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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Li2026.V2-ridgecv")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	ReAlnet01_cornet_fov4	.425
2	ReAlnet03_cornet	.341
3	ReAlnet01_cornet	.341
4	ReAlnet04_cornet	.340
5	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.339
6	resnet50_tutorial	.337
7	VOneCORnet-S	.337
8	dinov2_vitl14_reg_lc	.334
9	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.334
10	cvt_cvt-13-224-in1k_4_LucyV4	.331
11	compact_resnet50_robust_V4	.328
12	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.324
13	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.319
14	regnety_032	.314
15	cvt_cvt-21-384-in1k_4_LucyV4	.313
16	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.312
17	resnet50_robust_l2_eps1	.310
18	effnetb1_272x240	.310
19	resnet50_ewconplus_wzc	.308
20	deit_small_imagenet_full_seed-0	.306
21	AlexNet_SIN	.297
22	resnet152_imagenet_full	.297
23	cvt_cvt-13-384-in1k_4_LucyV4	.296
24	dinov2_vitl14_lc	.296
25	mobilenet_v2_1_4_224	.295
26	resnet50-SIN_IN	.295
27	seresnext50_32x4d	.295
28	focalnet_tiny_lrf_in1k	.294
29	voneresnet-50-non_stochastic	.293
30	resnet50-SIN	.290
31	resnet_50_v1_spiking	.287
32	resnet_50_v1	.287
33	resnet50-sup	.287
34	cvt_cvt-21-224-in1k_4_LucyV4	.287
35	vit_large_patch14_clip_336:laion2b_ft_in1k	.287
36	resnet50-VITO-8deg-cc	.285
37	resnet50-vitoimagevidnet8	.285
38	eicircuit_resnet18_bn_20260919	.285
39	AdvProp_efficientnet-b4	.285
40	deit_base_imagenet_full_seed-0	.283
41	resnet50-SIN_IN_IN	.281
42	resnet_SIN_IN_FT_IN	.281
43	resnet_50_v2	.280
44	antialiased-r50	.276
45	alexnet	.276
46	voneresnet-50	.273
47	AdvProp_efficientnet-b8	.273
48	densenet-121	.272
49	Res2Net50_26w_4s	.272
50	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.269
51	custom_model_cv_18_dagger_408	.267
52	AdvProp_efficientnet-b6	.267
53	pnasnet_large_halfPrecision	.266
54	grcnn	.265
55	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.265
56	resnet50-moclr8deg	.263
57	convnext_large_imagenet_full_seed-0	.260
58	shufflenet_v2_x1_0	.259
59	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.259
60	convnext_femto_ols:d1_in1k	.256
61	convnext_large:fb_in22k_ft_in1k	.251
62	efficientnet_b2_imagenet_full	.250
63	convnext_tiny_sup	.249
64	vit_relpos_base_patch16_clsgap_224:sw_in1k	.247
65	convnext_tiny_imagenet_full_seed-0	.246
66	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.244
67	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.243
68	AdvProp_efficientnet-b7	.241
69	resnet50_imagenet_full	.239
70	deit_large_imagenet_full_seed-0	.239
71	resnext101_32x8d_wsl	.237
72	AlexNet_SIN_fov12	.234
73	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.234
74	resnext101_32x16d_wsl	.228
75	vit_large_patch14_clip_224:openai_ft_in1k	.228
76	gabor_filter_entropy_4x4_v2_forcedCosine	.223
77	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.223
78	gabor_filter_entropy_4x4_v2	.221
79	alexnet_training_seed_10	.217
80	alexnet_training_seed_01	.216
81	resnet-50-robust	.216
82	alexnet_training_seed_07	.216
83	resnext101_32x32d_wsl	.212
84	alexnet_training_seed_03	.208
85	resnext101_32x48d_wsl	.204
86	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.203
87	alexnet_training_seed_02	.201
88	convnext_base_imagenet_full_seed-0	.200
89	vit_large_patch14_clip_224:laion2b_ft_in1k	.199
90	convnext_xlarge:fb_in22k_ft_in1k	.197
91	AlexNet_SIN_fov16	.197
92	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.190
93	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.189
94	alexnet_training_seed_09	.188
95	alexnet_training_seed_06	.186
96	convnext_small_imagenet_full_seed-0	.184
97	AT_efficientnet-b2	.180
98	AdvProp_efficientnet-b2	.180
99	convnext_tiny:in12k_ft_in1k	.171
100	convnext_base:clip_laiona_augreg_ft_in1k_384	.169
101	vit_base_patch16_clip_224:openai_ft_in1k	.162
102	efficientnet_b0	.162
103	gabor_filter_entropy_4x4	.141
104	ViT_L_32_imagenet1k	.060
105	gabor_filter_mean_abs_4x4_v2	.051
106	hmax	.036
107	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

0.75.

Note that scores are relative to this ceiling.

Data: Li2026.V2

Metric: ridgecv