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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Zerbe2026_fmri.V2-tau-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	.452
2	cvt_cvt-13-384-in1k_4_LucyV4	.446
3	vit_large_clip336_openai	.445
4	deit_small_imagenet_full_seed-0	.427
5	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.444
6	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.442
7	dinov2_vitl14_lc	.440
8	vit_relpos_base_patch16_clsgap_224:sw_in1k	.439
9	dinov2_vitl14_lc_res336	.438
10	dinov2_vitg14_lc	.435
11	vit_large_patch14_clip_336:laion2b_ft_in1k	.434
12	dinov2_vitl14_reg_lc_featread	.433
13	dinov2_vitl14_reg_lc	.433
14	deit_base_imagenet_full_seed-0	.416
15	dinov2_vitg14_reg_lc	.431
16	focalnet_tiny_lrf_in1k	.430
17	deit_large_imagenet_full_seed-0	.413
18	custom_model_cv_18_dagger_408	.428
19	eva02_large_in22k	.428
20	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.426
21	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.425
22	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.424
23	resnet50-vitoimagevidnet8	.423
24	resnet50-VITO-8deg-cc	.423
25	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.422
26	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.420
27	cvt_cvt-21-224-in1k_4_LucyV4	.418
28	cvt_cvt-21-384-in1k_4_LucyV4	.418
29	resnet50-SIN_IN	.418
30	resnet_SIN_IN_FT_IN	.418
31	resnet50-SIN_IN_IN	.418
32	resnet50-moclr8deg	.417
33	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.416
34	resnet152_imagenet_full	.415
35	resnet50_robust_l2_eps1	.416
36	resnet50_barlowtwins_linhead	.401
37	convnext_large_imagenet_full_seed-0	.415
38	antialiased-r50	.414
39	voneresnet-50-non_stochastic	.415
40	convnext_large:fb_in22k_ft_in1k	.414
41	seresnext50_32x4d	.414
42	vit_large_patch14_clip_224:openai_ft_in1k	.412
43	shufflenet_v2_x1_0	.413
44	resnet_50_v2	.412
45	ijepa_vith14_linhead	.399
46	Res2Net50_26w_4s	.412
47	resnet101_ecoset_full	.396
48	resnet50_byol_linhead	.397
49	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.410
50	convnext_xxlarge_clip	.410
51	resnet50_imagenet_full	.410
52	pnasnet_large_halfPrecision	.409
53	efficientnet_b1_imagenet_full	.394
54	voneresnet-50	.408
55	resnet50_simsiam_linhead	.394
56	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.407
57	resnext101_32x8d_wsl	.405
58	resnet50-SIN	.406
59	resnet_50_v1_spiking	.406
60	resnet_50_v1	.406
61	resnet50-sup	.406
62	resnet152_ecoset_full	.390
63	densenet-121	.404
64	resnet50_vicreg_linhead	.390
65	convnext_tiny_imagenet_full_seed-0	.403
66	convnext_xlarge:fb_in22k_ft_in1k	.402
67	resnext101_32x16d_wsl	.401
68	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.401
69	convnext_tiny_sup	.401
70	convnext_femto_ols:d1_in1k	.401
71	resnet50_tutorial	.401
72	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.399
73	resnet50_ewconplus_wzc	.399
74	AdvProp_efficientnet-b6	.398
75	resnet50_imagenet_100_seed-0	.384
76	eicircuit_resnet18_bn_20260919	.384
77	vit_large_patch14_clip_224:laion2b_ft_in1k	.395
78	cvt_cvt-13-224-in1k_4_LucyV4	.395
79	ReAlnet07_cornet	.381
80	ReAlnet08_cornet	.380
81	resnet18_ecoset_full	.379
82	grcnn	.392
83	AdvProp_efficientnet-b8	.391
84	ReAlnet06_cornet	.378
85	ReAlnet05_cornet	.378
86	effnetb1_272x240	.390
87	ReAlnet09_cornet	.376
88	ReAlnet04_cornet	.390
89	ReAlnet01_cornet	.389
90	ReAlnet02_cornet	.376
91	resnet34_imagenet_full	.376
92	ReAlnet03_cornet	.389
93	ReAlnet10_cornet	.374
94	resnet18_imagenet_full	.372
95	efficientnet_b2_imagenet_full	.386
96	AdvProp_efficientnet-b7	.384
97	AdvProp_efficientnet-b4	.385
98	mobilenet_v2_1_4_224	.383
99	mehrer2020_alexnet_training_seed_05	.369
100	ReAlnet01_cornet_fov12	.369
101	convnext_base_imagenet_full_seed-0	.380
102	mehrer2020_alexnet_training_seed_04	.368
103	convnext_base:clip_laiona_augreg_ft_in1k_384	.380
104	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.380
105	mehrer2020_alexnet_training_seed_02	.366
106	convnext_small_imagenet_100_seed-0	.362
107	mehrer2020_alexnet_training_seed_01	.364
108	resnet34_ecoset_full	.367
109	mehrer2020_alexnet_training_seed_10	.363
110	ReAlnet01_cornet_fov16	.364
111	mehrer2020_alexnet_training_seed_03	.363
112	mehrer2020_alexnet_training_seed_06	.362
113	mehrer2020_alexnet_training_seed_07	.362
114	convnext_small_imagenet_full_seed-0	.374
115	alexnet	.375
116	mehrer2020_alexnet_training_seed_08	.361
117	resnext101_32x48d_wsl	.369
118	convnext_tiny:in12k_ft_in1k	.369
119	mehrer2020_alexnet_training_seed_01_fov12	.354
120	resnet-50-robust	.363
121	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.362
122	AlexNet_SIN	.360
123	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.360
124	resnext101_32x32d_wsl	.359
125	gabor_filter_entropy_4x4_v2_forcedCosine	.350
126	VOneCORnet-S	.358
127	gabor_filter_entropy_4x4_v2	.346
128	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.351
129	AlexNet_SIN_fov12	.351
130	AT_efficientnet-b2	.348
131	AdvProp_efficientnet-b2	.348
132	deit_small_imagenet_100_seed-0	.336
133	resnet50_ecoset_full	.326
134	AlexNet_SIN_fov16	.339
135	efficientnet_b0	.339
136	vit_base_patch16_clip_224:openai_ft_in1k	.335
137	resnet101_imagenet_full	.318
138	mehrer2020_alexnet_training_seed_01_fov16	.319
139	gabor_filter_entropy_4x4	.302
140	regnety_032	.284
141	alexnet_training_seed_06	.243
142	alexnet_training_seed_01	.238
143	alexnet_training_seed_10	.238
144	ReAlnet01_cornet_fov4	.235
145	alexnet_training_seed_03	.230
146	alexnet_training_seed_07	.230
147	mehrer2020_alexnet_training_seed_01_fov4	.215
148	alexnet_training_seed_02	.222
149	gabor_filter_mean_abs_4x4_v2	.212
150	ViT_L_32_imagenet1k	.207
151	alexnet_training_seed_09	.201
152	compact_resnet50_robust_V4	.148
153	resnet50_simclr_linhead_01norm	.126
154	hmax	.087
155	mehrer2020_alexnet_training_seed_09
156	efficientnet_b0_imagenet_full
157	AlexNet_SIN_fov4

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-tau

Metric: ridgecv