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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	resnet50_simclr_linhead_01norm	.392
19	ReAlnet08_cornet	.392
20	ReAlnet04_cornet	.391
21	ReAlnet02_cornet	.391
22	custom_model_cv_18_dagger_408	.390
23	resnet50_ecoset_full	.390
24	ReAlnet03_cornet	.389
25	ReAlnet01_cornet	.389
26	densenet-121	.389
27	ReAlnet10_cornet	.386
28	ReAlnet07_cornet	.386
29	voneresnet-50-non_stochastic	.385
30	ReAlnet05_cornet	.385
31	ReAlnet09_cornet	.383
32	eicircuit_resnet18_bn_20260919	.382
33	ReAlnet06_cornet	.381
34	effnetb1_272x240	.381
35	resnet50_ewconplus_wzc	.379
36	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.379
37	deit_small_imagenet_full_seed-0	.377
38	resnet152_ecoset_full	.377
39	resnet101_ecoset_full	.375
40	alexnet	.375
41	resnet50-SIN_IN	.374
42	convnext_base:clip_laiona_augreg_ft_in1k_384	.371
43	resnet50-SIN_IN_IN	.370
44	resnet50-VITO-8deg-cc	.368
45	resnet50-vitoimagevidnet8	.368
46	resnet101_imagenet_full	.368
47	resnet18_ecoset_full	.367
48	resnet50_robust_l2_eps1	.367
49	antialiased-r50	.366
50	voneresnet-50	.362
51	resnet_50_v1_spiking	.361
52	resnet_50_v1	.361
53	resnet50-sup	.361
54	ReAlnet01_cornet_fov4	.357
55	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.355
56	efficientnet_b0	.350
57	resnet152_imagenet_full	.348
58	resnet18_imagenet_full	.347
59	resnet_SIN_IN_FT_IN	.346
60	dinov2_vitl14_lc	.345
61	resnet_50_v2	.344
62	convnext_small_imagenet_full_seed-0	.345
63	resnet34_ecoset_full	.344
64	vit_base_patch16_clip_224:openai_ft_in1k	.344
65	convnext_tiny_imagenet_full_seed-0	.344
66	vit_large_patch14_clip_224:laion2b_ft_in1k	.340
67	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.339
68	Res2Net50_26w_4s	.336
69	mehrer2020_alexnet_training_seed_01_fov4	.335
70	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.334
71	ReAlnet01_cornet_fov12	.328
72	resnet50_simsiam_linhead	.326
73	mobilenet_v2_1_4_224	.322
74	pnasnet_large_halfPrecision	.320
75	vit_relpos_base_patch16_clsgap_224:sw_in1k	.318
76	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.315
77	resnet50_byol_linhead	.310
78	ReAlnet01_cornet_fov16	.305
79	AlexNet_SIN	.304
80	cvt_cvt-13-224-in1k_4_LucyV4	.301
81	AdvProp_efficientnet-b2	.301
82	AT_efficientnet-b2	.301
83	vit_large_patch14_clip_336:laion2b_ft_in1k	.300
84	resnet50-SIN	.297
85	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.297
86	resnet34_imagenet_full	.294
87	resnet50_tutorial	.294
88	resnet50_imagenet_100_seed-0	.292
89	mehrer2020_alexnet_training_seed_02	.289
90	seresnext50_32x4d	.287
91	mehrer2020_alexnet_training_seed_03	.287
92	mehrer2020_alexnet_training_seed_05	.285
93	mehrer2020_alexnet_training_seed_07	.283
94	mehrer2020_alexnet_training_seed_06	.281
95	convnext_xlarge:fb_in22k_ft_in1k	.277
96	mehrer2020_alexnet_training_seed_08	.276
97	mehrer2020_alexnet_training_seed_01	.275
98	AdvProp_efficientnet-b7	.274
99	efficientnet_b1_imagenet_full	.274
100	AdvProp_efficientnet-b8	.274
101	mehrer2020_alexnet_training_seed_04	.273
102	mehrer2020_alexnet_training_seed_10	.273
103	efficientnet_b0_imagenet_full	.273
104	mehrer2020_alexnet_training_seed_09	.272
105	cvt_cvt-21-224-in1k_4_LucyV4	.270
106	AdvProp_efficientnet-b4	.268
107	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.267
108	convnext_tiny_sup	.265
109	AdvProp_efficientnet-b6	.261
110	cvt_cvt-21-384-in1k_4_LucyV4	.260
111	resnet50_vicreg_linhead	.255
112	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.254
113	resnet50_barlowtwins_linhead	.252
114	resnet-50-robust	.245
115	AlexNet_SIN_fov4	.236
116	grcnn	.236
117	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.234
118	cvt_cvt-13-384-in1k_4_LucyV4	.222
119	vit_large_clip336_openai	.216
120	dinov2_vitg14_lc	.208
121	convnext_small_imagenet_100_seed-0	.208
122	mehrer2020_alexnet_training_seed_01_fov12	.206
123	dinov2_vitl14_reg_lc_featread	.201
124	dinov2_vitl14_reg_lc	.201
125	ViT_L_32_imagenet1k	.199
126	alexnet_fov12	.198
127	efficientnet_b2_imagenet_full	.193
128	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.184
129	resnext101_32x16d_wsl	.174
130	ijepa_vith14_linhead	.173
131	focalnet_tiny_lrf_in1k	.168
132	regnety_032	.166
133	convnext_xxlarge_clip	.157
134	dinov2_vitg14_reg_lc	.156
135	resnext101_32x48d_wsl	.154
136	deit_small_imagenet_100_seed-0	.146
137	resnext101_32x8d_wsl	.139
138	mehrer2020_alexnet_training_seed_01_fov16	.138
139	AlexNet_SIN_fov12	.136
140	resnext101_32x32d_wsl	.129
141	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.118
142	AlexNet_SIN_fov16	.097
143	hmax	.085
144	gabor_filter_mean_abs_4x4_v2	.060
145	gabor_filter_entropy_4x4_v2	.054
146	gabor_filter_entropy_4x4	.050
147	alexnet_training_seed_06	.049
148	alexnet_training_seed_01	.042
149	alexnet_training_seed_02	.041
150	alexnet_training_seed_03	.041
151	alexnet_training_seed_07	.039
152	compact_resnet50_robust_V4	.038
153	alexnet_training_seed_09	.038
154	alexnet_training_seed_10	.035
155	gabor_filter_entropy_4x4_v2_forcedCosine	.028
156	vit_huge_clip336_laion
157	dinov2_vitl14_lc_res336
158	eva02_large_in22k

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