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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Li2026.V2-rdm")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	gabor_filter_entropy_4x4_v2_forcedCosine	.343
2	AlexNet_SIN_fov4	.337
3	alexnet_training_seed_06	.316
4	AlexNet_SIN	.309
5	mobilenet_v2_1_4_224	.297
6	alexnet_training_seed_02	.293
7	alexnet_training_seed_01	.278
8	alexnet_training_seed_03	.269
9	AlexNet_SIN_fov12	.250
10	AlexNet_SIN_fov16	.250
11	alexnet_training_seed_09	.232
12	alexnet_training_seed_07	.230
13	mehrer2020_alexnet_training_seed_01_fov4	.215
14	cvt_cvt-13-224-in1k_4_LucyV4	.201
15	convnext_large:fb_in22k_ft_in1k	.199
16	alexnet_training_seed_10	.195
17	mehrer2020_alexnet_training_seed_01_fov16	.194
18	resnet-50-robust	.189
19	mehrer2020_alexnet_training_seed_08	.183
20	mehrer2020_alexnet_training_seed_10	.183
21	mehrer2020_alexnet_training_seed_06	.182
22	mehrer2020_alexnet_training_seed_07	.181
23	resnet50_robust_l2_eps1	.179
24	mehrer2020_alexnet_training_seed_05	.175
25	mehrer2020_alexnet_training_seed_09	.174
26	mehrer2020_alexnet_training_seed_04	.174
27	mehrer2020_alexnet_training_seed_01	.171
28	mehrer2020_alexnet_training_seed_02	.169
29	mehrer2020_alexnet_training_seed_03	.163
30	deit_small_imagenet_full_seed-0	.163
31	AdvProp_efficientnet-b4	.161
32	effnetb1_272x240	.161
33	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.159
34	alexnet	.158
35	AdvProp_efficientnet-b8	.154
36	resnet50-VITO-8deg-cc	.152
37	resnet50-vitoimagevidnet8	.152
38	resnet50_byol_linhead	.148
39	AdvProp_efficientnet-b6	.144
40	convnext_tiny_imagenet_full_seed-0	.134
41	deit_small_imagenet_100_seed-0	.133
42	convnext_femto_ols:d1_in1k	.131
43	resnet50-moclr8deg	.126
44	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.125
45	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.123
46	vit_base_patch16_clip_224:openai_ft_in1k	.121
47	convnext_small_imagenet_100_seed-0	.120
48	mehrer2020_alexnet_training_seed_01_fov12	.119
49	resnet50_ewconplus_wzc	.113
50	resnet34_ecoset_full	.111
51	vit_large_patch14_clip_224:laion2b_ft_in1k	.111
52	resnet152_ecoset_full	.109
53	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.107
54	AT_efficientnet-b2	.106
55	AdvProp_efficientnet-b2	.106
56	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.106
57	AdvProp_efficientnet-b7	.105
58	focalnet_tiny_lrf_in1k	.105
59	resnet152_imagenet_full	.105
60	convnext_large_imagenet_full_seed-0	.104
61	vit_large_patch14_clip_336:laion2b_ft_in1k	.102
62	efficientnet_b0_imagenet_full	.102
63	ijepa_vith14_linhead	.101
64	grcnn	.094
65	resnet50_imagenet_100_seed-0	.094
66	resnet50-SIN_IN	.094
67	efficientnet_b0	.084
68	deit_base_imagenet_full_seed-0	.083
69	antialiased-r50	.081
70	deit_large_imagenet_full_seed-0	.081
71	pnasnet_large_halfPrecision	.080
72	resnet101_ecoset_full	.079
73	resnet50_ecoset_full	.079
74	convnext_base_imagenet_full_seed-0	.078
75	resnet34_imagenet_full	.074
76	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.074
77	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.073
78	convnext_xlarge:fb_in22k_ft_in1k	.071
79	ViT_L_32_imagenet1k	.071
80	resnet50-SIN	.068
81	convnext_small_imagenet_full_seed-0	.068
82	convnext_tiny:in12k_ft_in1k	.068
83	resnet50-SIN_IN_IN	.066
84	resnet_SIN_IN_FT_IN	.066
85	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.065
86	custom_model_cv_18_dagger_408	.064
87	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.059
88	cvt_cvt-21-224-in1k_4_LucyV4	.058
89	vit_large_patch14_clip_224:openai_ft_in1k	.053
90	dinov2_vitl14_lc	.053
91	voneresnet-50-non_stochastic	.052
92	resnet_50_v1_spiking	.050
93	resnet_50_v1	.050
94	resnet50-sup	.050
95	resnet50_simsiam_linhead	.048
96	convnext_base:clip_laiona_augreg_ft_in1k_384	.048
97	voneresnet-50	.044
98	ReAlnet01_cornet_fov16	.035
99	resnet50_imagenet_full	.034
100	resnet18_imagenet_full	.029
101	eicircuit_resnet18_bn_20260919	.029
102	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.028
103	cvt_cvt-21-384-in1k_4_LucyV4	.027
104	resnet18_ecoset_full	.025
105	Res2Net50_26w_4s	.024
106	resnet50_barlowtwins_linhead	.013
107	cvt_cvt-13-384-in1k_4_LucyV4	.012
108	resnet50_vicreg_linhead	.011
109	compact_resnet50_robust_V4	.005
110	ReAlnet08_cornet	.005
111	convnext_tiny_sup	.004
112	ReAlnet01_cornet_fov12	.003
113	resnet101_imagenet_full	.001
114	ReAlnet06_cornet	.000
115	ReAlnet05_cornet	.000
116	VOneCORnet-S	.000
117	vit_relpos_base_patch16_clsgap_224:sw_in1k	.000
118	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.000
119	resnet50_simclr_linhead_01norm	.000
120	ReAlnet07_cornet	.000
121	shufflenet_v2_x1_0	.000
122	ReAlnet09_cornet	.000
123	ReAlnet02_cornet	.000
124	resnext101_32x16d_wsl	.000
125	efficientnet_b2_imagenet_full	.000
126	resnet50_tutorial	.000
127	resnext101_32x48d_wsl	.000
128	ReAlnet10_cornet	.000
129	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.000
130	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.000
131	ReAlnet03_cornet	.000
132	resnext101_32x8d_wsl	.000
133	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.000
134	ReAlnet04_cornet	.000
135	resnext101_32x32d_wsl	.000
136	ReAlnet01_cornet	.000
137	resnet_50_v2	.000
138	gabor_filter_entropy_4x4	.000
139	gabor_filter_mean_abs_4x4_v2	.000
140	densenet-121	.000
141	efficientnet_b1_imagenet_full	.000
142	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.000
143	gabor_filter_entropy_4x4_v2	.000
144	regnety_032	.000
145	hmax	.000
146	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.000
147	dinov2_vitl14_reg_lc	.000
148	ReAlnet01_cornet_fov4	.000
149	seresnext50_32x4d

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

0.91.

Note that scores are relative to this ceiling.

Data: Li2026.V2

Metric: rdm