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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Papale2025.V1-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	.827
2	antialiased-r50	.821
3	voneresnet-50	.820
4	resnet-50-robust	.817
5	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.817
6	resnet_50_v1_spiking	.809
7	resnet_50_v1	.809
8	resnet50_tutorial	.809
9	resnet50-sup	.809
10	convnext_base:clip_laiona_augreg_ft_in1k_384	.809
11	vit_large_patch14_clip_224:openai_ft_in1k	.809
12	resnet50-SIN_IN_IN	.808
13	AdvProp_efficientnet-b8	.806
14	resnet_SIN_IN_FT_IN	.804
15	resnet50-SIN	.803
16	focalnet_tiny_lrf_in1k	.802
17	AdvProp_efficientnet-b6	.800
18	densenet-121	.799
19	mobilenet_v2_1_4_224	.796
20	cvt_cvt-13-384-in1k_4_LucyV4	.794
21	cvt_cvt-21-384-in1k_4_LucyV4	.794
22	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.793
23	grcnn	.792
24	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.790
25	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.789
26	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.786
27	AdvProp_efficientnet-b4	.785
28	AdvProp_efficientnet-b7	.783
29	resnext101_32x16d_wsl	.782
30	efficientnet_b0	.780
31	resnet50-moclr8deg	.778
32	vit_base_patch16_clip_224:openai_ft_in1k	.778
33	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.778
34	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.777
35	convnext_tiny_sup	.777
36	convnext_small_imagenet_full_seed-0	.772
37	resnext101_32x48d_wsl	.770
38	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.770
39	efficientnet_b2_imagenet_full	.768
40	ReAlnet03_cornet	.767
41	ReAlnet01_cornet	.765
42	ReAlnet04_cornet	.765
43	shufflenet_v2_x1_0	.765
44	compact_resnet50_robust_V4	.763
45	AlexNet_SIN	.761
46	cvt_cvt-13-224-in1k_4_LucyV4	.762
47	alexnet	.757
48	AT_efficientnet-b2	.757
49	AdvProp_efficientnet-b2	.757
50	deit_large_imagenet_full_seed-0	.757
51	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.755
52	deit_small_imagenet_full_seed-0	.754
53	cvt_cvt-21-224-in1k_4_LucyV4	.752
54	ViT_L_32_imagenet1k	.748
55	convnext_femto_ols:d1_in1k	.747
56	custom_model_cv_18_dagger_408	.745
57	vit_relpos_base_patch16_clsgap_224:sw_in1k	.745
58	AlexNet_SIN_fov12	.735
59	resnet152_imagenet_full	.727
60	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.726
61	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.726
62	effnetb1_272x240	.716
63	alexnet_training_seed_10	.711
64	alexnet_training_seed_09	.711
65	alexnet_training_seed_01	.709
66	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.707
67	convnext_base_imagenet_full_seed-0	.702
68	alexnet_training_seed_02	.700
69	alexnet_training_seed_07	.698
70	AlexNet_SIN_fov16	.663
71	resnet50_imagenet_full	.642
72	convnext_large_imagenet_full_seed-0	.625
73	convnext_tiny_imagenet_full_seed-0	.612
74	alexnet_training_seed_06	.605
75	alexnet_training_seed_03	.564
76	convnext_tiny:in12k_ft_in1k	.554
77	predictive_coding_pc	.439
78	pr1_stl10_disentangled_alpha04	.407
79	convnext_base_fb_in1k	.364
80	ReAlnet01_cornet_fov4	.277
81	hmax	.275
82	resnet18_fair
83	resnet50_st_wzc_seed3_2_323
84	resnet50_st_wzc_seed5_2_323
85	resnet50_st_wzc_seed4_2_323
86	resnet50_st_wzc_noseed_2_323
87	resnet50_st_wzc_2_323
88	resnet50_ewconplus_wzc_seed7_2_323
89	resnet50_ewconplus_wzc_2_323
90	resnet50_ewconplus_wzc_seed5_2__323
91	resnet50_ewconplus_wzc_seed6_2_323
92	resnet50_ewconplus_wzc_seed4_2_323
93	resnet50_st_wzc_seed7_2_323
94	resnet50_st_wzc_seed6_2_323
95	resnet50_ewconplus_wzc_seed3_2_323
96	resnet50_ewconplus_wzc_seed2_2__323
97	resnext101_32x32d_wsl
98	vit_large_patch14_clip_336:laion2b_ft_in1k
99	vit_large_patch14_clip_336:openai_ft_in12k_in1k
100	cvt_cvt-w24-384-in22k_finetuned-in1k_4
101	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
102	vit_large_patch14_clip_224:laion2b_ft_in1k
103	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k
104	voneresnet-50-non_stochastic
105	VOneCORnet-S
106	pnasnet_large_halfPrecision
107	resnet50-vitoimagevidnet8
108	resnext101_32x8d_wsl
109	resnet50-VITO-8deg-cc
110	resnet_50_v2
111	resnet50-SIN_IN
112	Res2Net50_26w_4s
113	resnet50_ewconplus_wzc
114	convnext_xlarge:fb_in22k_ft_in1k
115	convnext_large:fb_in22k_ft_in1k
116	deit_base_imagenet_full_seed-0

Benchmark bibtex

@article{papale_extensive_2025,
	title = {An extensive dataset of spiking activity to reveal the syntax of the ventral stream},
	volume = {113},
	issn = {08966273},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S089662732400881X},
	doi = {10.1016/j.neuron.2024.12.003},
	journal = {Neuron},
	author = {Papale, Paolo and Wang, Feng and Self, Matthew W. and Roelfsema, Pieter R.},
	year = {2025},
}

Ceiling

0.98.

Note that scores are relative to this ceiling.

Data: Papale2025.V1

Metric: ridgecv