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Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	ReAlnet01_cornet_fov4	.513
2	resnet152_imagenet_full	.431
3	resnet50_imagenet_full	.426
4	ReAlnet03_cornet	.424
5	ReAlnet01_cornet	.423
6	ReAlnet04_cornet	.423
7	resnet50_ewconplus_wzc	.423
8	deit_base_imagenet_full_seed-0	.413
9	resnet50-SIN_IN	.412
10	deit_large_imagenet_full_seed-0	.411
11	Res2Net50_26w_4s	.409
12	deit_small_imagenet_full_seed-0	.409
13	pnasnet_large_halfPrecision	.403
14	resnext101_32x8d_wsl	.402
15	resnet_50_v2	.399
16	dinov2_vitl14_reg_lc	.397
17	resnext101_32x32d_wsl	.394
18	AlexNet_SIN	.393
19	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.392
20	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.392
21	resnet50-vitoimagevidnet8	.391
22	resnet50-VITO-8deg-cc	.391
23	alexnet	.388
24	voneresnet-50	.387
25	dinov2_vitl14_lc	.384
26	resnet_SIN_IN_FT_IN	.382
27	cvt_cvt-21-224-in1k_4_LucyV4	.382
28	resnet-50-robust	.380
29	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.379
30	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.377
31	resnet50_robust_l2_eps1	.376
32	antialiased-r50	.376
33	cvt_cvt-13-224-in1k_4_LucyV4	.372
34	vit_base_patch16_clip_224:openai_ft_in1k	.371
35	resnet50-SIN	.371
36	vit_large_patch14_clip_224:laion2b_ft_in1k	.369
37	grcnn	.369
38	resnet50_tutorial	.369
39	resnet_50_v1	.369
40	resnet50-sup	.369
41	resnet_50_v1_spiking	.369
42	convnext_base:clip_laiona_augreg_ft_in1k_384	.369
43	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.368
44	resnet50-SIN_IN_IN	.366
45	densenet-121	.366
46	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.361
47	cvt_cvt-21-384-in1k_4_LucyV4	.358
48	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.357
49	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.352
50	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.350
51	mobilenet_v2_1_4_224	.348
52	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.347
53	vit_large_patch14_clip_224:openai_ft_in1k	.345
54	AdvProp_efficientnet-b6	.341
55	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.341
56	alexnet_training_seed_02	.340
57	alexnet_training_seed_09	.331
58	compact_resnet50_robust_V4	.331
59	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.330
60	cvt_cvt-13-384-in1k_4_LucyV4	.328
61	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.327
62	convnext_femto_ols:d1_in1k	.327
63	alexnet_training_seed_01	.325
64	AdvProp_efficientnet-b8	.324
65	alexnet_training_seed_07	.323
66	efficientnet_b2_imagenet_full	.323
67	convnext_small_imagenet_full_seed-0	.319
68	alexnet_training_seed_10	.317
69	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.317
70	custom_model_cv_18_dagger_408	.311
71	resnet50-moclr8deg	.311
72	convnext_tiny_sup	.310
73	vit_relpos_base_patch16_clsgap_224:sw_in1k	.310
74	AlexNet_SIN_fov12	.309
75	resnext101_32x16d_wsl	.309
76	resnext101_32x48d_wsl	.308
77	vit_large_patch14_clip_336:laion2b_ft_in1k	.299
78	AdvProp_efficientnet-b7	.298
79	efficientnet_b0	.295
80	ViT_L_32_imagenet1k	.292
81	AdvProp_efficientnet-b4	.291
82	alexnet_training_seed_06	.290
83	shufflenet_v2_x1_0	.272
84	voneresnet-50-non_stochastic	.261
85	alexnet_training_seed_03	.260
86	VOneCORnet-S	.257
87	effnetb1_272x240	.252
88	AT_efficientnet-b2	.251
89	AdvProp_efficientnet-b2	.251
90	AlexNet_SIN_fov16	.242
91	convnext_base_imagenet_full_seed-0	.239
92	convnext_large:fb_in22k_ft_in1k	.231
93	convnext_xlarge:fb_in22k_ft_in1k	.229
94	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.218
95	convnext_tiny_imagenet_full_seed-0	.182
96	convnext_large_imagenet_full_seed-0	.169
97	convnext_tiny:in12k_ft_in1k	.145
98	hmax	.038
99	focalnet_tiny_lrf_in1k
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

0.67.

Note that scores are relative to this ceiling.

Data: Li2026.V1

Metric: ridgecv