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Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.498
2	resnet50_robust_l2_eps1	.464
3	alexnet	.464
4	mobilenet_v2_1_4_224	.459
5	VOneCORnet-S	.458
6	resnet50_ewconplus_wzc	.451
7	convnext_small_imagenet_full_seed-0	.448
8	deit_small_imagenet_full_seed-0	.443
9	voneresnet-50	.440
10	convnext_tiny:in12k_ft_in1k	.439
11	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.436
12	convnext_large:fb_in22k_ft_in1k	.431
13	convnext_tiny_imagenet_full_seed-0	.431
14	resnet50-moclr8deg	.431
15	voneresnet-50-non_stochastic	.423
16	convnext_base_imagenet_full_seed-0	.423
17	resnet50_imagenet_full	.422
18	AlexNet_SIN	.410
19	resnet50-VITO-8deg-cc	.407
20	resnet50-vitoimagevidnet8	.407
21	resnet-50-robust	.407
22	antialiased-r50	.407
23	eicircuit_resnet18_bn_20260919	.403
24	convnext_xlarge:fb_in22k_ft_in1k	.402
25	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.401
26	resnet50-SIN_IN	.399
27	efficientnet_b0	.397
28	AT_efficientnet-b2	.396
29	AdvProp_efficientnet-b2	.396
30	effnetb1_272x240	.393
31	ReAlnet01_cornet	.393
32	deit_base_imagenet_full_seed-0	.391
33	AdvProp_efficientnet-b8	.391
34	ViT_L_32_imagenet1k	.391
35	ReAlnet04_cornet	.391
36	ReAlnet03_cornet	.389
37	vit_large_patch14_clip_224:openai_ft_in1k	.381
38	convnext_large_imagenet_full_seed-0	.379
39	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.375
40	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.375
41	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.374
42	custom_model_cv_18_dagger_408	.370
43	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.367
44	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.366
45	deit_large_imagenet_full_seed-0	.364
46	ReAlnet01_cornet_fov4	.363
47	convnext_base:clip_laiona_augreg_ft_in1k_384	.362
48	resnet_SIN_IN_FT_IN	.362
49	AdvProp_efficientnet-b7	.359
50	vit_large_patch14_clip_224:laion2b_ft_in1k	.355
51	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.354
52	AdvProp_efficientnet-b4	.354
53	resnet50-SIN_IN_IN	.353
54	vit_base_patch16_clip_224:openai_ft_in1k	.352
55	resnet50-SIN	.350
56	convnext_femto_ols:d1_in1k	.348
57	cvt_cvt-21-384-in1k_4_LucyV4	.346
58	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.345
59	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.344
60	densenet-121	.342
61	shufflenet_v2_x1_0	.342
62	grcnn	.339
63	AdvProp_efficientnet-b6	.334
64	resnet50-sup	.334
65	resnet_50_v1_spiking	.334
66	resnet_50_v1	.334
67	vit_large_patch14_clip_336:laion2b_ft_in1k	.331
68	vit_relpos_base_patch16_clsgap_224:sw_in1k	.312
69	AlexNet_SIN_fov16	.293
70	dinov2_vitl14_lc	.276
71	pnasnet_large_halfPrecision	.267
72	alexnet_training_seed_02	.266
73	AlexNet_SIN_fov12	.262
74	cvt_cvt-21-224-in1k_4_LucyV4	.261
75	alexnet_training_seed_01	.251
76	alexnet_training_seed_03	.240
77	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.238
78	alexnet_training_seed_06	.225
79	gabor_filter_entropy_4x4_v2_forcedCosine	.224
80	alexnet_training_seed_07	.222
81	Res2Net50_26w_4s	.213
82	cvt_cvt-13-224-in1k_4_LucyV4	.210
83	alexnet_training_seed_09	.209
84	alexnet_training_seed_10	.207
85	resnet152_imagenet_full	.196
86	hmax	.165
87	resnet_50_v2	.159
88	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.147
89	cvt_cvt-13-384-in1k_4_LucyV4	.140
90	resnet50_tutorial	.132
91	convnext_tiny_sup	.131
92	gabor_filter_entropy_4x4	.121
93	gabor_filter_entropy_4x4_v2	.107
94	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.095
95	seresnext50_32x4d	.089
96	dinov2_vitl14_reg_lc	.085
97	resnext101_32x8d_wsl	.083
98	gabor_filter_mean_abs_4x4_v2	.080
99	efficientnet_b2_imagenet_full	.048
100	compact_resnet50_robust_V4	.025
101	regnety_032	.000
102	resnext101_32x48d_wsl	.000
103	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.000
104	resnext101_32x32d_wsl	.000
105	resnext101_32x16d_wsl
106	cvt_cvt-w24-384-in22k_finetuned-in1k_4
107	focalnet_tiny_lrf_in1k

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

0.60.

Note that scores are relative to this ceiling.

Data: Li2026.IT

Metric: rdm