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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	vit_relpos_base_patch16_clsgap_224:sw_in1k	.160
34	vit_large_patch14_clip_336:laion2b_ft_in1k	.158
35	alexnet	.158
36	vit_large_patch14_clip_224:openai_ft_in1k	.157
37	convnext_xlarge:fb_in22k_ft_in1k	.157
38	convnext_femto_ols:d1_in1k	.156
39	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.156
40	resnet50-SIN_IN_IN	.154
41	resnet_SIN_IN_FT_IN	.154
42	resnext101_32x48d_wsl	.154
43	resnext101_32x32d_wsl	.153
44	resnet50-SIN_IN	.152
45	Res2Net50_26w_4s	.151
46	effnetb1_272x240	.151
47	resnet_50_v1	.151
48	resnet50-sup	.151
49	resnet50_tutorial	.151
50	resnet_50_v1_spiking	.151
51	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.151
52	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.150
53	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.150
54	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.149
55	pnasnet_large_halfPrecision	.148
56	convnext_large_imagenet_full_seed-0	.147
57	antialiased-r50	.147
58	resnext101_32x8d_wsl	.145
59	deit_small_imagenet_full_seed-0	.145
60	convnext_tiny_imagenet_full_seed-0	.145
61	vit_base_patch16_clip_224:openai_ft_in1k	.144
62	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.143
63	convnext_base:clip_laiona_augreg_ft_in1k_384	.143
64	dinov2_vitl14_lc	.141
65	convnext_base_imagenet_full_seed-0	.141
66	AdvProp_efficientnet-b7	.140
67	AdvProp_efficientnet-b6	.139
68	convnext_small_imagenet_full_seed-0	.139
69	deit_large_imagenet_full_seed-0	.138
70	resnet50_robust_l2_eps1	.138
71	voneresnet-50-non_stochastic	.137
72	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.136
73	resnet_50_v2	.135
74	AlexNet_SIN	.134
75	vit_large_patch14_clip_224:laion2b_ft_in1k	.134
76	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.134
77	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.134
78	deit_base_imagenet_full_seed-0	.133
79	resnet-50-robust	.132
80	grcnn	.132
81	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.130
82	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.129
83	AlexNet_SIN_fov16	.128
84	resnet50_imagenet_full	.125
85	densenet-121	.125
86	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.122
87	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.119
88	ViT_L_32_imagenet1k	.104
89	ReAlnet01_cornet_fov4	.081
90	alexnet_training_seed_02	.063
91	alexnet_training_seed_06	.061
92	alexnet_training_seed_09	.059
93	alexnet_training_seed_03	.059
94	alexnet_training_seed_01	.056
95	alexnet_training_seed_10	.056
96	alexnet_training_seed_07	.055
97	hmax	.030
98	compact_resnet50_robust_V4	.014
99	resnext101_32x16d_wsl
100	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