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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	mehrer2020_alexnet_training_seed_05	.291
3	focalnet_tiny_lrf_in1k	.289
4	mehrer2020_alexnet_training_seed_04	.288
5	mehrer2020_alexnet_training_seed_02	.287
6	mehrer2020_alexnet_training_seed_03	.286
7	resnet50_robust_l2_eps1	.284
8	mehrer2020_alexnet_training_seed_01	.284
9	alexnet	.284
10	mehrer2020_alexnet_training_seed_10	.283
11	vit_large_clip336_openai	.283
12	effnetb1_272x240	.283
13	resnet50-SIN_IN	.282
14	antialiased-r50	.282
15	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.282
16	mehrer2020_alexnet_training_seed_09	.281
17	mehrer2020_alexnet_training_seed_07	.281
18	AdvProp_efficientnet-b6	.280
19	mehrer2020_alexnet_training_seed_06	.280
20	gabor_filter_entropy_4x4_v2	.278
21	AdvProp_efficientnet-b4	.278
22	resnet_SIN_IN_FT_IN	.277
23	resnet50-SIN_IN_IN	.277
24	efficientnet_b0_imagenet_full	.269
25	convnext_large:fb_in22k_ft_in1k	.269
26	dinov2_vitl14_lc	.268
27	resnet50-SIN	.266
28	resnet50_ewconplus_wzc	.266
29	voneresnet-50-non_stochastic	.263
30	resnet34_ecoset_full	.261
31	AdvProp_efficientnet-b8	.261
32	vit_large_patch14_clip_336:laion2b_ft_in1k	.259
33	ijepa_vith14_linhead	.259
34	pnasnet_large_halfPrecision	.259
35	dinov2_vitl14_lc_res336	.257
36	resnet_50_v1_spiking	.257
37	resnet_50_v1	.257
38	resnet50-sup	.257
39	resnet152_imagenet_full	.257
40	voneresnet-50	.256
41	cvt_cvt-21-224-in1k_4_LucyV4	.256
42	Res2Net50_26w_4s	.255
43	convnext_large_imagenet_full_seed-0	.255
44	AlexNet_SIN	.254
45	resnet101_ecoset_full	.253
46	resnet152_ecoset_full	.253
47	AdvProp_efficientnet-b7	.252
48	resnet34_imagenet_full	.252
49	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.252
50	resnet50_imagenet_100_seed-0	.252
51	resnet18_imagenet_full	.252
52	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.250
53	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.250
54	eicircuit_resnet18_bn_20260919	.249
55	gabor_filter_entropy_4x4	.247
56	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.246
57	resnet18_ecoset_full	.243
58	custom_model_cv_18_dagger_408	.240
59	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.239
60	vit_large_patch14_clip_224:openai_ft_in1k	.236
61	convnext_xlarge:fb_in22k_ft_in1k	.235
62	grcnn	.234
63	deit_large_imagenet_full_seed-0	.234
64	vit_large_patch14_clip_224:laion2b_ft_in1k	.231
65	AlexNet_SIN_fov12	.231
66	dinov2_vitg14_lc	.229
67	convnext_tiny_imagenet_full_seed-0	.230
68	deit_base_imagenet_full_seed-0	.229
69	resnet-50-robust	.228
70	resnet50_imagenet_full	.225
71	convnext_femto_ols:d1_in1k	.225
72	mehrer2020_alexnet_training_seed_01_fov12	.223
73	AlexNet_SIN_fov16	.222
74	resnet50-moclr8deg	.221
75	cvt_cvt-13-384-in1k_4_LucyV4	.218
76	resnet50-VITO-8deg-cc	.218
77	resnet50-vitoimagevidnet8	.218
78	convnext_small_imagenet_100_seed-0	.219
79	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.217
80	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.217
81	resnet50_byol_linhead	.216
82	AdvProp_efficientnet-b2	.215
83	AT_efficientnet-b2	.215
84	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.205
85	mehrer2020_alexnet_training_seed_01_fov4	.204
86	efficientnet_b0	.203
87	convnext_tiny_sup	.202
88	resnet101_imagenet_full	.200
89	deit_small_imagenet_100_seed-0	.200
90	vit_relpos_base_patch16_clsgap_224:sw_in1k	.197
91	mehrer2020_alexnet_training_seed_01_fov16	.195
92	densenet-121	.190
93	convnext_base_imagenet_full_seed-0	.189
94	cvt_cvt-13-224-in1k_4_LucyV4	.188
95	convnext_base:clip_laiona_augreg_ft_in1k_384	.188
96	convnext_small_imagenet_full_seed-0	.187
97	cvt_cvt-21-384-in1k_4_LucyV4	.186
98	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.187
99	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.186
100	vit_base_patch16_clip_224:openai_ft_in1k	.186
101	AlexNet_SIN_fov4	.184
102	resnext101_32x8d_wsl	.179
103	resnext101_32x16d_wsl	.178
104	mobilenet_v2_1_4_224	.175
105	ReAlnet08_cornet	.173
106	efficientnet_b2_imagenet_full	.173
107	resnet50_ecoset_full	.170
108	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.170
109	resnext101_32x48d_wsl	.167
110	resnet_50_v2	.166
111	convnext_tiny:in12k_ft_in1k	.167
112	ReAlnet06_cornet	.164
113	ReAlnet05_cornet	.164
114	resnext101_32x32d_wsl	.164
115	gabor_filter_entropy_4x4_v2_forcedCosine	.164
116	ReAlnet07_cornet	.161
117	ReAlnet10_cornet	.160
118	seresnext50_32x4d	.155
119	resnet50_simsiam_linhead	.149
120	ReAlnet02_cornet	.148
121	ReAlnet09_cornet	.145
122	ReAlnet03_cornet	.144
123	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.144
124	ReAlnet04_cornet	.142
125	resnet50_tutorial	.142
126	efficientnet_b1_imagenet_full	.138
127	ReAlnet01_cornet	.137
128	alexnet_training_seed_06	.136
129	VOneCORnet-S	.133
130	ReAlnet01_cornet_fov12	.125
131	alexnet_training_seed_02	.125
132	dinov2_vitl14_reg_lc_featread	.113
133	dinov2_vitl14_reg_lc	.113
134	shufflenet_v2_x1_0	.108
135	alexnet_training_seed_01	.109
136	gabor_filter_mean_abs_4x4_v2	.107
137	alexnet_training_seed_03	.105
138	compact_resnet50_robust_V4	.103
139	ReAlnet01_cornet_fov4	.102
140	alexnet_training_seed_07	.101
141	alexnet_training_seed_09	.099
142	regnety_032	.097
143	convnext_xxlarge_clip	.092
144	alexnet_training_seed_10	.090
145	resnet50_vicreg_linhead	.087
146	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.086
147	ViT_L_32_imagenet1k	.081
148	resnet50_barlowtwins_linhead	.076
149	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.067
150	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.066
151	dinov2_vitg14_reg_lc	.064
152	hmax	.053
153	resnet50_simclr_linhead_01norm	.051
154	mehrer2020_alexnet_training_seed_08
155	ReAlnet01_cornet_fov16
156	vit_huge_clip336_laion
157	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.V2-rdm

Metric: pearson