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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Li2026.V4-rdm")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	AdvProp_efficientnet-b8	.304
2	AdvProp_efficientnet-b4	.288
3	mehrer2020_alexnet_training_seed_03	.272
4	mehrer2020_alexnet_training_seed_02	.270
5	mehrer2020_alexnet_training_seed_05	.268
6	mehrer2020_alexnet_training_seed_09	.265
7	mehrer2020_alexnet_training_seed_04	.264
8	mehrer2020_alexnet_training_seed_08	.263
9	mehrer2020_alexnet_training_seed_01	.263
10	mehrer2020_alexnet_training_seed_07	.262
11	mobilenet_v2_1_4_224	.258
12	mehrer2020_alexnet_training_seed_06	.257
13	AdvProp_efficientnet-b7	.251
14	efficientnet_b0	.248
15	effnetb1_272x240	.241
16	AdvProp_efficientnet-b6	.238
17	vit_relpos_base_patch16_clsgap_224:sw_in1k	.234
18	AlexNet_SIN_fov16	.227
19	convnext_large:fb_in22k_ft_in1k	.227
20	alexnet	.225
21	AlexNet_SIN	.224
22	AlexNet_SIN_fov12	.221
23	deit_base_imagenet_full_seed-0	.220
24	gabor_filter_entropy_4x4_v2_forcedCosine	.202
25	AT_efficientnet-b2	.197
26	AdvProp_efficientnet-b2	.197
27	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.195
28	resnet152_imagenet_full	.194
29	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.194
30	pnasnet_large_halfPrecision	.191
31	resnet101_imagenet_full	.186
32	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.186
33	mehrer2020_alexnet_training_seed_01_fov16	.185
34	resnet152_ecoset_full	.183
35	convnext_tiny_imagenet_full_seed-0	.182
36	voneresnet-50	.182
37	convnext_base_imagenet_full_seed-0	.180
38	convnext_small_imagenet_full_seed-0	.180
39	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.180
40	gabor_filter_entropy_4x4	.173
41	ReAlnet06_cornet	.171
42	ReAlnet07_cornet	.170
43	mehrer2020_alexnet_training_seed_01_fov4	.169
44	vit_large_patch14_clip_336:laion2b_ft_in1k	.169
45	resnet101_ecoset_full	.168
46	ReAlnet01_cornet	.167
47	convnext_xlarge:fb_in22k_ft_in1k	.162
48	ReAlnet04_cornet	.161
49	ReAlnet02_cornet	.161
50	convnext_large_imagenet_full_seed-0	.160
51	ReAlnet03_cornet	.160
52	gabor_filter_entropy_4x4_v2	.160
53	alexnet_training_seed_02	.159
54	ReAlnet08_cornet	.159
55	resnet50_ewconplus_wzc	.157
56	alexnet_training_seed_06	.157
57	ReAlnet10_cornet	.157
58	convnext_tiny_sup	.156
59	mehrer2020_alexnet_training_seed_01_fov12	.155
60	mehrer2020_alexnet_training_seed_10	.155
61	ReAlnet09_cornet	.155
62	resnet-50-robust	.155
63	ReAlnet05_cornet	.153
64	alexnet_training_seed_03	.153
65	hmax	.148
66	ReAlnet01_cornet_fov16	.147
67	custom_model_cv_18_dagger_408	.142
68	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.140
69	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.140
70	convnext_small_imagenet_100_seed-0	.137
71	deit_small_imagenet_full_seed-0	.133
72	alexnet_training_seed_01	.132
73	ViT_L_32_imagenet1k	.132
74	ReAlnet01_cornet_fov12	.126
75	resnet50-SIN	.125
76	resnet50-moclr8deg	.120
77	cvt_cvt-13-224-in1k_4_LucyV4	.118
78	cvt_cvt-21-224-in1k_4_LucyV4	.113
79	AlexNet_SIN_fov4	.112
80	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.111
81	densenet-121	.108
82	grcnn	.105
83	VOneCORnet-S	.103
84	alexnet_training_seed_09	.100
85	alexnet_training_seed_07	.096
86	dinov2_vitl14_lc	.084
87	focalnet_tiny_lrf_in1k	.080
88	resnet50_byol_linhead	.079
89	convnext_base:clip_laiona_augreg_ft_in1k_384	.079
90	ReAlnet01_cornet_fov4	.071
91	vit_large_patch14_clip_224:openai_ft_in1k	.068
92	gabor_filter_mean_abs_4x4_v2	.068
93	ijepa_vith14_linhead	.067
94	vit_base_patch16_clip_224:openai_ft_in1k	.060
95	resnet50_imagenet_100_seed-0	.057
96	efficientnet_b0_imagenet_full	.056
97	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.055
98	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.054
99	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.054
100	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.054
101	alexnet_training_seed_10	.053
102	cvt_cvt-13-384-in1k_4_LucyV4	.046
103	resnet50-vitoimagevidnet8	.031
104	resnet50-VITO-8deg-cc	.031
105	deit_small_imagenet_100_seed-0	.030
106	Res2Net50_26w_4s	.029
107	efficientnet_b2_imagenet_full	.023
108	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.018
109	cvt_cvt-21-384-in1k_4_LucyV4	.018
110	convnext_femto_ols:d1_in1k	.017
111	resnet34_ecoset_full	.013
112	deit_large_imagenet_full_seed-0	.007
113	compact_resnet50_robust_V4	.000
114	resnet18_ecoset_full	.000
115	seresnext50_32x4d	.000
116	resnet50-SIN_IN	.000
117	eicircuit_resnet18_bn_20260919	.000
118	resnet18_imagenet_full	.000
119	efficientnet_b1_imagenet_full	.000
120	vit_large_patch14_clip_224:laion2b_ft_in1k	.000
121	resnet34_imagenet_full	.000
122	voneresnet-50-non_stochastic	.000
123	resnet50_imagenet_full	.000
124	resnet50_simclr_linhead_01norm	.000
125	antialiased-r50	.000
126	resnet50_barlowtwins_linhead	.000
127	resnet50_vicreg_linhead	.000
128	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.000
129	resnet50_robust_l2_eps1	.000
130	resnet50_simsiam_linhead	.000
131	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.000
132	resnet50-SIN_IN_IN	.000
133	resnet_SIN_IN_FT_IN	.000
134	dinov2_vitl14_reg_lc	.000
135	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.000
136	resnet50_ecoset_full	.000
137	resnet_50_v1_spiking	.000
138	resnet50_tutorial	.000
139	resnet50-sup	.000
140	resnet_50_v1	.000
141	regnety_032	.000
142	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.000
143	resnext101_32x32d_wsl	.000
144	shufflenet_v2_x1_0	.000
145	resnext101_32x48d_wsl	.000
146	resnext101_32x16d_wsl	.000
147	resnext101_32x8d_wsl	.000
148	convnext_tiny:in12k_ft_in1k	.000
149	resnet_50_v2	.000

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

0.78.

Note that scores are relative to this ceiling.

Data: Li2026.V4

Metric: rdm