Leaderboard Explore Compare Profile Community Blog FAQ Competition 2024 Report Issue

Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Allen2022_fmri_surface.V1-ridge")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_huge_clip336_laion	.558
2	vit_large_clip336_openai	.552
3	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.542
4	vit_large_patch14_clip_224:openai_ft_in1k	.541
5	cvt_cvt-13-384-in1k_4_LucyV4	.540
6	vit_relpos_base_patch16_clsgap_224:sw_in1k	.536
7	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.531
8	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.528
9	regnety_032	.523
10	seresnext50_32x4d	.521
11	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.521
12	vith14_supervised_linhead	.521
13	eva02_large_in22k	.518
14	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.516
15	resnet50-SIN_IN_IN	.515
16	antialiased-r50	.513
17	resnet_50_v1_spiking	.510
18	resnet_50_v1	.510
19	resnet50-sup	.510
20	resnet50_tutorial	.510
21	AdvProp_efficientnet-b7	.510
22	resnext101_32x48d_wsl	.507
23	resnext101_32x16d_wsl	.506
24	resnet_SIN_IN_FT_IN	.504
25	vit_large_patch14_clip_336:laion2b_ft_in1k	.504
26	convnext_xxlarge_clip	.504
27	dinov2_vitl14_lc	.504
28	resnet50-SIN	.501
29	dinov2_vitg14_reg_lc	.502
30	efficientnet_b1_imagenet_full	.496
31	voneresnet-50	.497
32	convnext_base:clip_laiona_augreg_ft_in1k_384	.494
33	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.493
34	dinov2_vitl14_lc_res336	.494
35	efficientnet_b2_imagenet_full	.492
36	AdvProp_efficientnet-b8	.493
37	focalnet_tiny_lrf_in1k	.489
38	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.489
39	grcnn	.488
40	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.485
41	shufflenet_v2_x1_0	.484
42	resnet50_robust_l2_eps1	.481
43	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.481
44	pnasnet_large_halfPrecision	.481
45	custom_model_cv_18_dagger_408	.480
46	vit_large_patch14_clip_224:laion2b_ft_in1k	.480
47	ijepa_vith14_linhead	.479
48	dinov2_vitl14_reg_lc_featread	.479
49	dinov2_vitl14_reg_lc	.479
50	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.480
51	convnext_xlarge:fb_in22k_ft_in1k	.478
52	AdvProp_efficientnet-b6	.478
53	resnet50_ijepacnn_linhead	.477
54	efficientnet_b0	.477
55	deit_base_imagenet_full_seed-0	.477
56	resnet50_supervised_linhead	.476
57	resnet50-moclr8deg	.475
58	convnext_large:fb_in22k_ft_in1k	.473
59	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.474
60	cvt_cvt-13-224-in1k_4_LucyV4	.472
61	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.469
62	mobilenet_v2_1_4_224	.466
63	cvt_cvt-21-224-in1k_4_LucyV4	.462
64	resnet50_byol_linhead	.458
65	resnet50_cpc_linhead	.458
66	convnext_tiny_sup	.456
67	resnet50_simsiam_linhead	.456
68	vit_base_patch16_clip_224:openai_ft_in1k	.459
69	resnet50-VITO-8deg-cc	.455
70	resnet50-vitoimagevidnet8	.455
71	resnet50_vicreg_linhead	.454
72	Res2Net50_26w_4s	.454
73	cvt_cvt-21-384-in1k_4_LucyV4	.455
74	resnext101_32x32d_wsl	.453
75	resnet50_simclr_linhead_01norm	.452
76	resnext101_32x8d_wsl	.452
77	resnet50_barlowtwins_linhead	.452
78	AdvProp_efficientnet-b4	.451
79	AdvProp_efficientnet-b2	.448
80	AT_efficientnet-b2	.448
81	efficientnet_b0_imagenet_full	.447
82	effnetb1_272x240	.448
83	convnext_base_imagenet_full_seed-0	.447
84	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.446
85	convnext_femto_ols:d1_in1k	.446
86	resnet50-SIN_IN	.445
87	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.444
88	resnet50_ewconplus_wzc	.443
89	persistence_memory	.443
90	convnext_small_imagenet_full_seed-0	.441
91	densenet-121	.434
92	ReAlnet10_cornet	.435
93	ReAlnet01_cornet_fov12	.434
94	ReAlnet07_cornet	.434
95	ReAlnet08_cornet	.433
96	mehrer2020_alexnet_training_seed_05	.429
97	ReAlnet05_cornet	.431
98	ReAlnet03_cornet	.431
99	ReAlnet06_cornet	.430
100	mehrer2020_alexnet_training_seed_10	.429
101	ReAlnet02_cornet	.430
102	ReAlnet01_cornet	.430
103	ReAlnet04_cornet	.430
104	mehrer2020_alexnet_training_seed_04	.426
105	mehrer2020_alexnet_training_seed_03	.426
106	ReAlnet09_cornet	.427
107	deit_small_imagenet_full_seed-0	.426
108	mehrer2020_alexnet_training_seed_01	.423
109	deit_large_imagenet_full_seed-0	.424
110	resnet_50_v2	.420
111	mehrer2020_alexnet_training_seed_08	.417
112	deit_small_imagenet_100_seed-0	.419
113	mehrer2020_alexnet_training_seed_02	.416
114	mehrer2020_alexnet_training_seed_06	.416
115	persistence_mem	.416
116	mehrer2020_alexnet_training_seed_07	.414
117	ReAlnet01_cornet_fov16	.414
118	mehrer2020_alexnet_training_seed_09	.413
119	resnet50_ewconplus_wzc_seed2_2__323	.415
120	resnet18_imagenet_full	.414
121	alexnet	.413
122	AlexNet_SIN	.412
123	resnet50_ewconplus_wzc_seed5_2__323	.413
124	resnet50_ewconplus_wzc_seed6_2_323	.412
125	resnet50_st_wzc_seed6_2_323	.406
126	resnet34_imagenet_full	.407
127	resnet50_st_wzc_seed3_2_323	.404
128	compact_resnet50_robust_V4	.404
129	resnet50_st_wzc_seed7_2_323	.403
130	resnet34_ecoset_full	.402
131	mehrer2020_alexnet_training_seed_01_fov12	.398
132	resnet101_imagenet_full	.399
133	convnext_small_imagenet_100_seed-0	.395
134	resnet152_imagenet_full	.395
135	resnet50_ecoset_full	.393
136	resnet101_ecoset_full	.392
137	resnet152_ecoset_full	.386
138	AlexNet_SIN_fov12	.379
139	resnet-50-robust	.376
140	mehrer2020_alexnet_training_seed_01_fov16	.372
141	resnet18_fair	.371
142	convnext_large_imagenet_full_seed-0	.372
143	convnext_tiny_imagenet_full_seed-0	.367
144	eicircuit_resnet18_bn_20260919	.357
145	ViT_L_32_imagenet1k	.348
146	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.345
147	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.345
148	convnext_tiny:in12k_ft_in1k	.346
149	AlexNet_SIN_fov16	.333
150	resnet50_imagenet_full	.330
151	resnet50_imagenet_100_seed-0	.329
152	vonealexnet	.324
153	resnet18_ecoset_full	.322
154	alexnet_training_seed_02	.296
155	alexnet_training_seed_01	.294
156	alexnet_training_seed_06	.282
157	alexnet_training_seed_07	.280
158	alexnet_training_seed_09	.279
159	alexnet_training_seed_10	.277
160	pr1_stl10_disentangled_alpha04	.258
161	resnet50_untrained_linhead	.240
162	resnet50_imagenet_10_seed-0	.239
163	VOneCORnet-S	.229
164	gabor_filter_entropy_4x4_v2_forcedCosine	.219
165	mehrer2020_alexnet_training_seed_01_fov4	.217
166	gabor_filter_entropy_4x4_v2	.217
167	voneresnet-50-non_stochastic	.210
168	ReAlnet01_cornet_fov4	.209
169	AlexNet_SIN_fov4	.202
170	alexnet_training_seed_03	.189
171	predictive_coding_pc	.178
172	gabor_filter_mean_abs_4x4_v2	.154
173	convnext_base_fb_in1k	.122
174	vith14_untrained_linhead	.107
175	hmax	.074
176	gabor_filter_entropy_4x4	.064
177	convnext_small_imagenet_10_seed-0	.055
178	resnet50_imagenet_1_seed-0	.055
179	convnext_small_imagenet_1_seed-0	.040
180	deit_small_imagenet_10_seed-0	.040
181	deit_small_imagenet_1_seed-0	.037
182	dinov2_vitg14_lc

Benchmark bibtex

@article{allen_massive_2022,
    title = {A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence},
    volume = {25},
    issn = {1097-6256},
    doi = {10.1038/s41593-021-00962-x},
    journal = {Nature Neuroscience},
    author = {Allen, Emily J. and St-Yves, Ghislain and Wu, Yihan and Breedlove, Jesse L.
              and Prince, Jacob S. and Dowdle, Logan T. and Nau, Matthias and Caron, Brad
              and Pestilli, Franco and Charest, Ian and Hutchinson, J. Benjamin
              and Naselaris, Thomas and Kay, Kendrick},
    year = {2022},
    pages = {116--126},
}

Ceiling

0.51.

Note that scores are relative to this ceiling.

Data: Allen2022_fmri_surface.V1

Metric: ridge