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Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	AdvProp_efficientnet-b8	.304
2	AdvProp_efficientnet-b4	.288
3	mobilenet_v2_1_4_224	.258
4	AdvProp_efficientnet-b7	.251
5	efficientnet_b0	.248
6	effnetb1_272x240	.241
7	AdvProp_efficientnet-b6	.238
8	vit_relpos_base_patch16_clsgap_224:sw_in1k	.234
9	AlexNet_SIN_fov16	.227
10	convnext_large:fb_in22k_ft_in1k	.227
11	alexnet	.225
12	AlexNet_SIN	.224
13	AlexNet_SIN_fov12	.221
14	deit_base_imagenet_full_seed-0	.220
15	gabor_filter_entropy_4x4_v2_forcedCosine	.202
16	AT_efficientnet-b2	.197
17	AdvProp_efficientnet-b2	.197
18	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.195
19	resnet152_imagenet_full	.194
20	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.194
21	pnasnet_large_halfPrecision	.191
22	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.186
23	convnext_tiny_imagenet_full_seed-0	.182
24	voneresnet-50	.182
25	convnext_base_imagenet_full_seed-0	.180
26	convnext_small_imagenet_full_seed-0	.180
27	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.180
28	gabor_filter_entropy_4x4	.173
29	vit_large_patch14_clip_336:laion2b_ft_in1k	.169
30	ReAlnet01_cornet	.167
31	convnext_xlarge:fb_in22k_ft_in1k	.162
32	ReAlnet04_cornet	.161
33	convnext_large_imagenet_full_seed-0	.160
34	ReAlnet03_cornet	.160
35	gabor_filter_entropy_4x4_v2	.160
36	alexnet_training_seed_02	.159
37	resnet50_ewconplus_wzc	.157
38	alexnet_training_seed_06	.157
39	convnext_tiny_sup	.156
40	resnet-50-robust	.155
41	alexnet_training_seed_03	.153
42	hmax	.148
43	custom_model_cv_18_dagger_408	.142
44	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.140
45	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.140
46	deit_small_imagenet_full_seed-0	.133
47	alexnet_training_seed_01	.132
48	ViT_L_32_imagenet1k	.132
49	resnet50-SIN	.125
50	resnet50-moclr8deg	.120
51	cvt_cvt-13-224-in1k_4_LucyV4	.118
52	cvt_cvt-21-224-in1k_4_LucyV4	.113
53	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.111
54	densenet-121	.108
55	grcnn	.105
56	VOneCORnet-S	.103
57	alexnet_training_seed_09	.100
58	alexnet_training_seed_07	.096
59	dinov2_vitl14_lc	.084
60	focalnet_tiny_lrf_in1k	.080
61	convnext_base:clip_laiona_augreg_ft_in1k_384	.079
62	ReAlnet01_cornet_fov4	.071
63	vit_large_patch14_clip_224:openai_ft_in1k	.068
64	gabor_filter_mean_abs_4x4_v2	.068
65	vit_base_patch16_clip_224:openai_ft_in1k	.060
66	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.055
67	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.054
68	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.054
69	alexnet_training_seed_10	.053
70	cvt_cvt-13-384-in1k_4_LucyV4	.046
71	resnet50-vitoimagevidnet8	.031
72	resnet50-VITO-8deg-cc	.031
73	Res2Net50_26w_4s	.029
74	efficientnet_b2_imagenet_full	.023
75	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.018
76	cvt_cvt-21-384-in1k_4_LucyV4	.018
77	convnext_femto_ols:d1_in1k	.017
78	deit_large_imagenet_full_seed-0	.007
79	compact_resnet50_robust_V4	.000
80	seresnext50_32x4d	.000
81	resnet50-SIN_IN	.000
82	eicircuit_resnet18_bn_20260919	.000
83	vit_large_patch14_clip_224:laion2b_ft_in1k	.000
84	voneresnet-50-non_stochastic	.000
85	resnet50_imagenet_full	.000
86	antialiased-r50	.000
87	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.000
88	resnet50_robust_l2_eps1	.000
89	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.000
90	resnet50-SIN_IN_IN	.000
91	resnet_SIN_IN_FT_IN	.000
92	dinov2_vitl14_reg_lc	.000
93	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.000
94	resnet_50_v1_spiking	.000
95	resnet50_tutorial	.000
96	resnet50-sup	.000
97	resnet_50_v1	.000
98	regnety_032	.000
99	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.000
100	resnext101_32x32d_wsl	.000
101	shufflenet_v2_x1_0	.000
102	resnext101_32x48d_wsl	.000
103	resnext101_32x16d_wsl	.000
104	resnext101_32x8d_wsl	.000
105	convnext_tiny:in12k_ft_in1k	.000
106	resnet_50_v2	.000
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

0.78.

Note that scores are relative to this ceiling.

Data: Li2026.V4

Metric: rdm