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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	regnety_032	.793
28	grcnn	.792
29	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.790
30	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.789
31	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.786
32	AdvProp_efficientnet-b4	.785
33	AdvProp_efficientnet-b7	.783
34	seresnext50_32x4d	.783
35	resnext101_32x16d_wsl	.782
36	efficientnet_b0	.780
37	resnet50-moclr8deg	.778
38	vit_base_patch16_clip_224:openai_ft_in1k	.778
39	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.778
40	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.777
41	convnext_tiny_sup	.777
42	convnext_small_imagenet_full_seed-0	.772
43	resnext101_32x48d_wsl	.770
44	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.770
45	efficientnet_b2_imagenet_full	.768
46	ReAlnet03_cornet	.767
47	ReAlnet01_cornet	.765
48	ReAlnet04_cornet	.765
49	shufflenet_v2_x1_0	.765
50	compact_resnet50_robust_V4	.763
51	AlexNet_SIN	.761
52	cvt_cvt-13-224-in1k_4_LucyV4	.762
53	alexnet	.757
54	AT_efficientnet-b2	.757
55	AdvProp_efficientnet-b2	.757
56	deit_large_imagenet_full_seed-0	.757
57	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.755
58	deit_small_imagenet_full_seed-0	.754
59	cvt_cvt-21-224-in1k_4_LucyV4	.752
60	ViT_L_32_imagenet1k	.748
61	convnext_femto_ols:d1_in1k	.747
62	custom_model_cv_18_dagger_408	.745
63	vit_relpos_base_patch16_clsgap_224:sw_in1k	.745
64	AlexNet_SIN_fov12	.735
65	resnet152_imagenet_full	.727
66	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.726
67	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.726
68	effnetb1_272x240	.716
69	alexnet_training_seed_10	.711
70	alexnet_training_seed_09	.711
71	alexnet_training_seed_01	.709
72	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.707
73	convnext_base_imagenet_full_seed-0	.702
74	alexnet_training_seed_02	.700
75	alexnet_training_seed_07	.698
76	AlexNet_SIN_fov16	.663
77	eicircuit_resnet18_bn_20260919	.649
78	resnet50_imagenet_full	.642
79	convnext_large_imagenet_full_seed-0	.625
80	convnext_tiny_imagenet_full_seed-0	.612
81	alexnet_training_seed_06	.605
82	alexnet_training_seed_03	.564
83	convnext_tiny:in12k_ft_in1k	.554
84	gabor_filter_entropy_4x4_v2_forcedCosine	.460
85	gabor_filter_entropy_4x4_v2	.450
86	predictive_coding_pc	.439
87	pr1_stl10_disentangled_alpha04	.407
88	gabor_filter_entropy_4x4	.407
89	convnext_base_fb_in1k	.364
90	ReAlnet01_cornet_fov4	.277
91	hmax	.275
92	gabor_filter_mean_abs_4x4_v2	.241
93	vit_huge_clip336_laion
94	vit_large_clip336_openai
95	dinov2_vitl14_lc_res336
96	eva02_large_in22k
97	dinov2_vitg14_lc
98	dinov2_vitg14_reg_lc
99	resnet18_fair
100	resnet50_st_wzc_seed3_2_323
101	resnet50_ewconplus_wzc_seed5_2__323
102	resnet50_ewconplus_wzc_seed6_2_323
103	resnet50_st_wzc_seed7_2_323
104	resnet50_st_wzc_seed6_2_323
105	resnet50_ewconplus_wzc_seed2_2__323
106	resnext101_32x32d_wsl
107	vit_large_patch14_clip_336:laion2b_ft_in1k
108	vit_large_patch14_clip_336:openai_ft_in12k_in1k
109	cvt_cvt-w24-384-in22k_finetuned-in1k_4
110	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
111	vit_large_patch14_clip_224:laion2b_ft_in1k
112	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k
113	voneresnet-50-non_stochastic
114	VOneCORnet-S
115	pnasnet_large_halfPrecision
116	resnet50-vitoimagevidnet8
117	resnext101_32x8d_wsl
118	resnet50-VITO-8deg-cc
119	resnet_50_v2
120	resnet50-SIN_IN
121	Res2Net50_26w_4s
122	resnet50_ewconplus_wzc
123	convnext_xlarge:fb_in22k_ft_in1k
124	convnext_large:fb_in22k_ft_in1k
125	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