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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Hebart2023_fmri.V1-ridgecv")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_large_clip336_openai	.646
2	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.646
3	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.645
4	cvt_cvt-13-384-in1k_4_LucyV4	.642
5	vit_large_patch14_clip_224:laion2b_ft_in1k	.642
6	resnet_SIN_IN_FT_IN	.642
7	resnet-50-robust	.639
8	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.637
9	vit_large_patch14_clip_224:openai_ft_in1k	.636
10	resnet50-SIN	.633
11	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.632
12	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.633
13	vit_huge_clip336_laion	.632
14	AlexNet_SIN_fov12	.634
15	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.630
16	convnext_large_in22k_supervised	.629
17	ReAlnet01_cornet_fov16	.631
18	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.621
19	resnet50_robust_l2_eps1	.619
20	voneresnet-50	.617
21	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.617
22	convnext_femto_ols:d1_in1k	.617
23	deit_base_imagenet_full_seed-0	.617
24	vit_base_patch16_clip_224:openai_ft_in1k	.615
25	dinov2_vitl14_lc_res336	.614
26	resnet_50_v1	.611
27	resnet50-sup	.611
28	resnet50_tutorial	.611
29	resnet_50_v1_spiking	.611
30	dinov2_vitl14_lc	.611
31	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.612
32	convnext_xxlarge_clip	.611
33	mehrer2020_alexnet_training_seed_01_fov16	.611
34	convnext_base:clip_laiona_augreg_ft_in1k_384	.610
35	resnet50-SIN_IN_IN	.608
36	antialiased-r50	.607
37	AlexNet_SIN	.610
38	AdvProp_efficientnet-b8	.606
39	shufflenet_v2_x1_0	.607
40	AdvProp_efficientnet-b7	.604
41	efficientnet_b0_imagenet_full	.606
42	ReAlnet01_cornet_fov12	.606
43	efficientnet_b2_imagenet_full	.603
44	vit_large_patch14_clip_336:laion2b_ft_in1k	.603
45	dinov2_vitg14_lc	.603
46	dinov2_vitg14_reg_lc	.603
47	seresnext50_32x4d	.599
48	resnext101_32x16d_wsl	.599
49	vit_relpos_base_patch16_clsgap_224:sw_in1k	.600
50	convnext_large_clip_laion2b_finelayers	.599
51	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.600
52	ijepa_vith14_linhead	.596
53	AlexNet_SIN_fov16	.599
54	mehrer2020_alexnet_training_seed_09	.598
55	resnext101_32x48d_wsl	.593
56	AdvProp_efficientnet-b6	.591
57	mehrer2020_alexnet_training_seed_01_fov12	.594
58	mehrer2020_alexnet_training_seed_06	.595
59	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.592
60	resnet50-vitoimagevidnet8	.593
61	resnet50-VITO-8deg-cc	.593
62	mehrer2020_alexnet_training_seed_04	.594
63	efficientnet_b1_imagenet_full	.591
64	grcnn	.590
65	dinov2_vitl14_reg_lc_featread	.590
66	dinov2_vitl14_reg_lc	.590
67	focalnet_tiny_lrf_in1k	.590
68	convnext_xlarge:fb_in22k_ft_in1k	.590
69	mehrer2020_alexnet_training_seed_08	.593
70	convnext_large:fb_in22k_ft_in1k	.589
71	resnet50-moclr8deg	.587
72	mehrer2020_alexnet_training_seed_07	.591
73	AdvProp_efficientnet-b4	.587
74	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.588
75	deit3_base_patch16_224_fb_in22k_ft_in1k	.588
76	custom_model_cv_18_dagger_408	.587
77	mehrer2020_alexnet_training_seed_10	.587
78	mehrer2020_alexnet_training_seed_02	.585
79	mehrer2020_alexnet_training_seed_03	.585
80	deit3_base_patch16_224_fb_in1k	.585
81	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.585
82	convnext_base_fb_in22k_ft_in1k	.580
83	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.580
84	cvt_cvt-21-384-in1k_4_LucyV4	.581
85	yudixie_wbh_object_class_scaling_0415_imagenet	.579
86	mehrer2020_alexnet_training_seed_05	.580
87	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.577
88	Res2Net50_26w_4s	.577
89	cvt_cvt-13-224-in1k_4_LucyV4	.576
90	convnext_tiny_sup	.575
91	mehrer2020_alexnet_training_seed_01	.576
92	deepeisnn_ei_imagenet_resnet18	.573
93	densenet-121	.572
94	resnet50_byol_linhead	.573
95	resnet50_barlowtwins_linhead	.572
96	ReAlnet10_cornet	.572
97	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.573
98	compact_resnet50_robust_V4	.569
99	resnet50_simclr_linhead_01norm	.568
100	ReAlnet07_cornet	.569
101	resnext101_32x8d_wsl	.567
102	resnet50_ewconplus_wzc	.568
103	ReAlnet08_cornet	.569
104	deit_large_imagenet_full_seed-0	.567
105	ReAlnet03_cornet	.566
106	resnet50_simsiam_linhead	.564
107	resnext101_32x32d_wsl	.564
108	ReAlnet02_cornet	.565
109	deit_small_imagenet_full_seed-0	.566
110	ReAlnet05_cornet	.564
111	cvt_cvt-21-224-in1k_4_LucyV4	.562
112	resnet50-SIN_IN	.561
113	convnext_small_imagenet_full_seed-0	.560
114	ReAlnet01_cornet	.560
115	ReAlnet04_cornet	.559
116	mobilenet_v2_1_4_224	.555
117	efficientnet_b0	.554
118	regnety_032	.555
119	ReAlnet09_cornet	.556
120	AT_efficientnet-b2	.553
121	AdvProp_efficientnet-b2	.553
122	deepeisnn_ei_cifar10_resnet18	.553
123	deit_small_imagenet_100_seed-0	.550
124	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.544
125	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.544
126	resnet50_ecoset_full	.543
127	resnet101_ecoset_full	.541
128	resnet152_imagenet_full	.541
129	convnext_base_imagenet_full_seed-0	.535
130	resnet34_imagenet_full	.530
131	resnet101_imagenet_full	.529
132	resnet18_imagenet_full	.528
133	deepeisnn_ei_cifar10_vgg19	.526
134	resnet_50_v2	.525
135	resnet152_ecoset_full	.524
136	alexnet_training_seed_02	.522
137	alexnet_training_seed_09	.521
138	gabor_filter_entropy_4x4_v2_forcedCosine	.517
139	alexnet_training_seed_01	.518
140	gabor_filter_entropy_4x4_v2	.514
141	alexnet_training_seed_07	.516
142	eicircuit_resnet18_bn_20260919	.515
143	resnet34_ecoset_full	.509
144	ViT_L_32_imagenet1k	.504
145	resnet50_imagenet_100_seed-0	.503
146	alexnet_training_seed_10	.500
147	resnet50_imagenet_full	.499
148	gabor_filter_entropy_4x4	.488
149	alexnet_training_seed_06	.485
150	resnet18_ecoset_full	.481
151	yudixie_wbh_object_class_scaling_0415_run_0	.473
152	yudixie_wbh_object_class_scaling_0415_run_2	.469
153	convnext_tiny:in12k_ft_in1k	.469
154	effnetb1_272x240	.458
155	convnext_large_imagenet_full_seed-0	.460
156	convnext_tiny_imagenet_full_seed-0	.457
157	alexnet_training_seed_03	.452
158	yudixie_wbh_object_class_scaling_0415_run_1	.442
159	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.403
160	yudixie_wbh_object_class_scaling_0415_run_3	.394
161	resnet50_vicreg_linhead	.378
162	AlexNet_SIN_fov4	.349
163	pr1_stl10_disentangled_alpha04	.348
164	predictive_coding_pc	.343
165	mehrer2020_alexnet_training_seed_01_fov4	.324
166	ReAlnet01_cornet_fov4	.291
167	convnext_base_fb_in1k	.257
168	gabor_filter_mean_abs_4x4_v2	.258
169	convnext_base_fb_in1k_gram_aligned_dinov3	.234
170	hmax	.212
171	convnext_small_imagenet_100_seed-0
172	ReAlnet06_cornet
173	voneresnet-50-non_stochastic
174	VOneCORnet-S
175	eva02_large_in22k
176	alexnet
177	resnet18_fair
178	resnet50_st_wzc_seed3_2_323
179	resnet50_ewconplus_wzc_seed5_2__323
180	resnet50_ewconplus_wzc_seed6_2_323
181	resnet50_st_wzc_seed7_2_323
182	resnet50_st_wzc_seed6_2_323
183	resnet50_ewconplus_wzc_seed2_2__323
184	pnasnet_large_halfPrecision

Benchmark bibtex

None

Ceiling

0.54.

Note that scores are relative to this ceiling.

Data: Hebart2023_fmri.V1

Metric: ridgecv