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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Li2026.V4-ridgecv")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	voneresnet-50	.188
2	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.186
3	ReAlnet04_cornet	.181
4	ReAlnet01_cornet	.181
5	ReAlnet03_cornet	.181
6	custom_model_cv_18_dagger_408	.178
7	convnext_tiny:in12k_ft_in1k	.177
8	resnet50_ewconplus_wzc	.176
9	resnet50-vitoimagevidnet8	.176
10	resnet50-VITO-8deg-cc	.176
11	VOneCORnet-S	.175
12	resnet50-moclr8deg	.175
13	cvt_cvt-21-384-in1k_4_LucyV4	.174
14	convnext_tiny_sup	.172
15	cvt_cvt-13-384-in1k_4_LucyV4	.172
16	AdvProp_efficientnet-b4	.172
17	shufflenet_v2_x1_0	.171
18	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.171
19	cvt_cvt-21-224-in1k_4_LucyV4	.171
20	efficientnet_b2_imagenet_full	.170
21	resnet152_imagenet_full	.170
22	AdvProp_efficientnet-b8	.170
23	cvt_cvt-13-224-in1k_4_LucyV4	.170
24	focalnet_tiny_lrf_in1k	.169
25	efficientnet_b0	.169
26	resnet50-SIN	.169
27	AlexNet_SIN_fov12	.168
28	dinov2_vitl14_reg_lc	.167
29	AT_efficientnet-b2	.167
30	AdvProp_efficientnet-b2	.167
31	mobilenet_v2_1_4_224	.163
32	convnext_large:fb_in22k_ft_in1k	.162
33	regnety_032	.161
34	vit_relpos_base_patch16_clsgap_224:sw_in1k	.160
35	seresnext50_32x4d	.159
36	vit_large_patch14_clip_336:laion2b_ft_in1k	.158
37	alexnet	.158
38	vit_large_patch14_clip_224:openai_ft_in1k	.157
39	convnext_xlarge:fb_in22k_ft_in1k	.157
40	convnext_femto_ols:d1_in1k	.156
41	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.156
42	resnet50-SIN_IN_IN	.154
43	resnet_SIN_IN_FT_IN	.154
44	resnext101_32x48d_wsl	.154
45	resnext101_32x32d_wsl	.153
46	resnet50-SIN_IN	.152
47	Res2Net50_26w_4s	.151
48	effnetb1_272x240	.151
49	resnet_50_v1	.151
50	resnet50-sup	.151
51	resnet50_tutorial	.151
52	resnet_50_v1_spiking	.151
53	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.151
54	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.150
55	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.150
56	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.149
57	pnasnet_large_halfPrecision	.148
58	convnext_large_imagenet_full_seed-0	.147
59	antialiased-r50	.147
60	resnext101_32x8d_wsl	.145
61	deit_small_imagenet_full_seed-0	.145
62	convnext_tiny_imagenet_full_seed-0	.145
63	vit_base_patch16_clip_224:openai_ft_in1k	.144
64	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.143
65	convnext_base:clip_laiona_augreg_ft_in1k_384	.143
66	dinov2_vitl14_lc	.141
67	convnext_base_imagenet_full_seed-0	.141
68	AdvProp_efficientnet-b7	.140
69	AdvProp_efficientnet-b6	.139
70	convnext_small_imagenet_full_seed-0	.139
71	deit_large_imagenet_full_seed-0	.138
72	resnet50_robust_l2_eps1	.138
73	voneresnet-50-non_stochastic	.137
74	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.136
75	resnet_50_v2	.135
76	AlexNet_SIN	.134
77	vit_large_patch14_clip_224:laion2b_ft_in1k	.134
78	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.134
79	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.134
80	deit_base_imagenet_full_seed-0	.133
81	resnet-50-robust	.132
82	grcnn	.132
83	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.130
84	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.129
85	AlexNet_SIN_fov16	.128
86	resnet50_imagenet_full	.125
87	densenet-121	.125
88	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.122
89	eicircuit_resnet18_bn_20260919	.120
90	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.119
91	ViT_L_32_imagenet1k	.104
92	ReAlnet01_cornet_fov4	.081
93	gabor_filter_entropy_4x4_v2	.065
94	gabor_filter_entropy_4x4_v2_forcedCosine	.065
95	gabor_filter_entropy_4x4	.064
96	alexnet_training_seed_02	.063
97	alexnet_training_seed_06	.061
98	alexnet_training_seed_09	.059
99	alexnet_training_seed_03	.059
100	alexnet_training_seed_01	.056
101	alexnet_training_seed_10	.056
102	alexnet_training_seed_07	.055
103	hmax	.030
104	gabor_filter_mean_abs_4x4_v2	.025
105	compact_resnet50_robust_V4	.014
106	resnext101_32x16d_wsl
107	cvt_cvt-w24-384-in22k_finetuned-in1k_4

Benchmark bibtex

@article{li2026triplen,
    title = {Triple-N dataset: large-scale fMRI-guided dense recordings of nonhuman
             primate neural responses to natural scenes},
    author = {Li, Yipeng and Liu, Xieyi and Li, Wanru and Yang, Jia and Gong, Baoqi
              and Jin, Wei and Gong, Zhengxin and Wang, Kesheng and Luo, Jingqiu
              and Zhao, Zishuo and Bao, Pinglei},
    journal = {Nature Neuroscience},
    year = {2026},
    doi = {10.1038/s41593-026-02322-z},
    url = {https://doi.org/10.1038/s41593-026-02322-z},
}

Ceiling

0.77.

Note that scores are relative to this ceiling.

Data: Li2026.V4

Metric: ridgecv