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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	pnasnet_large_halfPrecision	.232
25	deit_base_imagenet_full_seed-0	.230
26	resnet152_imagenet_full	.223
27	alexnet_training_seed_07	.222
28	voneresnet-50	.221
29	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.221
30	deit_large_imagenet_full_seed-0	.215
31	cvt_cvt-13-224-in1k_4_LucyV4	.214
32	mobilenet_v2_1_4_224	.211
33	alexnet_training_seed_10	.208
34	AlexNet_SIN_fov16	.206
35	AlexNet_SIN_fov12	.202
36	resnet50-SIN	.194
37	deit_small_imagenet_full_seed-0	.191
38	efficientnet_b0	.177
39	resnet50_ewconplus_wzc	.173
40	AdvProp_efficientnet-b6	.164
41	convnext_small_imagenet_full_seed-0	.162
42	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.160
43	densenet-121	.156
44	Res2Net50_26w_4s	.153
45	antialiased-r50	.153
46	AdvProp_efficientnet-b4	.150
47	convnext_tiny_sup	.141
48	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.135
49	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.135
50	resnet50-vitoimagevidnet8	.129
51	resnet50-VITO-8deg-cc	.129
52	vit_large_patch14_clip_224:openai_ft_in1k	.126
53	AdvProp_efficientnet-b8	.126
54	dinov2_vitl14_lc	.117
55	AdvProp_efficientnet-b7	.117
56	resnet50-SIN_IN_IN	.115
57	convnext_xlarge:fb_in22k_ft_in1k	.115
58	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.113
59	focalnet_tiny_lrf_in1k	.113
60	resnet50_robust_l2_eps1	.107
61	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.107
62	ViT_L_32_imagenet1k	.104
63	convnext_tiny_imagenet_full_seed-0	.104
64	vit_large_patch14_clip_336:laion2b_ft_in1k	.102
65	resnet50_tutorial	.098
66	resnet50-sup	.098
67	resnet_50_v1	.098
68	resnet_50_v1_spiking	.098
69	AT_efficientnet-b2	.097
70	AdvProp_efficientnet-b2	.097
71	convnext_base_imagenet_full_seed-0	.096
72	effnetb1_272x240	.092
73	cvt_cvt-21-224-in1k_4_LucyV4	.092
74	cvt_cvt-21-384-in1k_4_LucyV4	.089
75	custom_model_cv_18_dagger_408	.079
76	convnext_large_imagenet_full_seed-0	.071
77	convnext_large:fb_in22k_ft_in1k	.070
78	convnext_tiny:in12k_ft_in1k	.069
79	voneresnet-50-non_stochastic	.064
80	cvt_cvt-13-384-in1k_4_LucyV4	.062
81	vit_base_patch16_clip_224:openai_ft_in1k	.058
82	resnet50-moclr8deg	.054
83	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.051
84	shufflenet_v2_x1_0	.050
85	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.043
86	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.040
87	vit_relpos_base_patch16_clsgap_224:sw_in1k	.038
88	resnext101_32x8d_wsl	.032
89	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.027
90	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.014
91	VOneCORnet-S	.014
92	efficientnet_b2_imagenet_full	.010
93	resnet_50_v2	.000
94	resnext101_32x32d_wsl	.000
95	resnext101_32x48d_wsl	.000
96	resnext101_32x16d_wsl	.000
97	dinov2_vitl14_reg_lc	.000
98	hmax	.000
99	cvt_cvt-w24-384-in22k_finetuned-in1k_4
100	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