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Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet50_robust_l2_eps1	.275
2	resnet50-moclr8deg	.268
3	shufflenet_v2_x1_0	.266
4	convnext_tiny:in12k_ft_in1k	.265
5	custom_model_cv_18_dagger_408	.264
6	resnet50_ewconplus_wzc	.263
7	VOneCORnet-S	.262
8	convnext_base_imagenet_full_seed-0	.261
9	convnext_large_imagenet_full_seed-0	.259
10	ReAlnet03_cornet	.258
11	voneresnet-50	.258
12	ReAlnet01_cornet	.258
13	convnext_large:fb_in22k_ft_in1k	.257
14	densenet-121	.256
15	resnet50-SIN_IN	.256
16	ReAlnet04_cornet	.255
17	voneresnet-50-non_stochastic	.254
18	resnet50-SIN_IN_IN	.253
19	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.253
20	seresnext50_32x4d	.252
21	resnet50_imagenet_full	.251
22	deit_base_imagenet_full_seed-0	.250
23	resnet50-vitoimagevidnet8	.250
24	resnet50-VITO-8deg-cc	.250
25	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.250
26	vit_relpos_base_patch16_clsgap_224:sw_in1k	.248
27	AT_efficientnet-b2	.248
28	AdvProp_efficientnet-b2	.248
29	convnext_base:clip_laiona_augreg_ft_in1k_384	.247
30	deit_large_imagenet_full_seed-0	.247
31	Res2Net50_26w_4s	.247
32	convnext_femto_ols:d1_in1k	.247
33	antialiased-r50	.247
34	deit_small_imagenet_full_seed-0	.247
35	focalnet_tiny_lrf_in1k	.246
36	resnet_50_v1_spiking	.246
37	resnet_50_v1	.246
38	resnet50-sup	.246
39	alexnet	.245
40	efficientnet_b0	.245
41	convnext_small_imagenet_full_seed-0	.244
42	resnet50_tutorial	.244
43	convnext_tiny_imagenet_full_seed-0	.243
44	vit_base_patch16_clip_224:openai_ft_in1k	.242
45	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.242
46	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.242
47	resnet152_imagenet_full	.241
48	dinov2_vitl14_lc	.241
49	mobilenet_v2_1_4_224	.240
50	AdvProp_efficientnet-b7	.240
51	effnetb1_272x240	.240
52	vit_large_patch14_clip_336:laion2b_ft_in1k	.239
53	resnext101_32x16d_wsl	.239
54	vit_large_patch14_clip_224:openai_ft_in1k	.239
55	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.239
56	efficientnet_b2_imagenet_full	.239
57	resnet_50_v2	.239
58	resnet50-SIN	.238
59	resnext101_32x48d_wsl	.238
60	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.238
61	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.237
62	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.237
63	resnet-50-robust	.237
64	eicircuit_resnet18_bn_20260919	.237
65	resnext101_32x32d_wsl	.234
66	resnet_SIN_IN_FT_IN	.234
67	vit_large_patch14_clip_224:laion2b_ft_in1k	.232
68	convnext_xlarge:fb_in22k_ft_in1k	.226
69	dinov2_vitl14_reg_lc	.224
70	regnety_032	.223
71	pnasnet_large_halfPrecision	.222
72	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.220
73	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.218
74	AlexNet_SIN	.216
75	cvt_cvt-13-224-in1k_4_LucyV4	.213
76	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.213
77	ReAlnet01_cornet_fov4	.212
78	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.210
79	resnext101_32x8d_wsl	.208
80	convnext_tiny_sup	.208
81	cvt_cvt-13-384-in1k_4_LucyV4	.203
82	cvt_cvt-21-384-in1k_4_LucyV4	.199
83	grcnn	.198
84	cvt_cvt-21-224-in1k_4_LucyV4	.194
85	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.191
86	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.191
87	ViT_L_32_imagenet1k	.190
88	AlexNet_SIN_fov12	.179
89	AlexNet_SIN_fov16	.154
90	gabor_filter_entropy_4x4_v2	.055
91	alexnet_training_seed_02	.055
92	gabor_filter_entropy_4x4	.054
93	gabor_filter_entropy_4x4_v2_forcedCosine	.054
94	alexnet_training_seed_03	.054
95	alexnet_training_seed_01	.054
96	alexnet_training_seed_07	.053
97	alexnet_training_seed_10	.053
98	alexnet_training_seed_09	.050
99	alexnet_training_seed_06	.048
100	hmax	.047
101	gabor_filter_mean_abs_4x4_v2	.039
102	compact_resnet50_robust_V4	.032
103	vit_large_patch14_clip_336:openai_ft_in12k_in1k
104	cvt_cvt-w24-384-in22k_finetuned-in1k_4
105	AdvProp_efficientnet-b4
106	AdvProp_efficientnet-b8
107	AdvProp_efficientnet-b6

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

0.64.

Note that scores are relative to this ceiling.

Data: Li2026.IT

Metric: ridgecv