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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	ReAlnet01_cornet_fov16	.224
2	ReAlnet01_cornet_fov12	.221
3	voneresnet-50	.188
4	resnet50_barlowtwins_linhead	.186
5	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.186
6	ReAlnet06_cornet	.183
7	resnet50_byol_linhead	.183
8	ReAlnet07_cornet	.183
9	ReAlnet09_cornet	.182
10	ReAlnet02_cornet	.182
11	ReAlnet05_cornet	.181
12	ReAlnet10_cornet	.181
13	ReAlnet08_cornet	.181
14	ReAlnet04_cornet	.181
15	ReAlnet01_cornet	.181
16	resnet50_vicreg_linhead	.181
17	ReAlnet03_cornet	.181
18	resnet50_simsiam_linhead	.179
19	custom_model_cv_18_dagger_408	.178
20	convnext_tiny:in12k_ft_in1k	.177
21	resnet50_simclr_linhead_01norm	.176
22	resnet50_ewconplus_wzc	.176
23	resnet50-vitoimagevidnet8	.176
24	resnet50-VITO-8deg-cc	.176
25	VOneCORnet-S	.175
26	resnet50-moclr8deg	.175
27	cvt_cvt-21-384-in1k_4_LucyV4	.174
28	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.174
29	convnext_tiny_sup	.172
30	cvt_cvt-13-384-in1k_4_LucyV4	.172
31	AdvProp_efficientnet-b4	.172
32	shufflenet_v2_x1_0	.171
33	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.171
34	cvt_cvt-21-224-in1k_4_LucyV4	.171
35	efficientnet_b2_imagenet_full	.170
36	resnet152_imagenet_full	.170
37	AdvProp_efficientnet-b8	.170
38	cvt_cvt-13-224-in1k_4_LucyV4	.170
39	focalnet_tiny_lrf_in1k	.169
40	efficientnet_b0	.169
41	ijepa_vith14_linhead	.169
42	resnet50-SIN	.169
43	resnet101_imagenet_full	.168
44	resnet152_ecoset_full	.168
45	AlexNet_SIN_fov12	.168
46	resnet101_ecoset_full	.167
47	dinov2_vitl14_reg_lc	.167
48	AT_efficientnet-b2	.167
49	AdvProp_efficientnet-b2	.167
50	mobilenet_v2_1_4_224	.163
51	convnext_large:fb_in22k_ft_in1k	.162
52	regnety_032	.161
53	vit_relpos_base_patch16_clsgap_224:sw_in1k	.160
54	seresnext50_32x4d	.159
55	mehrer2020_alexnet_training_seed_01_fov12	.158
56	vit_large_patch14_clip_336:laion2b_ft_in1k	.158
57	alexnet	.158
58	mehrer2020_alexnet_training_seed_01_fov16	.157
59	vit_large_patch14_clip_224:openai_ft_in1k	.157
60	convnext_xlarge:fb_in22k_ft_in1k	.157
61	convnext_femto_ols:d1_in1k	.156
62	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.156
63	mehrer2020_alexnet_training_seed_04	.155
64	mehrer2020_alexnet_training_seed_05	.154
65	resnet50-SIN_IN_IN	.154
66	resnet_SIN_IN_FT_IN	.154
67	resnext101_32x48d_wsl	.154
68	mehrer2020_alexnet_training_seed_03	.154
69	resnext101_32x32d_wsl	.153
70	mehrer2020_alexnet_training_seed_02	.153
71	mehrer2020_alexnet_training_seed_01	.153
72	resnet50-SIN_IN	.152
73	Res2Net50_26w_4s	.151
74	effnetb1_272x240	.151
75	resnet_50_v1	.151
76	resnet50-sup	.151
77	resnet50_tutorial	.151
78	resnet_50_v1_spiking	.151
79	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.151
80	mehrer2020_alexnet_training_seed_06	.151
81	mehrer2020_alexnet_training_seed_07	.151
82	mehrer2020_alexnet_training_seed_09	.150
83	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.150
84	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.150
85	mehrer2020_alexnet_training_seed_08	.150
86	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.149
87	resnext101_32x16d_wsl	.148
88	pnasnet_large_halfPrecision	.148
89	convnext_large_imagenet_full_seed-0	.147
90	antialiased-r50	.147
91	resnext101_32x8d_wsl	.145
92	deit_small_imagenet_full_seed-0	.145
93	convnext_tiny_imagenet_full_seed-0	.145
94	vit_base_patch16_clip_224:openai_ft_in1k	.144
95	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.143
96	convnext_base:clip_laiona_augreg_ft_in1k_384	.143
97	resnet34_ecoset_full	.142
98	dinov2_vitl14_lc	.141
99	convnext_base_imagenet_full_seed-0	.141
100	AdvProp_efficientnet-b7	.140
101	AdvProp_efficientnet-b6	.139
102	convnext_small_imagenet_full_seed-0	.139
103	deit_large_imagenet_full_seed-0	.138
104	resnet50_robust_l2_eps1	.138
105	efficientnet_b1_imagenet_full	.137
106	voneresnet-50-non_stochastic	.137
107	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.136
108	resnet_50_v2	.135
109	AlexNet_SIN	.134
110	vit_large_patch14_clip_224:laion2b_ft_in1k	.134
111	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.134
112	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.134
113	deit_base_imagenet_full_seed-0	.133
114	resnet-50-robust	.132
115	grcnn	.132
116	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.130
117	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.129
118	AlexNet_SIN_fov16	.128
119	resnet50_imagenet_100_seed-0	.127
120	resnet50_ecoset_full	.127
121	resnet18_imagenet_full	.126
122	resnet50_imagenet_full	.125
123	densenet-121	.125
124	resnet18_ecoset_full	.123
125	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.122
126	efficientnet_b0_imagenet_full	.122
127	eicircuit_resnet18_bn_20260919	.120
128	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.119
129	resnet34_imagenet_full	.117
130	mehrer2020_alexnet_training_seed_10	.117
131	convnext_small_imagenet_100_seed-0	.110
132	deit_small_imagenet_100_seed-0	.104
133	ViT_L_32_imagenet1k	.104
134	ReAlnet01_cornet_fov4	.081
135	gabor_filter_entropy_4x4_v2	.065
136	gabor_filter_entropy_4x4_v2_forcedCosine	.065
137	gabor_filter_entropy_4x4	.064
138	alexnet_training_seed_02	.063
139	mehrer2020_alexnet_training_seed_01_fov4	.063
140	alexnet_training_seed_06	.061
141	alexnet_training_seed_09	.059
142	alexnet_training_seed_03	.059
143	alexnet_training_seed_01	.056
144	alexnet_training_seed_10	.056
145	alexnet_training_seed_07	.055
146	AlexNet_SIN_fov4	.054
147	hmax	.030
148	gabor_filter_mean_abs_4x4_v2	.025
149	compact_resnet50_robust_V4	.014

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