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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Zerbe2026_fmri.IT-rdm-pearson")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_tiny:in12k_ft_in1k	.463
2	convnext_large:fb_in22k_ft_in1k	.453
3	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.438
4	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.436
5	convnext_femto_ols:d1_in1k	.433
6	deit_large_imagenet_full_seed-0	.429
7	deit_base_imagenet_full_seed-0	.425
8	resnet50-moclr8deg	.421
9	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.414
10	convnext_base_imagenet_full_seed-0	.412
11	shufflenet_v2_x1_0	.409
12	vit_large_patch14_clip_224:openai_ft_in1k	.406
13	convnext_large_imagenet_full_seed-0	.402
14	resnet50_imagenet_full	.398
15	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.398
16	VOneCORnet-S	.397
17	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.395
18	ReAlnet04_cornet	.391
19	custom_model_cv_18_dagger_408	.390
20	ReAlnet03_cornet	.389
21	ReAlnet01_cornet	.389
22	densenet-121	.389
23	voneresnet-50-non_stochastic	.385
24	eicircuit_resnet18_bn_20260919	.382
25	effnetb1_272x240	.381
26	resnet50_ewconplus_wzc	.379
27	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.379
28	deit_small_imagenet_full_seed-0	.377
29	alexnet	.375
30	resnet50-SIN_IN	.374
31	convnext_base:clip_laiona_augreg_ft_in1k_384	.371
32	resnet50-SIN_IN_IN	.370
33	resnet50-VITO-8deg-cc	.368
34	resnet50-vitoimagevidnet8	.368
35	resnet50_robust_l2_eps1	.367
36	antialiased-r50	.366
37	voneresnet-50	.362
38	resnet_50_v1_spiking	.361
39	resnet_50_v1	.361
40	resnet50-sup	.361
41	ReAlnet01_cornet_fov4	.357
42	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.355
43	efficientnet_b0	.350
44	resnet152_imagenet_full	.348
45	resnet_SIN_IN_FT_IN	.346
46	dinov2_vitl14_lc	.345
47	resnet_50_v2	.344
48	convnext_small_imagenet_full_seed-0	.345
49	vit_base_patch16_clip_224:openai_ft_in1k	.344
50	convnext_tiny_imagenet_full_seed-0	.344
51	vit_large_patch14_clip_224:laion2b_ft_in1k	.340
52	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.339
53	Res2Net50_26w_4s	.336
54	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.334
55	mobilenet_v2_1_4_224	.322
56	pnasnet_large_halfPrecision	.320
57	vit_relpos_base_patch16_clsgap_224:sw_in1k	.318
58	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.315
59	AlexNet_SIN	.304
60	cvt_cvt-13-224-in1k_4_LucyV4	.301
61	AdvProp_efficientnet-b2	.301
62	AT_efficientnet-b2	.301
63	vit_large_patch14_clip_336:laion2b_ft_in1k	.300
64	resnet50-SIN	.297
65	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.297
66	resnet50_tutorial	.294
67	seresnext50_32x4d	.287
68	convnext_xlarge:fb_in22k_ft_in1k	.277
69	AdvProp_efficientnet-b7	.274
70	AdvProp_efficientnet-b8	.274
71	cvt_cvt-21-224-in1k_4_LucyV4	.270
72	AdvProp_efficientnet-b4	.268
73	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.267
74	convnext_tiny_sup	.265
75	AdvProp_efficientnet-b6	.261
76	cvt_cvt-21-384-in1k_4_LucyV4	.260
77	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.254
78	resnet-50-robust	.245
79	grcnn	.236
80	cvt_cvt-13-384-in1k_4_LucyV4	.222
81	vit_large_clip336_openai	.216
82	dinov2_vitg14_lc	.208
83	dinov2_vitl14_reg_lc_featread	.201
84	dinov2_vitl14_reg_lc	.201
85	ViT_L_32_imagenet1k	.199
86	alexnet_fov12	.198
87	efficientnet_b2_imagenet_full	.193
88	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.184
89	resnext101_32x16d_wsl	.174
90	focalnet_tiny_lrf_in1k	.168
91	regnety_032	.166
92	convnext_xxlarge_clip	.157
93	dinov2_vitg14_reg_lc	.156
94	resnext101_32x48d_wsl	.154
95	resnext101_32x8d_wsl	.139
96	AlexNet_SIN_fov12	.136
97	resnext101_32x32d_wsl	.129
98	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.118
99	AlexNet_SIN_fov16	.097
100	hmax	.085
101	gabor_filter_mean_abs_4x4_v2	.060
102	gabor_filter_entropy_4x4_v2	.054
103	gabor_filter_entropy_4x4	.050
104	alexnet_training_seed_06	.049
105	alexnet_training_seed_01	.042
106	alexnet_training_seed_02	.041
107	alexnet_training_seed_03	.041
108	alexnet_training_seed_07	.039
109	compact_resnet50_robust_V4	.038
110	alexnet_training_seed_09	.038
111	alexnet_training_seed_10	.035
112	gabor_filter_entropy_4x4_v2_forcedCosine	.028
113	vit_huge_clip336_laion
114	dinov2_vitl14_lc_res336
115	eva02_large_in22k
116	cvt_cvt-w24-384-in22k_finetuned-in1k_4

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

0.75.

Note that scores are relative to this ceiling.

Data: Zerbe2026_fmri.IT-rdm

Metric: pearson