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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	ijepa_vith14_linhead	.828
2	resnet50_robust_l2_eps1	.827
3	dinov2_vitl14_lc	.822
4	antialiased-r50	.821
5	voneresnet-50	.820
6	dinov2_vitl14_reg_lc	.819
7	dinov2_vitl14_reg_lc_featread	.819
8	resnet-50-robust	.817
9	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.817
10	resnet_50_v1_spiking	.809
11	resnet_50_v1	.809
12	resnet50_tutorial	.809
13	resnet50-sup	.809
14	convnext_base:clip_laiona_augreg_ft_in1k_384	.809
15	vit_large_patch14_clip_224:openai_ft_in1k	.809
16	resnet50-SIN_IN_IN	.808
17	AdvProp_efficientnet-b8	.806
18	resnet_SIN_IN_FT_IN	.804
19	resnet50-SIN	.803
20	focalnet_tiny_lrf_in1k	.802
21	mehrer2020_alexnet_training_seed_08	.800
22	AdvProp_efficientnet-b6	.800
23	densenet-121	.799
24	mehrer2020_alexnet_training_seed_10	.797
25	mehrer2020_alexnet_training_seed_09	.797
26	mobilenet_v2_1_4_224	.796
27	mehrer2020_alexnet_training_seed_06	.796
28	mehrer2020_alexnet_training_seed_07	.795
29	mehrer2020_alexnet_training_seed_01	.795
30	mehrer2020_alexnet_training_seed_04	.794
31	cvt_cvt-13-384-in1k_4_LucyV4	.794
32	convnext_xxlarge_clip	.794
33	cvt_cvt-21-384-in1k_4_LucyV4	.794
34	mehrer2020_alexnet_training_seed_03	.793
35	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.793
36	regnety_032	.793
37	grcnn	.792
38	mehrer2020_alexnet_training_seed_05	.791
39	mehrer2020_alexnet_training_seed_02	.791
40	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.790
41	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.789
42	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.786
43	AdvProp_efficientnet-b4	.785
44	AdvProp_efficientnet-b7	.783
45	seresnext50_32x4d	.783
46	resnext101_32x16d_wsl	.782
47	efficientnet_b0	.780
48	resnet50-moclr8deg	.778
49	vit_base_patch16_clip_224:openai_ft_in1k	.778
50	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.778
51	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.777
52	convnext_tiny_sup	.777
53	efficientnet_b1_imagenet_full	.774
54	convnext_small_imagenet_full_seed-0	.772
55	convnext_small_imagenet_100_seed-0	.771
56	ReAlnet10_cornet	.770
57	resnext101_32x48d_wsl	.770
58	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.770
59	ReAlnet07_cornet	.768
60	efficientnet_b2_imagenet_full	.768
61	ReAlnet08_cornet	.768
62	ReAlnet03_cornet	.767
63	ReAlnet02_cornet	.767
64	ReAlnet01_cornet_fov12	.766
65	ReAlnet05_cornet	.766
66	ReAlnet01_cornet	.765
67	ReAlnet04_cornet	.765
68	ReAlnet06_cornet	.765
69	shufflenet_v2_x1_0	.765
70	ReAlnet09_cornet	.763
71	compact_resnet50_robust_V4	.763
72	AlexNet_SIN	.761
73	cvt_cvt-13-224-in1k_4_LucyV4	.762
74	efficientnet_b0_imagenet_full	.762
75	mehrer2020_alexnet_training_seed_01_fov12	.761
76	ReAlnet01_cornet_fov16	.758
77	alexnet	.757
78	AT_efficientnet-b2	.757
79	AdvProp_efficientnet-b2	.757
80	deit_large_imagenet_full_seed-0	.757
81	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.755
82	deit_small_imagenet_full_seed-0	.754
83	cvt_cvt-21-224-in1k_4_LucyV4	.752
84	ViT_L_32_imagenet1k	.748
85	convnext_femto_ols:d1_in1k	.747
86	custom_model_cv_18_dagger_408	.745
87	vit_relpos_base_patch16_clsgap_224:sw_in1k	.745
88	deit_small_imagenet_100_seed-0	.742
89	AlexNet_SIN_fov12	.735
90	resnet50_ecoset_full	.735
91	resnet18_imagenet_full	.732
92	resnet101_imagenet_full	.729
93	resnet152_imagenet_full	.727
94	resnet152_ecoset_full	.726
95	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.726
96	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.726
97	resnet101_ecoset_full	.725
98	mehrer2020_alexnet_training_seed_01_fov16	.718
99	resnet34_imagenet_full	.715
100	resnet34_ecoset_full	.715
101	effnetb1_272x240	.716
102	alexnet_training_seed_10	.711
103	alexnet_training_seed_09	.711
104	alexnet_training_seed_01	.709
105	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.707
106	convnext_base_imagenet_full_seed-0	.702
107	alexnet_training_seed_02	.700
108	alexnet_training_seed_07	.698
109	AlexNet_SIN_fov16	.663
110	eicircuit_resnet18_bn_20260919	.649
111	resnet50_imagenet_full	.642
112	resnet18_ecoset_full	.641
113	resnet50_imagenet_100_seed-0	.639
114	convnext_large_imagenet_full_seed-0	.625
115	convnext_tiny_imagenet_full_seed-0	.612
116	alexnet_training_seed_06	.605
117	alexnet_training_seed_03	.564
118	convnext_tiny:in12k_ft_in1k	.554
119	gabor_filter_entropy_4x4_v2_forcedCosine	.460
120	gabor_filter_entropy_4x4_v2	.450
121	predictive_coding_pc	.439
122	pr1_stl10_disentangled_alpha04	.407
123	gabor_filter_entropy_4x4	.407
124	convnext_base_fb_in1k	.364
125	AlexNet_SIN_fov4	.310
126	mehrer2020_alexnet_training_seed_01_fov4	.300
127	ReAlnet01_cornet_fov4	.277
128	hmax	.275
129	gabor_filter_mean_abs_4x4_v2	.241
130	resnet50_simclr_linhead_01norm
131	resnet50_barlowtwins_linhead
132	resnet50_simsiam_linhead
133	resnet50_vicreg_linhead
134	resnet50_byol_linhead
135	resnet50-SIN_IN
136	vit_large_patch14_clip_336:laion2b_ft_in1k
137	resnet50-VITO-8deg-cc
138	Res2Net50_26w_4s
139	resnext101_32x32d_wsl
140	resnext101_32x8d_wsl
141	resnet50_ewconplus_wzc
142	vit_huge_clip336_laion
143	vit_large_clip336_openai
144	dinov2_vitl14_lc_res336
145	eva02_large_in22k
146	dinov2_vitg14_lc
147	dinov2_vitg14_reg_lc
148	resnet18_fair
149	resnet50_st_wzc_seed3_2_323
150	resnet50_ewconplus_wzc_seed5_2__323
151	resnet50_ewconplus_wzc_seed6_2_323
152	resnet50_st_wzc_seed7_2_323
153	resnet50_st_wzc_seed6_2_323
154	resnet50_ewconplus_wzc_seed2_2__323
155	vit_large_patch14_clip_336:openai_ft_in12k_in1k
156	cvt_cvt-w24-384-in22k_finetuned-in1k_4
157	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
158	vit_large_patch14_clip_224:laion2b_ft_in1k
159	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k
160	voneresnet-50-non_stochastic
161	VOneCORnet-S
162	pnasnet_large_halfPrecision
163	resnet50-vitoimagevidnet8
164	resnet_50_v2
165	convnext_xlarge:fb_in22k_ft_in1k
166	convnext_large:fb_in22k_ft_in1k
167	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