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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Zerbe2026_fmri.V2-ood-ridgecv")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_huge_clip336_laion	.413
2	cvt_cvt-13-384-in1k_4_LucyV4	.402
3	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.400
4	deit_small_imagenet_full_seed-0	.383
5	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.394
6	resnet50-SIN_IN	.392
7	convnext_large:fb_in22k_ft_in1k	.393
8	vit_relpos_base_patch16_clsgap_224:sw_in1k	.391
9	dinov2_vitg14_lc	.391
10	vit_large_clip336_openai	.391
11	dinov2_vitl14_reg_lc_featread	.390
12	dinov2_vitl14_reg_lc	.390
13	dinov2_vitg14_reg_lc	.390
14	antialiased-r50	.388
15	resnet_50_v1_spiking	.389
16	resnet_50_v1	.389
17	resnet50-sup	.389
18	shufflenet_v2_x1_0	.385
19	deit_large_imagenet_full_seed-0	.370
20	focalnet_tiny_lrf_in1k	.385
21	resnet_SIN_IN_FT_IN	.385
22	resnet50-SIN_IN_IN	.385
23	efficientnet_b2_imagenet_full	.383
24	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.383
25	deit_base_imagenet_full_seed-0	.369
26	regnety_032	.383
27	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.381
28	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.381
29	dinov2_vitl14_lc	.380
30	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.377
31	voneresnet-50	.376
32	Res2Net50_26w_4s	.376
33	convnext_tiny_sup	.375
34	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.375
35	resnext101_32x8d_wsl	.373
36	resnet_50_v2	.372
37	cvt_cvt-21-224-in1k_4_LucyV4	.372
38	voneresnet-50-non_stochastic	.373
39	custom_model_cv_18_dagger_408	.370
40	AdvProp_efficientnet-b4	.370
41	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.370
42	resnext101_32x16d_wsl	.369
43	resnet50_tutorial	.370
44	convnext_tiny:in12k_ft_in1k	.367
45	vit_large_patch14_clip_224:openai_ft_in1k	.367
46	resnet50_ewconplus_wzc	.367
47	resnet50-moclr8deg	.367
48	pnasnet_large_halfPrecision	.367
49	resnet50_robust_l2_eps1	.366
50	AdvProp_efficientnet-b6	.364
51	cvt_cvt-13-224-in1k_4_LucyV4	.365
52	convnext_large_imagenet_full_seed-0	.364
53	convnext_femto_ols:d1_in1k	.364
54	resnet152_imagenet_full	.362
55	densenet-121	.361
56	effnetb1_272x240	.362
57	AdvProp_efficientnet-b8	.360
58	resnet50_imagenet_full	.362
59	resnet50-SIN	.359
60	convnext_base:clip_laiona_augreg_ft_in1k_384	.358
61	seresnext50_32x4d	.357
62	cvt_cvt-21-384-in1k_4_LucyV4	.356
63	resnet50-vitoimagevidnet8	.355
64	resnet50-VITO-8deg-cc	.355
65	grcnn	.354
66	resnext101_32x48d_wsl	.353
67	ReAlnet01_cornet	.352
68	ReAlnet04_cornet	.352
69	gabor_filter_entropy_4x4_v2_forcedCosine	.339
70	ReAlnet03_cornet	.351
71	mobilenet_v2_1_4_224	.350
72	gabor_filter_entropy_4x4_v2	.336
73	convnext_xlarge:fb_in22k_ft_in1k	.348
74	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.346
75	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.345
76	resnext101_32x32d_wsl	.344
77	vit_base_patch16_clip_224:openai_ft_in1k	.343
78	resnet-50-robust	.341
79	convnext_tiny_imagenet_full_seed-0	.342
80	convnext_base_imagenet_full_seed-0	.341
81	vit_large_patch14_clip_336:laion2b_ft_in1k	.340
82	AdvProp_efficientnet-b7	.337
83	convnext_small_imagenet_full_seed-0	.334
84	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.334
85	convnext_xxlarge_clip	.332
86	eva02_large_in22k	.332
87	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.326
88	vit_large_patch14_clip_224:laion2b_ft_in1k	.321
89	alexnet	.320
90	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.316
91	gabor_filter_entropy_4x4	.301
92	efficientnet_b0	.310
93	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.309
94	AT_efficientnet-b2	.307
95	AdvProp_efficientnet-b2	.307
96	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.305
97	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.294
98	AlexNet_SIN	.288
99	AlexNet_SIN_fov12	.284
100	VOneCORnet-S	.280
101	AlexNet_SIN_fov16	.277
102	gabor_filter_mean_abs_4x4_v2	.197
103	ReAlnet01_cornet_fov4	.202
104	alexnet_training_seed_06	.186
105	hmax	.178
106	alexnet_training_seed_03	.180
107	ViT_L_32_imagenet1k	.176
108	alexnet_training_seed_02	.166
109	alexnet_training_seed_09	.163
110	alexnet_training_seed_10	.166
111	alexnet_training_seed_07	.160
112	alexnet_training_seed_01	.160
113	compact_resnet50_robust_V4	.103
114	eicircuit_resnet18_bn_20260919
115	dinov2_vitl14_lc_res336

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.V2-ood

Metric: ridgecv