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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	resnet50_imagenet_full	.251
21	deit_base_imagenet_full_seed-0	.250
22	resnet50-vitoimagevidnet8	.250
23	resnet50-VITO-8deg-cc	.250
24	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.250
25	vit_relpos_base_patch16_clsgap_224:sw_in1k	.248
26	AT_efficientnet-b2	.248
27	AdvProp_efficientnet-b2	.248
28	convnext_base:clip_laiona_augreg_ft_in1k_384	.247
29	deit_large_imagenet_full_seed-0	.247
30	Res2Net50_26w_4s	.247
31	convnext_femto_ols:d1_in1k	.247
32	antialiased-r50	.247
33	deit_small_imagenet_full_seed-0	.247
34	focalnet_tiny_lrf_in1k	.246
35	resnet_50_v1_spiking	.246
36	resnet_50_v1	.246
37	resnet50-sup	.246
38	alexnet	.245
39	efficientnet_b0	.245
40	convnext_small_imagenet_full_seed-0	.244
41	resnet50_tutorial	.244
42	convnext_tiny_imagenet_full_seed-0	.243
43	vit_base_patch16_clip_224:openai_ft_in1k	.242
44	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.242
45	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.242
46	resnet152_imagenet_full	.241
47	dinov2_vitl14_lc	.241
48	mobilenet_v2_1_4_224	.240
49	AdvProp_efficientnet-b7	.240
50	effnetb1_272x240	.240
51	vit_large_patch14_clip_336:laion2b_ft_in1k	.239
52	resnext101_32x16d_wsl	.239
53	vit_large_patch14_clip_224:openai_ft_in1k	.239
54	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.239
55	efficientnet_b2_imagenet_full	.239
56	resnet_50_v2	.239
57	resnet50-SIN	.238
58	resnext101_32x48d_wsl	.238
59	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.238
60	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.237
61	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.237
62	resnet-50-robust	.237
63	resnext101_32x32d_wsl	.234
64	resnet_SIN_IN_FT_IN	.234
65	vit_large_patch14_clip_224:laion2b_ft_in1k	.232
66	convnext_xlarge:fb_in22k_ft_in1k	.226
67	dinov2_vitl14_reg_lc	.224
68	pnasnet_large_halfPrecision	.222
69	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.220
70	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.218
71	AlexNet_SIN	.216
72	cvt_cvt-13-224-in1k_4_LucyV4	.213
73	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.213
74	ReAlnet01_cornet_fov4	.212
75	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.210
76	resnext101_32x8d_wsl	.208
77	convnext_tiny_sup	.208
78	cvt_cvt-13-384-in1k_4_LucyV4	.203
79	cvt_cvt-21-384-in1k_4_LucyV4	.199
80	grcnn	.198
81	cvt_cvt-21-224-in1k_4_LucyV4	.194
82	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.191
83	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.191
84	ViT_L_32_imagenet1k	.190
85	AlexNet_SIN_fov12	.179
86	AlexNet_SIN_fov16	.154
87	alexnet_training_seed_02	.055
88	alexnet_training_seed_03	.054
89	alexnet_training_seed_01	.054
90	alexnet_training_seed_07	.053
91	alexnet_training_seed_10	.053
92	alexnet_training_seed_09	.050
93	alexnet_training_seed_06	.048
94	hmax	.047
95	compact_resnet50_robust_V4	.032
96	vit_large_patch14_clip_336:openai_ft_in12k_in1k
97	cvt_cvt-w24-384-in22k_finetuned-in1k_4
98	AdvProp_efficientnet-b4
99	AdvProp_efficientnet-b8
100	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