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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	convnext_xlarge:fb_in22k_ft_in1k	.402
24	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.401
25	resnet50-SIN_IN	.399
26	efficientnet_b0	.397
27	AT_efficientnet-b2	.396
28	AdvProp_efficientnet-b2	.396
29	effnetb1_272x240	.393
30	ReAlnet01_cornet	.393
31	deit_base_imagenet_full_seed-0	.391
32	AdvProp_efficientnet-b8	.391
33	ViT_L_32_imagenet1k	.391
34	ReAlnet04_cornet	.391
35	ReAlnet03_cornet	.389
36	vit_large_patch14_clip_224:openai_ft_in1k	.381
37	convnext_large_imagenet_full_seed-0	.379
38	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.375
39	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.375
40	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.374
41	custom_model_cv_18_dagger_408	.370
42	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.367
43	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.366
44	deit_large_imagenet_full_seed-0	.364
45	ReAlnet01_cornet_fov4	.363
46	convnext_base:clip_laiona_augreg_ft_in1k_384	.362
47	resnet_SIN_IN_FT_IN	.362
48	AdvProp_efficientnet-b7	.359
49	vit_large_patch14_clip_224:laion2b_ft_in1k	.355
50	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.354
51	AdvProp_efficientnet-b4	.354
52	resnet50-SIN_IN_IN	.353
53	vit_base_patch16_clip_224:openai_ft_in1k	.352
54	resnet50-SIN	.350
55	convnext_femto_ols:d1_in1k	.348
56	cvt_cvt-21-384-in1k_4_LucyV4	.346
57	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.345
58	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.344
59	densenet-121	.342
60	shufflenet_v2_x1_0	.342
61	grcnn	.339
62	AdvProp_efficientnet-b6	.334
63	resnet50-sup	.334
64	resnet_50_v1_spiking	.334
65	resnet_50_v1	.334
66	vit_large_patch14_clip_336:laion2b_ft_in1k	.331
67	vit_relpos_base_patch16_clsgap_224:sw_in1k	.312
68	AlexNet_SIN_fov16	.293
69	dinov2_vitl14_lc	.276
70	pnasnet_large_halfPrecision	.267
71	alexnet_training_seed_02	.266
72	AlexNet_SIN_fov12	.262
73	cvt_cvt-21-224-in1k_4_LucyV4	.261
74	alexnet_training_seed_01	.251
75	alexnet_training_seed_03	.240
76	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.238
77	alexnet_training_seed_06	.225
78	alexnet_training_seed_07	.222
79	Res2Net50_26w_4s	.213
80	cvt_cvt-13-224-in1k_4_LucyV4	.210
81	alexnet_training_seed_09	.209
82	alexnet_training_seed_10	.207
83	resnet152_imagenet_full	.196
84	hmax	.165
85	resnet_50_v2	.159
86	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.147
87	cvt_cvt-13-384-in1k_4_LucyV4	.140
88	resnet50_tutorial	.132
89	convnext_tiny_sup	.131
90	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.095
91	dinov2_vitl14_reg_lc	.085
92	resnext101_32x8d_wsl	.083
93	efficientnet_b2_imagenet_full	.048
94	compact_resnet50_robust_V4	.025
95	resnext101_32x48d_wsl	.000
96	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.000
97	resnext101_32x32d_wsl	.000
98	resnext101_32x16d_wsl
99	cvt_cvt-w24-384-in22k_finetuned-in1k_4
100	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