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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Li2026.V1-rdm")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet-50-robust	.340
2	mehrer2020_alexnet_training_seed_01_fov4	.332
3	resnet_SIN_IN_FT_IN	.323
4	AlexNet_SIN_fov4	.315
5	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.289
6	alexnet_training_seed_02	.287
7	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.281
8	resnet50_imagenet_full	.281
9	mehrer2020_alexnet_training_seed_09	.279
10	mehrer2020_alexnet_training_seed_10	.279
11	mehrer2020_alexnet_training_seed_08	.276
12	mehrer2020_alexnet_training_seed_07	.275
13	alexnet	.275
14	ReAlnet01_cornet_fov4	.272
15	mehrer2020_alexnet_training_seed_06	.271
16	mehrer2020_alexnet_training_seed_04	.269
17	mehrer2020_alexnet_training_seed_03	.268
18	mehrer2020_alexnet_training_seed_02	.264
19	compact_resnet50_robust_V4	.263
20	mehrer2020_alexnet_training_seed_05	.262
21	mehrer2020_alexnet_training_seed_01	.261
22	alexnet_training_seed_06	.260
23	AlexNet_SIN	.260
24	alexnet_training_seed_01	.254
25	convnext_base:clip_laiona_augreg_ft_in1k_384	.251
26	ReAlnet06_cornet	.251
27	ReAlnet09_cornet	.248
28	alexnet_training_seed_09	.248
29	ReAlnet02_cornet	.246
30	ReAlnet08_cornet	.246
31	ReAlnet10_cornet	.244
32	ReAlnet03_cornet	.242
33	ReAlnet07_cornet	.242
34	ReAlnet05_cornet	.241
35	vit_large_patch14_clip_224:laion2b_ft_in1k	.240
36	grcnn	.239
37	alexnet_training_seed_03	.238
38	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.237
39	convnext_femto_ols:d1_in1k	.236
40	ReAlnet01_cornet	.235
41	resnet50-SIN_IN	.235
42	ReAlnet04_cornet	.234
43	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.234
44	gabor_filter_entropy_4x4_v2_forcedCosine	.233
45	pnasnet_large_halfPrecision	.232
46	eicircuit_resnet18_bn_20260919	.232
47	deit_base_imagenet_full_seed-0	.230
48	resnet152_imagenet_full	.223
49	alexnet_training_seed_07	.222
50	voneresnet-50	.221
51	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.221
52	deit_large_imagenet_full_seed-0	.215
53	cvt_cvt-13-224-in1k_4_LucyV4	.214
54	mobilenet_v2_1_4_224	.211
55	alexnet_training_seed_10	.208
56	resnet50_imagenet_100_seed-0	.208
57	AlexNet_SIN_fov16	.206
58	AlexNet_SIN_fov12	.202
59	convnext_small_imagenet_100_seed-0	.196
60	resnet50-SIN	.194
61	deit_small_imagenet_full_seed-0	.191
62	mehrer2020_alexnet_training_seed_01_fov12	.190
63	ReAlnet01_cornet_fov12	.187
64	resnet18_ecoset_full	.187
65	mehrer2020_alexnet_training_seed_01_fov16	.186
66	efficientnet_b0	.177
67	resnet50_ewconplus_wzc	.173
68	resnet152_ecoset_full	.168
69	ReAlnet01_cornet_fov16	.167
70	resnet50_ecoset_full	.166
71	AdvProp_efficientnet-b6	.164
72	convnext_small_imagenet_full_seed-0	.162
73	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.160
74	resnet101_imagenet_full	.157
75	densenet-121	.156
76	resnet101_ecoset_full	.154
77	Res2Net50_26w_4s	.153
78	antialiased-r50	.153
79	AdvProp_efficientnet-b4	.150
80	resnet34_imagenet_full	.147
81	ijepa_vith14_linhead	.143
82	convnext_tiny_sup	.141
83	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.135
84	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.135
85	resnet18_imagenet_full	.131
86	resnet50-vitoimagevidnet8	.129
87	resnet50-VITO-8deg-cc	.129
88	vit_large_patch14_clip_224:openai_ft_in1k	.126
89	AdvProp_efficientnet-b8	.126
90	dinov2_vitl14_lc	.117
91	AdvProp_efficientnet-b7	.117
92	resnet34_ecoset_full	.117
93	resnet50-SIN_IN_IN	.115
94	convnext_xlarge:fb_in22k_ft_in1k	.115
95	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.113
96	focalnet_tiny_lrf_in1k	.113
97	resnet50_robust_l2_eps1	.107
98	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.107
99	ViT_L_32_imagenet1k	.104
100	deit_small_imagenet_100_seed-0	.104
101	convnext_tiny_imagenet_full_seed-0	.104
102	vit_large_patch14_clip_336:laion2b_ft_in1k	.102
103	resnet50_tutorial	.098
104	resnet50-sup	.098
105	resnet_50_v1	.098
106	resnet_50_v1_spiking	.098
107	AT_efficientnet-b2	.097
108	AdvProp_efficientnet-b2	.097
109	convnext_base_imagenet_full_seed-0	.096
110	effnetb1_272x240	.092
111	cvt_cvt-21-224-in1k_4_LucyV4	.092
112	cvt_cvt-21-384-in1k_4_LucyV4	.089
113	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.087
114	custom_model_cv_18_dagger_408	.079
115	efficientnet_b1_imagenet_full	.071
116	convnext_large_imagenet_full_seed-0	.071
117	convnext_large:fb_in22k_ft_in1k	.070
118	convnext_tiny:in12k_ft_in1k	.069
119	voneresnet-50-non_stochastic	.064
120	cvt_cvt-13-384-in1k_4_LucyV4	.062
121	vit_base_patch16_clip_224:openai_ft_in1k	.058
122	resnet50_simsiam_linhead	.055
123	resnet50-moclr8deg	.054
124	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.051
125	shufflenet_v2_x1_0	.050
126	efficientnet_b0_imagenet_full	.048
127	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.043
128	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.040
129	vit_relpos_base_patch16_clsgap_224:sw_in1k	.038
130	resnext101_32x8d_wsl	.032
131	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.027
132	resnet50_barlowtwins_linhead	.025
133	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.014
134	VOneCORnet-S	.014
135	efficientnet_b2_imagenet_full	.010
136	regnety_032	.004
137	resnet50_vicreg_linhead	.002
138	resnet50_byol_linhead	.002
139	resnet50_simclr_linhead_01norm	.000
140	resnet_50_v2	.000
141	resnext101_32x32d_wsl	.000
142	resnext101_32x48d_wsl	.000
143	gabor_filter_mean_abs_4x4_v2	.000
144	resnext101_32x16d_wsl	.000
145	gabor_filter_entropy_4x4	.000
146	gabor_filter_entropy_4x4_v2	.000
147	dinov2_vitl14_reg_lc	.000
148	hmax	.000
149	seresnext50_32x4d

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

0.82.

Note that scores are relative to this ceiling.

Data: Li2026.V1

Metric: rdm