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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Zerbe2026_fmri.V1-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	.425
2	cvt_cvt-13-384-in1k_4_LucyV4	.424
3	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.415
4	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.414
5	vit_relpos_base_patch16_clsgap_224:sw_in1k	.407
6	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.404
7	densenet-121	.402
8	resnext101_32x16d_wsl	.400
9	convnext_large:fb_in22k_ft_in1k	.397
10	shufflenet_v2_x1_0	.397
11	vit_large_clip336_openai	.397
12	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.393
13	vit_large_patch14_clip_224:openai_ft_in1k	.394
14	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.392
15	AdvProp_efficientnet-b6	.391
16	resnet_50_v1_spiking	.391
17	resnet50-sup	.391
18	resnet_50_v1	.391
19	resnet50_tutorial	.391
20	resnext101_32x48d_wsl	.390
21	resnet50-SIN_IN_IN	.390
22	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.388
23	regnety_032	.368
24	convnext_femto_ols:d1_in1k	.386
25	antialiased-r50	.384
26	grcnn	.382
27	convnext_xlarge:fb_in22k_ft_in1k	.380
28	focalnet_tiny_lrf_in1k	.381
29	cvt_cvt-13-224-in1k_4_LucyV4	.379
30	efficientnet_b2_imagenet_full	.377
31	cvt_cvt-21-224-in1k_4_LucyV4	.377
32	AdvProp_efficientnet-b4	.375
33	cvt_cvt-21-384-in1k_4_LucyV4	.376
34	resnet50-SIN	.376
35	custom_model_cv_18_dagger_408	.375
36	resnet_SIN_IN_FT_IN	.376
37	effnetb1_272x240	.374
38	seresnext50_32x4d	.355
39	convnext_tiny_sup	.371
40	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.370
41	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.370
42	pnasnet_large_halfPrecision	.369
43	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.371
44	convnext_base_imagenet_full_seed-0	.365
45	convnext_small_imagenet_full_seed-0	.362
46	dinov2_vitg14_reg_lc	.363
47	mobilenet_v2_1_4_224	.361
48	gabor_filter_entropy_4x4_v2_forcedCosine	.345
49	resnet50-moclr8deg	.359
50	AdvProp_efficientnet-b7	.360
51	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.360
52	gabor_filter_entropy_4x4_v2	.342
53	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.357
54	dinov2_vitg14_lc	.357
55	voneresnet-50	.356
56	vit_large_patch14_clip_336:laion2b_ft_in1k	.353
57	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.352
58	vit_large_patch14_clip_224:laion2b_ft_in1k	.354
59	resnet50_robust_l2_eps1	.350
60	efficientnet_b0	.350
61	dinov2_vitl14_reg_lc	.350
62	eva02_large_in22k	.349
63	AT_efficientnet-b2	.347
64	AdvProp_efficientnet-b2	.347
65	vit_base_patch16_clip_224:openai_ft_in1k	.348
66	dinov2_vitl14_lc	.349
67	resnext101_32x32d_wsl	.346
68	convnext_tiny:in12k_ft_in1k	.344
69	deit_base_imagenet_full_seed-0	.334
70	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.343
71	resnext101_32x8d_wsl	.340
72	resnet50-VITO-8deg-cc	.340
73	resnet50-vitoimagevidnet8	.340
74	AdvProp_efficientnet-b8	.338
75	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.341
76	dinov2_vitl14_lc_res336	.336
77	resnet_50_v2	.334
78	convnext_tiny_imagenet_full_seed-0	.332
79	convnext_large_imagenet_full_seed-0	.331
80	resnet-50-robust	.333
81	deit_small_imagenet_full_seed-0	.320
82	convnext_base:clip_laiona_augreg_ft_in1k_384	.330
83	gabor_filter_entropy_4x4	.307
84	resnet50-SIN_IN	.321
85	deit_large_imagenet_full_seed-0	.308
86	Res2Net50_26w_4s	.317
87	ReAlnet03_cornet	.312
88	ReAlnet01_cornet	.312
89	ReAlnet04_cornet	.312
90	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.310
91	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.310
92	resnet50_ewconplus_wzc	.308
93	alexnet	.299
94	AlexNet_SIN_fov16	.297
95	ViT_L_32_imagenet1k	.296
96	AlexNet_SIN	.288
97	resnet152_imagenet_full	.288
98	AlexNet_SIN_fov12	.284
99	compact_resnet50_robust_V4	.266
100	eicircuit_resnet18_bn_20260919	.242
101	resnet50_imagenet_full	.236
102	gabor_filter_mean_abs_4x4_v2	.211
103	alexnet_training_seed_02	.213
104	alexnet_training_seed_06	.206
105	alexnet_training_seed_09	.203
106	alexnet_training_seed_07	.202
107	alexnet_training_seed_01	.200
108	alexnet_training_seed_10	.196
109	alexnet_training_seed_03	.178
110	hmax	.163
111	ReAlnet01_cornet_fov4	.163
112	voneresnet-50-non_stochastic	.130
113	VOneCORnet-S	.084
114	convnext_xxlarge_clip
115	dinov2_vitl14_reg_lc_featread

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

0.81.

Note that scores are relative to this ceiling.

Data: Zerbe2026_fmri.V1-ood

Metric: ridgecv