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

0.75.

Note that scores are relative to this ceiling.

Data: Li2026.V2

Metric: ridgecv