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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Zerbe2026_fmri.V1-rdm-pearson")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_large_clip336_openai	.330
2	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.329
3	AdvProp_efficientnet-b7	.325
4	AdvProp_efficientnet-b6	.323
5	convnext_femto_ols:d1_in1k	.322
6	vit_large_patch14_clip_224:openai_ft_in1k	.319
7	efficientnet_b0	.319
8	AdvProp_efficientnet-b4	.314
9	convnext_xlarge:fb_in22k_ft_in1k	.306
10	AdvProp_efficientnet-b8	.303
11	AT_efficientnet-b2	.303
12	AdvProp_efficientnet-b2	.303
13	alexnet	.300
14	vit_large_patch14_clip_224:laion2b_ft_in1k	.301
15	convnext_small_imagenet_full_seed-0	.301
16	effnetb1_272x240	.299
17	resnet_SIN_IN_FT_IN	.296
18	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.295
19	AlexNet_SIN	.294
20	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.294
21	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.291
22	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.291
23	gabor_filter_entropy_4x4_v2	.289
24	vit_large_patch14_clip_336:laion2b_ft_in1k	.288
25	convnext_tiny_sup	.281
26	deit_base_imagenet_full_seed-0	.278
27	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.277
28	AlexNet_SIN_fov12	.275
29	convnext_large:fb_in22k_ft_in1k	.271
30	voneresnet-50	.270
31	antialiased-r50	.268
32	custom_model_cv_18_dagger_408	.268
33	pnasnet_large_halfPrecision	.265
34	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.264
35	cvt_cvt-13-384-in1k_4_LucyV4	.262
36	grcnn	.263
37	AlexNet_SIN_fov16	.263
38	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.255
39	vit_relpos_base_patch16_clsgap_224:sw_in1k	.254
40	gabor_filter_entropy_4x4	.253
41	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.253
42	resnet50-SIN	.252
43	vit_base_patch16_clip_224:openai_ft_in1k	.245
44	resnet-50-robust	.245
45	convnext_base:clip_laiona_augreg_ft_in1k_384	.243
46	convnext_base_imagenet_full_seed-0	.240
47	deit_large_imagenet_full_seed-0	.238
48	dinov2_vitl14_lc	.230
49	cvt_cvt-13-224-in1k_4_LucyV4	.229
50	ReAlnet03_cornet	.225
51	compact_resnet50_robust_V4	.223
52	ReAlnet04_cornet	.222
53	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.218
54	ReAlnet01_cornet	.218
55	efficientnet_b2_imagenet_full	.213
56	deit_small_imagenet_full_seed-0	.208
57	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.204
58	convnext_tiny_imagenet_full_seed-0	.200
59	mobilenet_v2_1_4_224	.194
60	gabor_filter_entropy_4x4_v2_forcedCosine	.193
61	resnet50-SIN_IN	.191
62	convnext_large_imagenet_full_seed-0	.191
63	dinov2_vitl14_lc_res336	.186
64	resnet50_ewconplus_wzc	.184
65	resnet50-SIN_IN_IN	.181
66	seresnext50_32x4d	.178
67	alexnet_training_seed_06	.177
68	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.172
69	convnext_tiny:in12k_ft_in1k	.172
70	resnet152_imagenet_full	.171
71	resnet_50_v1_spiking	.170
72	resnet50_tutorial	.170
73	resnet_50_v1	.170
74	resnet50-sup	.170
75	cvt_cvt-21-224-in1k_4_LucyV4	.168
76	cvt_cvt-21-384-in1k_4_LucyV4	.165
77	focalnet_tiny_lrf_in1k	.163
78	alexnet_training_seed_03	.164
79	Res2Net50_26w_4s	.156
80	resnet50-vitoimagevidnet8	.150
81	resnet50-VITO-8deg-cc	.150
82	alexnet_training_seed_02	.150
83	ReAlnet01_cornet_fov4	.141
84	alexnet_training_seed_01	.142
85	dinov2_vitg14_lc	.139
86	resnet50_imagenet_full	.136
87	ViT_L_32_imagenet1k	.131
88	alexnet_training_seed_07	.131
89	densenet-121	.129
90	alexnet_training_seed_09	.130
91	shufflenet_v2_x1_0	.118
92	resnet50_robust_l2_eps1	.115
93	convnext_xxlarge_clip	.114
94	gabor_filter_mean_abs_4x4_v2	.114
95	alexnet_training_seed_10	.113
96	regnety_032	.108
97	eicircuit_resnet18_bn_20260919	.107
98	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.099
99	resnext101_32x16d_wsl	.084
100	resnext101_32x8d_wsl	.083
101	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.082
102	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.082
103	voneresnet-50-non_stochastic	.074
104	resnet50-moclr8deg	.071
105	resnet_50_v2	.065
106	resnext101_32x48d_wsl	.063
107	VOneCORnet-S	.063
108	dinov2_vitl14_reg_lc_featread	.054
109	dinov2_vitl14_reg_lc	.054
110	resnext101_32x32d_wsl	.052
111	dinov2_vitg14_reg_lc	.048
112	hmax	.044
113	vit_huge_clip336_laion
114	eva02_large_in22k
115	convnext_xxlarge:clip_laion2b_soup_ft_in1k

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

0.79.

Note that scores are relative to this ceiling.

Data: Zerbe2026_fmri.V1-rdm

Metric: pearson