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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Papale2025.V4-ridgecv")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_xxlarge_clip	.657
2	custom_model_cv_18_dagger_408	.649
3	AdvProp_efficientnet-b2	.648
4	AT_efficientnet-b2	.648
5	shufflenet_v2_x1_0	.628
6	dinov2_vitl14_reg_lc_featread	.624
7	dinov2_vitl14_reg_lc	.624
8	vit_relpos_base_patch16_clsgap_224:sw_in1k	.615
9	cvt_cvt-13-384-in1k_4_LucyV4	.612
10	resnet50-moclr8deg	.607
11	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.606
12	voneresnet-50	.605
13	resnet50-vitoimagevidnet8	.603
14	resnet50-VITO-8deg-cc	.603
15	cvt_cvt-21-384-in1k_4_LucyV4	.603
16	cvt_cvt-21-224-in1k_4_LucyV4	.601
17	convnext_tiny:in12k_ft_in1k	.599
18	cvt_cvt-13-224-in1k_4_LucyV4	.594
19	vit_large_patch14_clip_224:openai_ft_in1k	.593
20	ReAlnet01_cornet	.588
21	ReAlnet03_cornet	.586
22	ReAlnet04_cornet	.586
23	resnet50-SIN	.586
24	resnet152_imagenet_full	.584
25	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.581
26	efficientnet_b0	.580
27	AdvProp_efficientnet-b8	.579
28	resnet50_ewconplus_wzc	.579
29	resnet50_st_wzc_seed3_2_323	.578
30	mobilenet_v2_1_4_224	.575
31	resnet50_st_wzc_seed7_2_323	.573
32	AdvProp_efficientnet-b4	.573
33	resnet50-SIN_IN_IN	.572
34	resnet_SIN_IN_FT_IN	.572
35	convnext_tiny_sup	.568
36	dinov2_vitl14_lc	.567
37	resnet_50_v1_spiking	.566
38	resnet50_tutorial	.566
39	resnet50-sup	.566
40	resnet_50_v1	.566
41	resnet50-SIN_IN	.565
42	focalnet_tiny_lrf_in1k	.563
43	VOneCORnet-S	.557
44	Res2Net50_26w_4s	.556
45	AlexNet_SIN	.554
46	resnet50_ewconplus_wzc_seed6_2_323	.552
47	convnext_xlarge:fb_in22k_ft_in1k	.551
48	deit_small_imagenet_full_seed-0	.550
49	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.550
50	vit_base_patch16_clip_224:openai_ft_in1k	.550
51	efficientnet_b2_imagenet_full	.548
52	resnext101_32x16d_wsl	.547
53	resnext101_32x48d_wsl	.545
54	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.544
55	resnet_50_v2	.543
56	vit_large_patch14_clip_224:laion2b_ft_in1k	.543
57	antialiased-r50	.543
58	convnext_base:clip_laiona_augreg_ft_in1k_384	.541
59	resnet50_ewconplus_wzc_seed5_2__323	.538
60	resnet-50-robust	.538
61	convnext_tiny_imagenet_full_seed-0	.534
62	resnext101_32x8d_wsl	.534
63	resnext101_32x32d_wsl	.534
64	deit_large_imagenet_full_seed-0	.533
65	resnet50_ewconplus_wzc_seed2_2__323	.528
66	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.525
67	convnext_large_imagenet_full_seed-0	.523
68	effnetb1_272x240	.522
69	resnet50_robust_l2_eps1	.520
70	AlexNet_SIN_fov12	.519
71	resnet50_imagenet_full	.518
72	convnext_small_imagenet_full_seed-0	.508
73	voneresnet-50-non_stochastic	.508
74	convnext_base_imagenet_full_seed-0	.507
75	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.507
76	convnext_femto_ols:d1_in1k	.501
77	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.498
78	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.491
79	resnet18_fair	.491
80	AdvProp_efficientnet-b6	.488
81	AdvProp_efficientnet-b7	.482
82	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.480
83	densenet-121	.478
84	predictive_coding_pc	.458
85	grcnn	.456
86	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.450
87	pr1_stl10_disentangled_alpha04	.427
88	ViT_L_32_imagenet1k	.420
89	AlexNet_SIN_fov16	.359
90	alexnet_training_seed_02	.325
91	alexnet_training_seed_03	.323
92	alexnet_training_seed_06	.320
93	alexnet_training_seed_01	.314
94	alexnet_training_seed_09	.311
95	alexnet_training_seed_07	.307
96	alexnet_training_seed_10	.305
97	ReAlnet01_cornet_fov4	.272
98	hmax	.236
99	compact_resnet50_robust_V4	.134
100	vit_huge_clip336_laion
101	vit_large_clip336_openai
102	dinov2_vitl14_lc_res336
103	eva02_large_in22k
104	dinov2_vitg14_reg_lc
105	dinov2_vitg14_lc
106	convnext_base_fb_in1k
107	resnet50_st_wzc_seed6_2_323
108	cvt_cvt-w24-384-in22k_finetuned-in1k_4
109	vit_large_patch14_clip_336:laion2b_ft_in1k
110	vit_large_patch14_clip_336:openai_ft_in12k_in1k
111	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
112	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k
113	vit_large_patch14_clip_224:openai_ft_in12k_in1k
114	pnasnet_large_halfPrecision
115	convnext_large:fb_in22k_ft_in1k
116	deit_base_imagenet_full_seed-0
117	convnext_xxlarge:clip_laion2b_soup_ft_in1k

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.V4

Metric: ridgecv