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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Li2026.V1-rdm")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet-50-robust	.340
2	resnet_SIN_IN_FT_IN	.323
3	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.289
4	alexnet_training_seed_02	.287
5	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.281
6	resnet50_imagenet_full	.281
7	alexnet	.275
8	ReAlnet01_cornet_fov4	.272
9	compact_resnet50_robust_V4	.263
10	alexnet_training_seed_06	.260
11	AlexNet_SIN	.260
12	alexnet_training_seed_01	.254
13	alexnet_training_seed_09	.248
14	ReAlnet03_cornet	.242
15	vit_large_patch14_clip_224:laion2b_ft_in1k	.240
16	grcnn	.239
17	alexnet_training_seed_03	.238
18	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.237
19	convnext_femto_ols:d1_in1k	.236
20	ReAlnet01_cornet	.235
21	resnet50-SIN_IN	.235
22	ReAlnet04_cornet	.234
23	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.234
24	gabor_filter_entropy_4x4_v2_forcedCosine	.233
25	pnasnet_large_halfPrecision	.232
26	eicircuit_resnet18_bn_20260919	.232
27	deit_base_imagenet_full_seed-0	.230
28	resnet152_imagenet_full	.223
29	alexnet_training_seed_07	.222
30	voneresnet-50	.221
31	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.221
32	deit_large_imagenet_full_seed-0	.215
33	cvt_cvt-13-224-in1k_4_LucyV4	.214
34	mobilenet_v2_1_4_224	.211
35	alexnet_training_seed_10	.208
36	AlexNet_SIN_fov16	.206
37	AlexNet_SIN_fov12	.202
38	resnet50-SIN	.194
39	deit_small_imagenet_full_seed-0	.191
40	efficientnet_b0	.177
41	resnet50_ewconplus_wzc	.173
42	AdvProp_efficientnet-b6	.164
43	convnext_small_imagenet_full_seed-0	.162
44	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.160
45	densenet-121	.156
46	Res2Net50_26w_4s	.153
47	antialiased-r50	.153
48	AdvProp_efficientnet-b4	.150
49	convnext_tiny_sup	.141
50	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.135
51	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.135
52	resnet50-vitoimagevidnet8	.129
53	resnet50-VITO-8deg-cc	.129
54	vit_large_patch14_clip_224:openai_ft_in1k	.126
55	AdvProp_efficientnet-b8	.126
56	dinov2_vitl14_lc	.117
57	AdvProp_efficientnet-b7	.117
58	resnet50-SIN_IN_IN	.115
59	convnext_xlarge:fb_in22k_ft_in1k	.115
60	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.113
61	focalnet_tiny_lrf_in1k	.113
62	resnet50_robust_l2_eps1	.107
63	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.107
64	ViT_L_32_imagenet1k	.104
65	convnext_tiny_imagenet_full_seed-0	.104
66	vit_large_patch14_clip_336:laion2b_ft_in1k	.102
67	resnet50_tutorial	.098
68	resnet50-sup	.098
69	resnet_50_v1	.098
70	resnet_50_v1_spiking	.098
71	AT_efficientnet-b2	.097
72	AdvProp_efficientnet-b2	.097
73	convnext_base_imagenet_full_seed-0	.096
74	effnetb1_272x240	.092
75	cvt_cvt-21-224-in1k_4_LucyV4	.092
76	cvt_cvt-21-384-in1k_4_LucyV4	.089
77	custom_model_cv_18_dagger_408	.079
78	convnext_large_imagenet_full_seed-0	.071
79	convnext_large:fb_in22k_ft_in1k	.070
80	convnext_tiny:in12k_ft_in1k	.069
81	voneresnet-50-non_stochastic	.064
82	cvt_cvt-13-384-in1k_4_LucyV4	.062
83	vit_base_patch16_clip_224:openai_ft_in1k	.058
84	resnet50-moclr8deg	.054
85	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.051
86	shufflenet_v2_x1_0	.050
87	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.043
88	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.040
89	vit_relpos_base_patch16_clsgap_224:sw_in1k	.038
90	resnext101_32x8d_wsl	.032
91	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.027
92	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.014
93	VOneCORnet-S	.014
94	efficientnet_b2_imagenet_full	.010
95	regnety_032	.004
96	resnet_50_v2	.000
97	resnext101_32x32d_wsl	.000
98	resnext101_32x48d_wsl	.000
99	gabor_filter_mean_abs_4x4_v2	.000
100	resnext101_32x16d_wsl	.000
101	gabor_filter_entropy_4x4	.000
102	gabor_filter_entropy_4x4_v2	.000
103	dinov2_vitl14_reg_lc	.000
104	hmax	.000
105	seresnext50_32x4d
106	cvt_cvt-w24-384-in22k_finetuned-in1k_4
107	convnext_base:clip_laiona_augreg_ft_in1k_384

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

0.82.

Note that scores are relative to this ceiling.

Data: Li2026.V1

Metric: rdm