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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_training_seed_06	.316
3	AlexNet_SIN	.309
4	mobilenet_v2_1_4_224	.297
5	alexnet_training_seed_02	.293
6	alexnet_training_seed_01	.278
7	alexnet_training_seed_03	.269
8	AlexNet_SIN_fov12	.250
9	AlexNet_SIN_fov16	.250
10	alexnet_training_seed_09	.232
11	alexnet_training_seed_07	.230
12	cvt_cvt-13-224-in1k_4_LucyV4	.201
13	convnext_large:fb_in22k_ft_in1k	.199
14	alexnet_training_seed_10	.195
15	resnet-50-robust	.189
16	resnet50_robust_l2_eps1	.179
17	deit_small_imagenet_full_seed-0	.163
18	AdvProp_efficientnet-b4	.161
19	effnetb1_272x240	.161
20	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.159
21	alexnet	.158
22	AdvProp_efficientnet-b8	.154
23	resnet50-VITO-8deg-cc	.152
24	resnet50-vitoimagevidnet8	.152
25	AdvProp_efficientnet-b6	.144
26	convnext_tiny_imagenet_full_seed-0	.134
27	convnext_femto_ols:d1_in1k	.131
28	resnet50-moclr8deg	.126
29	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.125
30	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.123
31	vit_base_patch16_clip_224:openai_ft_in1k	.121
32	resnet50_ewconplus_wzc	.113
33	vit_large_patch14_clip_224:laion2b_ft_in1k	.111
34	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.107
35	AT_efficientnet-b2	.106
36	AdvProp_efficientnet-b2	.106
37	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.106
38	AdvProp_efficientnet-b7	.105
39	focalnet_tiny_lrf_in1k	.105
40	resnet152_imagenet_full	.105
41	convnext_large_imagenet_full_seed-0	.104
42	vit_large_patch14_clip_336:laion2b_ft_in1k	.102
43	grcnn	.094
44	resnet50-SIN_IN	.094
45	efficientnet_b0	.084
46	deit_base_imagenet_full_seed-0	.083
47	antialiased-r50	.081
48	deit_large_imagenet_full_seed-0	.081
49	pnasnet_large_halfPrecision	.080
50	convnext_base_imagenet_full_seed-0	.078
51	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.074
52	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.073
53	convnext_xlarge:fb_in22k_ft_in1k	.071
54	ViT_L_32_imagenet1k	.071
55	resnet50-SIN	.068
56	convnext_small_imagenet_full_seed-0	.068
57	convnext_tiny:in12k_ft_in1k	.068
58	resnet50-SIN_IN_IN	.066
59	resnet_SIN_IN_FT_IN	.066
60	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.065
61	custom_model_cv_18_dagger_408	.064
62	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.059
63	cvt_cvt-21-224-in1k_4_LucyV4	.058
64	vit_large_patch14_clip_224:openai_ft_in1k	.053
65	dinov2_vitl14_lc	.053
66	voneresnet-50-non_stochastic	.052
67	resnet_50_v1_spiking	.050
68	resnet_50_v1	.050
69	resnet50-sup	.050
70	convnext_base:clip_laiona_augreg_ft_in1k_384	.048
71	voneresnet-50	.044
72	resnet50_imagenet_full	.034
73	eicircuit_resnet18_bn_20260919	.029
74	cvt_cvt-21-384-in1k_4_LucyV4	.027
75	Res2Net50_26w_4s	.024
76	cvt_cvt-13-384-in1k_4_LucyV4	.012
77	compact_resnet50_robust_V4	.005
78	convnext_tiny_sup	.004
79	VOneCORnet-S	.000
80	vit_relpos_base_patch16_clsgap_224:sw_in1k	.000
81	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.000
82	shufflenet_v2_x1_0	.000
83	resnext101_32x16d_wsl	.000
84	efficientnet_b2_imagenet_full	.000
85	resnet50_tutorial	.000
86	resnext101_32x48d_wsl	.000
87	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.000
88	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.000
89	ReAlnet03_cornet	.000
90	resnext101_32x8d_wsl	.000
91	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.000
92	ReAlnet04_cornet	.000
93	resnext101_32x32d_wsl	.000
94	ReAlnet01_cornet	.000
95	resnet_50_v2	.000
96	gabor_filter_entropy_4x4	.000
97	gabor_filter_mean_abs_4x4_v2	.000
98	densenet-121	.000
99	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.000
100	gabor_filter_entropy_4x4_v2	.000
101	regnety_032	.000
102	hmax	.000
103	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.000
104	dinov2_vitl14_reg_lc	.000
105	ReAlnet01_cornet_fov4	.000
106	seresnext50_32x4d
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

0.91.

Note that scores are relative to this ceiling.

Data: Li2026.V2

Metric: rdm