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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Zerbe2026_fmri.IT-tau-ridgecv")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnext101_32x48d_wsl	.348
2	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.347
3	resnext101_32x32d_wsl	.342
4	resnext101_32x16d_wsl	.336
5	vit_large_patch14_clip_336:laion2b_ft_in1k	.334
6	convnext_large:fb_in22k_ft_in1k	.328
7	deit_large_imagenet_full_seed-0	.329
8	vit_relpos_base_patch16_clsgap_224:sw_in1k	.324
9	eva02_large_in22k	.323
10	convnext_base:clip_laiona_augreg_ft_in1k_384	.323
11	resnet50-moclr8deg	.322
12	pnasnet_large_halfPrecision	.321
13	densenet-121	.319
14	vit_base_patch16_clip_224:openai_ft_in1k	.319
15	deit_small_imagenet_full_seed-0	.322
16	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.317
17	convnext_femto_ols:d1_in1k	.317
18	resnet_50_v2	.316
19	resnet152_imagenet_full	.316
20	convnext_xxlarge_clip	.314
21	convnext_tiny:in12k_ft_in1k	.313
22	focalnet_tiny_lrf_in1k	.313
23	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.312
24	ReAlnet01_cornet	.312
25	ReAlnet04_cornet	.310
26	convnext_large_imagenet_full_seed-0	.309
27	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.308
28	ReAlnet03_cornet	.308
29	seresnext50_32x4d	.312
30	resnet_50_v1_spiking	.307
31	resnet_50_v1	.307
32	resnet50-sup	.307
33	convnext_base_imagenet_full_seed-0	.306
34	deit_base_imagenet_full_seed-0	.310
35	Res2Net50_26w_4s	.305
36	regnety_032	.308
37	dinov2_vitl14_lc	.303
38	antialiased-r50	.303
39	dinov2_vitg14_reg_lc	.303
40	convnext_tiny_sup	.302
41	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.302
42	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.301
43	shufflenet_v2_x1_0	.301
44	resnet50-SIN_IN_IN	.300
45	dinov2_vitl14_lc_res336	.300
46	resnet50-SIN_IN	.300
47	VOneCORnet-S	.299
48	voneresnet-50-non_stochastic	.297
49	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.296
50	effnetb1_272x240	.295
51	vit_large_patch14_clip_224:openai_ft_in1k	.294
52	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.293
53	resnet50_tutorial	.293
54	custom_model_cv_18_dagger_408	.293
55	resnet50_imagenet_full	.292
56	resnet_SIN_IN_FT_IN	.290
57	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.290
58	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.289
59	efficientnet_b2_imagenet_full	.288
60	resnet50_ewconplus_wzc	.288
61	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.286
62	voneresnet-50	.286
63	efficientnet_b0	.285
64	resnet50-VITO-8deg-cc	.283
65	resnet50-vitoimagevidnet8	.283
66	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.283
67	resnet50_robust_l2_eps1	.278
68	vit_large_patch14_clip_224:laion2b_ft_in1k	.276
69	convnext_small_imagenet_full_seed-0	.274
70	mobilenet_v2_1_4_224	.273
71	dinov2_vitl14_reg_lc_featread	.272
72	dinov2_vitl14_reg_lc	.272
73	alexnet	.272
74	convnext_tiny_imagenet_full_seed-0	.262
75	ReAlnet01_cornet_fov4	.257
76	vit_huge_clip336_laion	.257
77	AdvProp_efficientnet-b2	.256
78	AT_efficientnet-b2	.256
79	resnet50-SIN	.255
80	cvt_cvt-13-224-in1k_4_LucyV4	.250
81	convnext_xlarge:fb_in22k_ft_in1k	.249
82	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.248
83	resnext101_32x8d_wsl	.246
84	AdvProp_efficientnet-b6	.246
85	AdvProp_efficientnet-b4	.244
86	cvt_cvt-13-384-in1k_4_LucyV4	.241
87	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.240
88	AdvProp_efficientnet-b7	.239
89	cvt_cvt-21-384-in1k_4_LucyV4	.234
90	vit_large_clip336_openai	.228
91	AdvProp_efficientnet-b8	.227
92	ViT_L_32_imagenet1k	.226
93	cvt_cvt-21-224-in1k_4_LucyV4	.221
94	resnet-50-robust	.221
95	dinov2_vitg14_lc	.221
96	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.216
97	grcnn	.214
98	AlexNet_SIN	.204
99	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.203
100	AlexNet_SIN_fov12	.169
101	AlexNet_SIN_fov16	.136
102	gabor_filter_mean_abs_4x4_v2	.054
103	gabor_filter_entropy_4x4	.048
104	alexnet_training_seed_03	.047
105	hmax	.046
106	alexnet_training_seed_06	.045
107	alexnet_training_seed_02	.045
108	alexnet_training_seed_01	.044
109	alexnet_training_seed_07	.044
110	gabor_filter_entropy_4x4_v2_forcedCosine	.044
111	gabor_filter_entropy_4x4_v2	.043
112	alexnet_training_seed_10	.042
113	alexnet_training_seed_09	.035
114	compact_resnet50_robust_V4	.021
115	eicircuit_resnet18_bn_20260919

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

0.76.

Note that scores are relative to this ceiling.

Data: Zerbe2026_fmri.IT-tau

Metric: ridgecv