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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	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.621
18	resnet50_robust_l2_eps1	.619
19	voneresnet-50	.617
20	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.617
21	convnext_femto_ols:d1_in1k	.617
22	deit_base_imagenet_full_seed-0	.617
23	vit_base_patch16_clip_224:openai_ft_in1k	.615
24	dinov2_vitl14_lc_res336	.614
25	resnet_50_v1	.611
26	resnet50-sup	.611
27	resnet50_tutorial	.611
28	resnet_50_v1_spiking	.611
29	dinov2_vitl14_lc	.611
30	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.612
31	convnext_xxlarge_clip	.611
32	convnext_base:clip_laiona_augreg_ft_in1k_384	.610
33	resnet50-SIN_IN_IN	.608
34	antialiased-r50	.607
35	AlexNet_SIN	.610
36	AdvProp_efficientnet-b8	.606
37	shufflenet_v2_x1_0	.607
38	AdvProp_efficientnet-b7	.604
39	efficientnet_b2_imagenet_full	.603
40	vit_large_patch14_clip_336:laion2b_ft_in1k	.603
41	dinov2_vitg14_lc	.603
42	dinov2_vitg14_reg_lc	.603
43	resnext101_32x16d_wsl	.599
44	vit_relpos_base_patch16_clsgap_224:sw_in1k	.600
45	convnext_large_clip_laion2b_finelayers	.599
46	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.600
47	AlexNet_SIN_fov16	.599
48	resnext101_32x48d_wsl	.593
49	AdvProp_efficientnet-b6	.591
50	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.592
51	resnet50-vitoimagevidnet8	.593
52	resnet50-VITO-8deg-cc	.593
53	grcnn	.590
54	dinov2_vitl14_reg_lc_featread	.590
55	dinov2_vitl14_reg_lc	.590
56	focalnet_tiny_lrf_in1k	.590
57	convnext_xlarge:fb_in22k_ft_in1k	.590
58	convnext_large:fb_in22k_ft_in1k	.589
59	resnet50-moclr8deg	.587
60	AdvProp_efficientnet-b4	.587
61	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.588
62	deit3_base_patch16_224_fb_in22k_ft_in1k	.588
63	custom_model_cv_18_dagger_408	.587
64	deit3_base_patch16_224_fb_in1k	.585
65	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.585
66	convnext_base_fb_in22k_ft_in1k	.580
67	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.580
68	cvt_cvt-21-384-in1k_4_LucyV4	.581
69	yudixie_wbh_object_class_scaling_0415_imagenet	.579
70	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.577
71	Res2Net50_26w_4s	.577
72	cvt_cvt-13-224-in1k_4_LucyV4	.576
73	convnext_tiny_sup	.575
74	deepeisnn_ei_imagenet_resnet18	.573
75	densenet-121	.572
76	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.573
77	compact_resnet50_robust_V4	.569
78	resnext101_32x8d_wsl	.567
79	resnet50_ewconplus_wzc	.568
80	deit_large_imagenet_full_seed-0	.567
81	ReAlnet03_cornet	.566
82	resnext101_32x32d_wsl	.564
83	deit_small_imagenet_full_seed-0	.566
84	cvt_cvt-21-224-in1k_4_LucyV4	.562
85	resnet50-SIN_IN	.561
86	convnext_small_imagenet_full_seed-0	.560
87	ReAlnet01_cornet	.560
88	ReAlnet04_cornet	.559
89	mobilenet_v2_1_4_224	.555
90	efficientnet_b0	.554
91	AT_efficientnet-b2	.553
92	AdvProp_efficientnet-b2	.553
93	deepeisnn_ei_cifar10_resnet18	.553
94	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.544
95	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.544
96	resnet152_imagenet_full	.541
97	convnext_base_imagenet_full_seed-0	.535
98	deepeisnn_ei_cifar10_vgg19	.526
99	resnet_50_v2	.525
100	alexnet_training_seed_02	.522
101	alexnet_training_seed_09	.521
102	alexnet_training_seed_01	.518
103	alexnet_training_seed_07	.516
104	ViT_L_32_imagenet1k	.504
105	alexnet_training_seed_10	.500
106	resnet50_imagenet_full	.499
107	alexnet_training_seed_06	.485
108	yudixie_wbh_object_class_scaling_0415_run_0	.473
109	yudixie_wbh_object_class_scaling_0415_run_2	.469
110	convnext_tiny:in12k_ft_in1k	.469
111	effnetb1_272x240	.458
112	convnext_large_imagenet_full_seed-0	.460
113	convnext_tiny_imagenet_full_seed-0	.457
114	alexnet_training_seed_03	.452
115	yudixie_wbh_object_class_scaling_0415_run_1	.442
116	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.403
117	yudixie_wbh_object_class_scaling_0415_run_3	.394
118	pr1_stl10_disentangled_alpha04	.348
119	predictive_coding_pc	.343
120	ReAlnet01_cornet_fov4	.291
121	convnext_base_fb_in1k	.257
122	convnext_base_fb_in1k_gram_aligned_dinov3	.234
123	hmax	.212
124	eva02_large_in22k
125	alexnet
126	resnet18_fair
127	resnet50_st_wzc_seed3_2_323
128	resnet50_ewconplus_wzc_seed5_2__323
129	resnet50_ewconplus_wzc_seed6_2_323
130	resnet50_st_wzc_seed7_2_323
131	resnet50_st_wzc_seed6_2_323
132	resnet50_ewconplus_wzc_seed2_2__323
133	VOneCORnet-S
134	voneresnet-50-non_stochastic
135	pnasnet_large_halfPrecision

Benchmark bibtex

None

Ceiling

0.54.

Note that scores are relative to this ceiling.

Data: Hebart2023_fmri.V1

Metric: ridgecv