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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Zerbe2026_fmri.V1-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	.493
2	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.492
3	cvt_cvt-13-384-in1k_4_LucyV4	.491
4	vit_large_clip336_openai	.490
5	vit_large_patch14_clip_224:openai_ft_in1k	.488
6	vit_large_patch14_clip_336:laion2b_ft_in1k	.483
7	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.484
8	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.483
9	eva02_large_in22k	.481
10	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.478
11	vit_relpos_base_patch16_clsgap_224:sw_in1k	.476
12	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.474
13	antialiased-r50	.473
14	resnet_SIN_IN_FT_IN	.474
15	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.472
16	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.469
17	efficientnet_b2_imagenet_full	.467
18	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.463
19	convnext_femto_ols:d1_in1k	.463
20	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.463
21	resnet50-SIN_IN_IN	.462
22	dinov2_vitl14_lc	.461
23	grcnn	.460
24	voneresnet-50	.460
25	vit_base_patch16_clip_224:openai_ft_in1k	.459
26	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.460
27	resnet50-SIN	.458
28	convnext_base:clip_laiona_augreg_ft_in1k_384	.458
29	shufflenet_v2_x1_0	.455
30	vith14_supervised_linhead	.438
31	deit_base_imagenet_full_seed-0	.436
32	resnet_50_v1	.456
33	resnet_50_v1_spiking	.456
34	resnet50-sup	.456
35	resnet50_tutorial	.456
36	vit_large_patch14_clip_224:laion2b_ft_in1k	.457
37	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.455
38	custom_model_cv_18_dagger_408	.452
39	AdvProp_efficientnet-b8	.454
40	pnasnet_large_halfPrecision	.454
41	dinov2_vitg14_reg_lc	.454
42	convnext_xxlarge_clip	.452
43	dinov2_vitl14_reg_lc_featread	.451
44	dinov2_vitl14_reg_lc	.451
45	dinov2_vitg14_lc	.450
46	AdvProp_efficientnet-b6	.449
47	densenet-121	.449
48	ijepa_vith14_linhead	.433
49	efficientnet_b0_imagenet_full	.428
50	focalnet_tiny_lrf_in1k	.448
51	resnext101_32x48d_wsl	.445
52	seresnext50_32x4d	.443
53	AdvProp_efficientnet-b7	.442
54	resnet50_robust_l2_eps1	.442
55	efficientnet_b1_imagenet_full	.422
56	cvt_cvt-13-224-in1k_4_LucyV4	.442
57	resnet50_ijepacnn_linhead	.425
58	resnext101_32x16d_wsl	.439
59	cvt_cvt-21-384-in1k_4_LucyV4	.440
60	ReAlnet01_cornet_fov16	.421
61	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.438
62	convnext_small_imagenet_full_seed-0	.434
63	cvt_cvt-21-224-in1k_4_LucyV4	.435
64	resnet50-moclr8deg	.433
65	resnet50_cpc_linhead	.417
66	convnext_xlarge:fb_in22k_ft_in1k	.432
67	mobilenet_v2_1_4_224	.433
68	resnet50-VITO-8deg-cc	.434
69	resnet50-vitoimagevidnet8	.434
70	resnet-50-robust	.434
71	resnet50_supervised_linhead	.415
72	efficientnet_b0	.432
73	convnext_tiny_sup	.431
74	ReAlnet01_cornet_fov12	.412
75	Res2Net50_26w_4s	.429
76	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.425
77	effnetb1_272x240	.427
78	persistence_memory	.427
79	convnext_small_imagenet_100_seed-0	.407
80	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.427
81	deit_large_imagenet_full_seed-0	.408
82	resnet50-SIN_IN	.425
83	resnet50_simsiam_linhead	.408
84	convnext_large:fb_in22k_ft_in1k	.423
85	convnext_base_imagenet_full_seed-0	.422
86	AdvProp_efficientnet-b4	.423
87	resnet50_ewconplus_wzc	.422
88	resnext101_32x8d_wsl	.422
89	deit_small_imagenet_full_seed-0	.403
90	mehrer2020_alexnet_training_seed_03	.402
91	resnext101_32x32d_wsl	.420
92	mehrer2020_alexnet_training_seed_04	.401
93	AT_efficientnet-b2	.418
94	AdvProp_efficientnet-b2	.418
95	mehrer2020_alexnet_training_seed_05	.398
96	resnet50_barlowtwins_linhead	.398
97	mehrer2020_alexnet_training_seed_02	.398
98	ReAlnet10_cornet	.399
99	ReAlnet08_cornet	.398
100	ReAlnet07_cornet	.397
101	mehrer2020_alexnet_training_seed_06	.396
102	mehrer2020_alexnet_training_seed_07	.396
103	resnet50_simclr_linhead_01norm	.396
104	ReAlnet05_cornet	.396
105	ReAlnet06_cornet	.395
106	ReAlnet02_cornet	.395
107	ReAlnet03_cornet	.411
108	AlexNet_SIN_fov16	.409
109	mehrer2020_alexnet_training_seed_08	.394
110	mehrer2020_alexnet_training_seed_01	.394
111	mehrer2020_alexnet_training_seed_10	.393
112	mehrer2020_alexnet_training_seed_09	.393
113	ReAlnet04_cornet	.411
114	ReAlnet09_cornet	.394
115	ReAlnet01_cornet	.410
116	resnet152_imagenet_full	.406
117	mehrer2020_alexnet_training_seed_01_fov12	.385
118	AlexNet_SIN	.402
119	resnet18_imagenet_full	.386
120	resnet_50_v2	.403
121	alexnet	.402
122	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.400
123	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.400
124	resnet101_ecoset_full	.384
125	resnet34_imagenet_full	.383
126	resnet101_imagenet_full	.386
127	resnet50_ecoset_full	.384
128	compact_resnet50_robust_V4	.399
129	resnet34_ecoset_full	.381
130	regnety_032	.394
131	mehrer2020_alexnet_training_seed_01_fov16	.376
132	AlexNet_SIN_fov12	.393
133	deit_small_imagenet_100_seed-0	.380
134	persistence_mem	.393
135	resnet152_ecoset_full	.375
136	gabor_filter_entropy_4x4_v2_forcedCosine	.374
137	resnet50_byol_linhead	.360
138	gabor_filter_entropy_4x4_v2	.369
139	convnext_large_imagenet_full_seed-0	.366
140	eicircuit_resnet18_bn_20260919	.350
141	resnet50_imagenet_100_seed-0	.348
142	resnet50_imagenet_full	.362
143	convnext_tiny:in12k_ft_in1k	.358
144	ViT_L_32_imagenet1k	.355
145	resnet50_vicreg_linhead	.337
146	convnext_tiny_imagenet_full_seed-0	.353
147	resnet18_ecoset_full	.335
148	gabor_filter_entropy_4x4	.326
149	vonealexnet	.323
150	alexnet_training_seed_02	.316
151	resnet50_imagenet_10_seed-0	.302
152	alexnet_training_seed_01	.314
153	alexnet_training_seed_09	.312
154	alexnet_training_seed_10	.308
155	alexnet_training_seed_06	.306
156	alexnet_training_seed_07	.305
157	alexnet_training_seed_03	.275
158	gabor_filter_mean_abs_4x4_v2	.256
159	resnet50_untrained_linhead	.248
160	AlexNet_SIN_fov4	.242
161	mehrer2020_alexnet_training_seed_01_fov4	.240
162	ReAlnet01_cornet_fov4	.245
163	voneresnet-50-non_stochastic	.236
164	VOneCORnet-S	.155
165	hmax	.125
166	vith14_untrained_linhead	.111
167	resnet50_imagenet_1_seed-0	.071
168	convnext_small_imagenet_10_seed-0	.068
169	deit_small_imagenet_10_seed-0	.062
170	convnext_small_imagenet_1_seed-0	.058
171	deit_small_imagenet_1_seed-0	.057
172	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

0.81.

Note that scores are relative to this ceiling.

Data: Zerbe2026_fmri.V1-tau

Metric: ridgecv