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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Zerbe2026_fmri.V2-rdm-pearson")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	deit_small_imagenet_full_seed-0	.298
2	focalnet_tiny_lrf_in1k	.289
3	resnet50_robust_l2_eps1	.284
4	alexnet	.284
5	vit_large_clip336_openai	.283
6	effnetb1_272x240	.283
7	resnet50-SIN_IN	.282
8	antialiased-r50	.282
9	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.282
10	AdvProp_efficientnet-b6	.280
11	gabor_filter_entropy_4x4_v2	.278
12	AdvProp_efficientnet-b4	.278
13	resnet_SIN_IN_FT_IN	.277
14	resnet50-SIN_IN_IN	.277
15	convnext_large:fb_in22k_ft_in1k	.269
16	dinov2_vitl14_lc	.268
17	resnet50-SIN	.266
18	resnet50_ewconplus_wzc	.266
19	voneresnet-50-non_stochastic	.263
20	AdvProp_efficientnet-b8	.261
21	vit_large_patch14_clip_336:laion2b_ft_in1k	.259
22	pnasnet_large_halfPrecision	.259
23	dinov2_vitl14_lc_res336	.257
24	resnet_50_v1_spiking	.257
25	resnet_50_v1	.257
26	resnet50-sup	.257
27	resnet152_imagenet_full	.257
28	voneresnet-50	.256
29	cvt_cvt-21-224-in1k_4_LucyV4	.256
30	Res2Net50_26w_4s	.255
31	convnext_large_imagenet_full_seed-0	.255
32	AlexNet_SIN	.254
33	AdvProp_efficientnet-b7	.252
34	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.252
35	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.250
36	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.250
37	eicircuit_resnet18_bn_20260919	.249
38	gabor_filter_entropy_4x4	.247
39	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.246
40	custom_model_cv_18_dagger_408	.240
41	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.239
42	vit_large_patch14_clip_224:openai_ft_in1k	.236
43	convnext_xlarge:fb_in22k_ft_in1k	.235
44	grcnn	.234
45	deit_large_imagenet_full_seed-0	.234
46	vit_large_patch14_clip_224:laion2b_ft_in1k	.231
47	AlexNet_SIN_fov12	.231
48	dinov2_vitg14_lc	.229
49	convnext_tiny_imagenet_full_seed-0	.230
50	deit_base_imagenet_full_seed-0	.229
51	resnet-50-robust	.228
52	resnet50_imagenet_full	.225
53	convnext_femto_ols:d1_in1k	.225
54	AlexNet_SIN_fov16	.222
55	resnet50-moclr8deg	.221
56	cvt_cvt-13-384-in1k_4_LucyV4	.218
57	resnet50-VITO-8deg-cc	.218
58	resnet50-vitoimagevidnet8	.218
59	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.217
60	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.217
61	AdvProp_efficientnet-b2	.215
62	AT_efficientnet-b2	.215
63	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.205
64	efficientnet_b0	.203
65	convnext_tiny_sup	.202
66	vit_relpos_base_patch16_clsgap_224:sw_in1k	.197
67	densenet-121	.190
68	convnext_base_imagenet_full_seed-0	.189
69	cvt_cvt-13-224-in1k_4_LucyV4	.188
70	convnext_base:clip_laiona_augreg_ft_in1k_384	.188
71	convnext_small_imagenet_full_seed-0	.187
72	cvt_cvt-21-384-in1k_4_LucyV4	.186
73	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.187
74	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.186
75	vit_base_patch16_clip_224:openai_ft_in1k	.186
76	resnext101_32x8d_wsl	.179
77	mobilenet_v2_1_4_224	.175
78	efficientnet_b2_imagenet_full	.173
79	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.170
80	resnext101_32x48d_wsl	.167
81	resnet_50_v2	.166
82	convnext_tiny:in12k_ft_in1k	.167
83	resnext101_32x32d_wsl	.164
84	gabor_filter_entropy_4x4_v2_forcedCosine	.164
85	seresnext50_32x4d	.155
86	ReAlnet03_cornet	.144
87	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.144
88	ReAlnet04_cornet	.142
89	resnet50_tutorial	.142
90	ReAlnet01_cornet	.137
91	alexnet_training_seed_06	.136
92	VOneCORnet-S	.133
93	alexnet_training_seed_02	.125
94	dinov2_vitl14_reg_lc_featread	.113
95	dinov2_vitl14_reg_lc	.113
96	shufflenet_v2_x1_0	.108
97	alexnet_training_seed_01	.109
98	gabor_filter_mean_abs_4x4_v2	.107
99	alexnet_training_seed_03	.105
100	compact_resnet50_robust_V4	.103
101	ReAlnet01_cornet_fov4	.102
102	alexnet_training_seed_07	.101
103	alexnet_training_seed_09	.099
104	regnety_032	.097
105	convnext_xxlarge_clip	.092
106	alexnet_training_seed_10	.090
107	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.086
108	ViT_L_32_imagenet1k	.081
109	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.067
110	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.066
111	dinov2_vitg14_reg_lc	.064
112	hmax	.053
113	vit_huge_clip336_laion
114	eva02_large_in22k
115	resnext101_32x16d_wsl

Benchmark bibtex

@inproceedings{zerbe_laion-fmri_2026,
      title = {{LAION}-{fMRI}: A densely sampled 7T-fMRI dataset providing broad coverage of natural image diversity},
      author = {Zerbe, Josefine and Roth, Johannes and Mell, Maggie Mae and Herholz, Peer and Knapen, Tomas and Hebart, Martin N.},
      year = {2026},
      booktitle = {Vision Sciences Society Annual Meeting},
  }

Ceiling

0.77.

Note that scores are relative to this ceiling.

Data: Zerbe2026_fmri.V2-rdm

Metric: pearson