tiny:in12k_ft_in1k	.195
78	convnext_tiny_sup	.193
79	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.192
80	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.192
81	vit_relpos_base_patch16_clsgap_224:sw_in1k	.190
82	cvt_cvt-13-224-in1k_4_LucyV4	.186
83	alexnet_training_seed_06	.184
84	alexnet_training_seed_02	.184
85	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.183
86	cvt_cvt-21-384-in1k_4_LucyV4	.179
87	yudixie_wbh_object_class_scaling_0415_imagenet	.178
88	resnet_50_v2	.178
89	alexnet_training_seed_03	.170
90	alexnet_training_seed_01	.166
91	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.154
92	yudixie_wbh_object_class_scaling_0415_run_2	.154
93	resnext101_32x48d_wsl	.153
94	convnext_base_fb_in1k_gram_aligned_dinov3	.151
95	alexnet_training_seed_09	.151
96	resnet50_tutorial	.149
97	ReAlnet03_cornet	.148
98	alexnet_training_seed_07	.147
99	ReAlnet04_cornet	.146
100	pr1_stl10_disentangled_alpha04	.145
101	ReAlnet01_cornet	.143
102	alexnet_training_seed_10	.130
103	VOneCORnet-S	.126
104	shufflenet_v2_x1_0	.123
105	predictive_coding_pc	.123
106	ViT_L_32_imagenet1k	.119
107	resnet18_fair	.111
108	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.107
109	ReAlnet01_cornet_fov4	.100
110	compact_resnet50_robust_V4	.087
111	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.081
112	convnext_base_fb_in22k_ft_in1k	.079
113	hmax	.048
114	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.047
115	convnext_base_fb_in1k	.040
116	resnext101_32x32d_wsl
117	resnext101_32x8d_wsl
118	resnext101_32x16d_wsl
119	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288
120	effnetb1_cutmix_augmix_sam_e1_5avg_424x377
121	resnet50_imagenet_full
122	resnet152_imagenet_full
123	effnetb1_272x240
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_base_imagenet_full_seed-0
130	convnext_tiny_imagenet_full_seed-0
131	convnext_small_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

0.62.

Note that scores are relative to this ceiling.

Data: Allen2022_fmri_surface.V2

Metric: rdm