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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	alexnet_training_seed_06	.316
2	AlexNet_SIN	.309
3	mobilenet_v2_1_4_224	.297
4	alexnet_training_seed_02	.293
5	alexnet_training_seed_01	.278
6	alexnet_training_seed_03	.269
7	AlexNet_SIN_fov12	.250
8	AlexNet_SIN_fov16	.250
9	alexnet_training_seed_09	.232
10	alexnet_training_seed_07	.230
11	cvt_cvt-13-224-in1k_4_LucyV4	.201
12	convnext_large:fb_in22k_ft_in1k	.199
13	alexnet_training_seed_10	.195
14	resnet-50-robust	.189
15	resnet50_robust_l2_eps1	.179
16	deit_small_imagenet_full_seed-0	.163
17	AdvProp_efficientnet-b4	.161
18	effnetb1_272x240	.161
19	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.159
20	alexnet	.158
21	AdvProp_efficientnet-b8	.154
22	resnet50-VITO-8deg-cc	.152
23	resnet50-vitoimagevidnet8	.152
24	AdvProp_efficientnet-b6	.144
25	convnext_tiny_imagenet_full_seed-0	.134
26	convnext_femto_ols:d1_in1k	.131
27	resnet50-moclr8deg	.126
28	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.125
29	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.123
30	vit_base_patch16_clip_224:openai_ft_in1k	.121
31	resnet50_ewconplus_wzc	.113
32	vit_large_patch14_clip_224:laion2b_ft_in1k	.111
33	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.107
34	AT_efficientnet-b2	.106
35	AdvProp_efficientnet-b2	.106
36	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.106
37	AdvProp_efficientnet-b7	.105
38	focalnet_tiny_lrf_in1k	.105
39	resnet152_imagenet_full	.105
40	convnext_large_imagenet_full_seed-0	.104
41	vit_large_patch14_clip_336:laion2b_ft_in1k	.102
42	grcnn	.094
43	resnet50-SIN_IN	.094
44	efficientnet_b0	.084
45	deit_base_imagenet_full_seed-0	.083
46	antialiased-r50	.081
47	deit_large_imagenet_full_seed-0	.081
48	pnasnet_large_halfPrecision	.080
49	convnext_base_imagenet_full_seed-0	.078
50	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.074
51	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.073
52	convnext_xlarge:fb_in22k_ft_in1k	.071
53	ViT_L_32_imagenet1k	.071
54	resnet50-SIN	.068
55	convnext_small_imagenet_full_seed-0	.068
56	convnext_tiny:in12k_ft_in1k	.068
57	resnet50-SIN_IN_IN	.066
58	resnet_SIN_IN_FT_IN	.066
59	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.065
60	custom_model_cv_18_dagger_408	.064
61	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.059
62	cvt_cvt-21-224-in1k_4_LucyV4	.058
63	vit_large_patch14_clip_224:openai_ft_in1k	.053
64	dinov2_vitl14_lc	.053
65	voneresnet-50-non_stochastic	.052
66	resnet_50_v1_spiking	.050
67	resnet_50_v1	.050
68	resnet50-sup	.050
69	convnext_base:clip_laiona_augreg_ft_in1k_384	.048
70	voneresnet-50	.044
71	resnet50_imagenet_full	.034
72	cvt_cvt-21-384-in1k_4_LucyV4	.027
73	Res2Net50_26w_4s	.024
74	cvt_cvt-13-384-in1k_4_LucyV4	.012
75	compact_resnet50_robust_V4	.005
76	convnext_tiny_sup	.004
77	VOneCORnet-S	.000
78	vit_relpos_base_patch16_clsgap_224:sw_in1k	.000
79	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.000
80	shufflenet_v2_x1_0	.000
81	resnext101_32x16d_wsl	.000
82	efficientnet_b2_imagenet_full	.000
83	resnet50_tutorial	.000
84	resnext101_32x48d_wsl	.000
85	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.000
86	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.000
87	ReAlnet03_cornet	.000
88	resnext101_32x8d_wsl	.000
89	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.000
90	ReAlnet04_cornet	.000
91	resnext101_32x32d_wsl	.000
92	ReAlnet01_cornet	.000
93	resnet_50_v2	.000
94	densenet-121	.000
95	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.000
96	hmax	.000
97	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.000
98	dinov2_vitl14_reg_lc	.000
99	ReAlnet01_cornet_fov4	.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

0.91.

Note that scores are relative to this ceiling.

Data: Li2026.V2

Metric: rdm