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

0.78.

Note that scores are relative to this ceiling.

Data: Li2026.V4

Metric: rdm