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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Zerbe2026_fmri.V4-rdm-pearson")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_huge_clip336_laion	.256
2	custom_model_cv_18_dagger_408	.240
3	cvt_cvt-13-224-in1k_4_LucyV4	.236
4	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.235
5	resnet152_imagenet_full	.235
6	ReAlnet01_cornet	.232
7	pnasnet_large_halfPrecision	.229
8	ReAlnet04_cornet	.227
9	ReAlnet03_cornet	.227
10	cvt_cvt-21-224-in1k_4_LucyV4	.226
11	effnetb1_272x240	.224
12	vit_relpos_base_patch16_clsgap_224:sw_in1k	.220
13	AdvProp_efficientnet-b2	.212
14	AT_efficientnet-b2	.212
15	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.212
16	vit_large_clip336_openai	.212
17	convnext_large:fb_in22k_ft_in1k	.211
18	alexnet	.208
19	efficientnet_b0	.207
20	resnet50-SIN	.205
21	AdvProp_efficientnet-b4	.205
22	resnet50_ewconplus_wzc	.203
23	voneresnet-50	.203
24	AlexNet_SIN	.200
25	vit_large_patch14_clip_224:openai_ft_in1k	.200
26	deit_small_imagenet_full_seed-0	.200
27	deit_base_imagenet_full_seed-0	.199
28	vit_large_patch14_clip_336:laion2b_ft_in1k	.199
29	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.198
30	vit_base_patch16_clip_224:openai_ft_in1k	.195
31	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.193
32	focalnet_tiny_lrf_in1k	.192
33	AdvProp_efficientnet-b8	.190
34	convnext_tiny_imagenet_full_seed-0	.190
35	AdvProp_efficientnet-b7	.189
36	dinov2_vitl14_lc_res336	.189
37	ReAlnet01_cornet_fov4	.187
38	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.187
39	resnet50-moclr8deg	.187
40	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.186
41	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.182
42	AdvProp_efficientnet-b6	.181
43	convnext_tiny_sup	.182
44	convnext_xlarge:fb_in22k_ft_in1k	.181
45	VOneCORnet-S	.180
46	gabor_filter_entropy_4x4_v2	.179
47	dinov2_vitg14_lc	.179
48	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.178
49	convnext_base_imagenet_full_seed-0	.178
50	convnext_large_imagenet_full_seed-0	.175
51	cvt_cvt-13-384-in1k_4_LucyV4	.174
52	convnext_small_imagenet_full_seed-0	.168
53	resnet50-vitoimagevidnet8	.165
54	resnet50-VITO-8deg-cc	.165
55	cvt_cvt-21-384-in1k_4_LucyV4	.165
56	Res2Net50_26w_4s	.164
57	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.160
58	convnext_femto_ols:d1_in1k	.159
59	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.159
60	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.157
61	resnet50-SIN_IN	.156
62	grcnn	.152
63	vit_large_patch14_clip_224:laion2b_ft_in1k	.151
64	AlexNet_SIN_fov12	.152
65	eicircuit_resnet18_bn_20260919	.148
66	gabor_filter_entropy_4x4	.147
67	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.147
68	convnext_base:clip_laiona_augreg_ft_in1k_384	.143
69	efficientnet_b2_imagenet_full	.141
70	antialiased-r50	.135
71	deit_large_imagenet_full_seed-0	.131
72	AlexNet_SIN_fov16	.130
73	resnet-50-robust	.129
74	resnet50_imagenet_full	.128
75	seresnext50_32x4d	.128
76	mobilenet_v2_1_4_224	.124
77	ViT_L_32_imagenet1k	.121
78	dinov2_vitl14_lc	.122
79	resnet_SIN_IN_FT_IN	.118
80	resnet50-SIN_IN_IN	.118
81	voneresnet-50-non_stochastic	.117
82	resnet_50_v1_spiking	.111
83	resnet_50_v1	.111
84	resnet50_tutorial	.111
85	resnet50-sup	.111
86	gabor_filter_entropy_4x4_v2_forcedCosine	.108
87	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.098
88	resnet50_robust_l2_eps1	.095
89	shufflenet_v2_x1_0	.094
90	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.087
91	convnext_tiny:in12k_ft_in1k	.086
92	resnext101_32x32d_wsl	.085
93	regnety_032	.083
94	resnext101_32x8d_wsl	.081
95	dinov2_vitl14_reg_lc	.081
96	convnext_xxlarge_clip	.080
97	resnext101_32x48d_wsl	.079
98	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.078
99	densenet-121	.078
100	alexnet_training_seed_06	.073
101	gabor_filter_mean_abs_4x4_v2	.073
102	alexnet_training_seed_02	.071
103	resnext101_32x16d_wsl	.069
104	alexnet_training_seed_03	.065
105	alexnet_training_seed_01	.064
106	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.061
107	alexnet_training_seed_09	.058
108	alexnet_training_seed_07	.056
109	resnet_50_v2	.055
110	compact_resnet50_robust_V4	.053
111	hmax	.053
112	dinov2_vitg14_reg_lc	.051
113	alexnet_training_seed_10	.042
114	dinov2_vitl14_reg_lc_featread
115	eva02_large_in22k

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.V4-rdm

Metric: pearson