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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	dinov2_vitl14_lc	.822
3	antialiased-r50	.821
4	voneresnet-50	.820
5	dinov2_vitl14_reg_lc	.819
6	dinov2_vitl14_reg_lc_featread	.819
7	resnet-50-robust	.817
8	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.817
9	resnet_50_v1_spiking	.809
10	resnet_50_v1	.809
11	resnet50_tutorial	.809
12	resnet50-sup	.809
13	convnext_base:clip_laiona_augreg_ft_in1k_384	.809
14	vit_large_patch14_clip_224:openai_ft_in1k	.809
15	resnet50-SIN_IN_IN	.808
16	AdvProp_efficientnet-b8	.806
17	resnet_SIN_IN_FT_IN	.804
18	resnet50-SIN	.803
19	focalnet_tiny_lrf_in1k	.802
20	AdvProp_efficientnet-b6	.800
21	densenet-121	.799
22	mobilenet_v2_1_4_224	.796
23	cvt_cvt-13-384-in1k_4_LucyV4	.794
24	convnext_xxlarge_clip	.794
25	cvt_cvt-21-384-in1k_4_LucyV4	.794
26	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.793
27	grcnn	.792
28	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.790
29	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.789
30	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.786
31	AdvProp_efficientnet-b4	.785
32	AdvProp_efficientnet-b7	.783
33	resnext101_32x16d_wsl	.782
34	efficientnet_b0	.780
35	resnet50-moclr8deg	.778
36	vit_base_patch16_clip_224:openai_ft_in1k	.778
37	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.778
38	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.777
39	convnext_tiny_sup	.777
40	convnext_small_imagenet_full_seed-0	.772
41	resnext101_32x48d_wsl	.770
42	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.770
43	efficientnet_b2_imagenet_full	.768
44	ReAlnet03_cornet	.767
45	ReAlnet01_cornet	.765
46	ReAlnet04_cornet	.765
47	shufflenet_v2_x1_0	.765
48	compact_resnet50_robust_V4	.763
49	AlexNet_SIN	.761
50	cvt_cvt-13-224-in1k_4_LucyV4	.762
51	alexnet	.757
52	AT_efficientnet-b2	.757
53	AdvProp_efficientnet-b2	.757
54	deit_large_imagenet_full_seed-0	.757
55	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.755
56	deit_small_imagenet_full_seed-0	.754
57	cvt_cvt-21-224-in1k_4_LucyV4	.752
58	ViT_L_32_imagenet1k	.748
59	convnext_femto_ols:d1_in1k	.747
60	custom_model_cv_18_dagger_408	.745
61	vit_relpos_base_patch16_clsgap_224:sw_in1k	.745
62	AlexNet_SIN_fov12	.735
63	resnet152_imagenet_full	.727
64	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.726
65	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.726
66	effnetb1_272x240	.716
67	alexnet_training_seed_10	.711
68	alexnet_training_seed_09	.711
69	alexnet_training_seed_01	.709
70	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.707
71	convnext_base_imagenet_full_seed-0	.702
72	alexnet_training_seed_02	.700
73	alexnet_training_seed_07	.698
74	AlexNet_SIN_fov16	.663
75	resnet50_imagenet_full	.642
76	convnext_large_imagenet_full_seed-0	.625
77	convnext_tiny_imagenet_full_seed-0	.612
78	alexnet_training_seed_06	.605
79	alexnet_training_seed_03	.564
80	convnext_tiny:in12k_ft_in1k	.554
81	predictive_coding_pc	.439
82	pr1_stl10_disentangled_alpha04	.407
83	convnext_base_fb_in1k	.364
84	ReAlnet01_cornet_fov4	.277
85	hmax	.275
86	vit_huge_clip336_laion
87	vit_large_clip336_openai
88	dinov2_vitl14_lc_res336
89	eva02_large_in22k
90	dinov2_vitg14_lc
91	dinov2_vitg14_reg_lc
92	resnet18_fair
93	resnet50_st_wzc_seed3_2_323
94	resnet50_ewconplus_wzc_seed5_2__323
95	resnet50_ewconplus_wzc_seed6_2_323
96	resnet50_st_wzc_seed7_2_323
97	resnet50_st_wzc_seed6_2_323
98	resnet50_ewconplus_wzc_seed2_2__323
99	resnext101_32x32d_wsl
100	vit_large_patch14_clip_336:laion2b_ft_in1k
101	vit_large_patch14_clip_336:openai_ft_in12k_in1k
102	cvt_cvt-w24-384-in22k_finetuned-in1k_4
103	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
104	vit_large_patch14_clip_224:laion2b_ft_in1k
105	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k
106	voneresnet-50-non_stochastic
107	VOneCORnet-S
108	pnasnet_large_halfPrecision
109	resnet50-vitoimagevidnet8
110	resnext101_32x8d_wsl
111	resnet50-VITO-8deg-cc
112	resnet_50_v2
113	resnet50-SIN_IN
114	Res2Net50_26w_4s
115	resnet50_ewconplus_wzc
116	convnext_xlarge:fb_in22k_ft_in1k
117	convnext_large:fb_in22k_ft_in1k
118	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