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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	ijepa_vith14_linhead	.688
2	resnet50_byol_linhead	.679
3	resnet50_simclr_linhead_01norm	.679
4	resnet50_vicreg_linhead	.678
5	resnet50_barlowtwins_linhead	.672
6	resnet50_simsiam_linhead	.671
7	convnext_xxlarge_clip	.657
8	custom_model_cv_18_dagger_408	.649
9	AdvProp_efficientnet-b2	.648
10	AT_efficientnet-b2	.648
11	shufflenet_v2_x1_0	.628
12	ReAlnet01_cornet_fov16	.627
13	dinov2_vitl14_reg_lc_featread	.624
14	dinov2_vitl14_reg_lc	.624
15	vit_relpos_base_patch16_clsgap_224:sw_in1k	.615
16	cvt_cvt-13-384-in1k_4_LucyV4	.612
17	resnet50-moclr8deg	.607
18	ReAlnet01_cornet_fov12	.606
19	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.606
20	voneresnet-50	.605
21	resnet50-vitoimagevidnet8	.603
22	resnet50-VITO-8deg-cc	.603
23	cvt_cvt-21-384-in1k_4_LucyV4	.603
24	cvt_cvt-21-224-in1k_4_LucyV4	.601
25	convnext_tiny:in12k_ft_in1k	.599
26	seresnext50_32x4d	.596
27	ReAlnet06_cornet	.594
28	cvt_cvt-13-224-in1k_4_LucyV4	.594
29	vit_large_patch14_clip_224:openai_ft_in1k	.593
30	ReAlnet01_cornet	.588
31	ReAlnet02_cornet	.587
32	ReAlnet09_cornet	.587
33	ReAlnet03_cornet	.586
34	ReAlnet04_cornet	.586
35	resnet50-SIN	.586
36	ReAlnet10_cornet	.585
37	resnet152_imagenet_full	.584
38	ReAlnet08_cornet	.582
39	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.581
40	efficientnet_b0	.580
41	AdvProp_efficientnet-b8	.579
42	resnet50_ewconplus_wzc	.579
43	resnet50_st_wzc_seed3_2_323	.578
44	mehrer2020_alexnet_training_seed_04	.575
45	mobilenet_v2_1_4_224	.575
46	resnet50_st_wzc_seed7_2_323	.573
47	AdvProp_efficientnet-b4	.573
48	mehrer2020_alexnet_training_seed_06	.572
49	resnet50-SIN_IN_IN	.572
50	resnet_SIN_IN_FT_IN	.572
51	mehrer2020_alexnet_training_seed_05	.571
52	mehrer2020_alexnet_training_seed_07	.570
53	resnet101_imagenet_full	.570
54	mehrer2020_alexnet_training_seed_02	.570
55	resnet152_ecoset_full	.569
56	resnet101_ecoset_full	.569
57	mehrer2020_alexnet_training_seed_03	.568
58	convnext_tiny_sup	.568
59	mehrer2020_alexnet_training_seed_09	.568
60	dinov2_vitl14_lc	.567
61	resnet_50_v1_spiking	.566
62	resnet50_tutorial	.566
63	resnet50-sup	.566
64	resnet_50_v1	.566
65	resnet50-SIN_IN	.565
66	mehrer2020_alexnet_training_seed_08	.564
67	focalnet_tiny_lrf_in1k	.563
68	mehrer2020_alexnet_training_seed_01	.561
69	VOneCORnet-S	.557
70	Res2Net50_26w_4s	.556
71	regnety_032	.555
72	AlexNet_SIN	.554
73	resnet50_ewconplus_wzc_seed6_2_323	.552
74	convnext_xlarge:fb_in22k_ft_in1k	.551
75	deit_small_imagenet_full_seed-0	.550
76	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.550
77	vit_base_patch16_clip_224:openai_ft_in1k	.550
78	efficientnet_b2_imagenet_full	.548
79	mehrer2020_alexnet_training_seed_01_fov16	.547
80	resnext101_32x16d_wsl	.547
81	resnext101_32x48d_wsl	.545
82	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.544
83	resnet_50_v2	.543
84	vit_large_patch14_clip_224:laion2b_ft_in1k	.543
85	antialiased-r50	.543
86	mehrer2020_alexnet_training_seed_01_fov12	.542
87	convnext_base:clip_laiona_augreg_ft_in1k_384	.541
88	resnet50_ewconplus_wzc_seed5_2__323	.538
89	resnet-50-robust	.538
90	convnext_tiny_imagenet_full_seed-0	.534
91	resnext101_32x8d_wsl	.534
92	resnext101_32x32d_wsl	.534
93	deit_large_imagenet_full_seed-0	.533
94	resnet50_ewconplus_wzc_seed2_2__323	.528
95	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.525
96	convnext_large_imagenet_full_seed-0	.523
97	effnetb1_272x240	.522
98	resnet50_robust_l2_eps1	.520
99	AlexNet_SIN_fov12	.519
100	resnet50_imagenet_full	.518
101	resnet34_ecoset_full	.516
102	efficientnet_b1_imagenet_full	.509
103	convnext_small_imagenet_full_seed-0	.508
104	voneresnet-50-non_stochastic	.508
105	convnext_base_imagenet_full_seed-0	.507
106	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.507
107	resnet50_ecoset_full	.503
108	convnext_femto_ols:d1_in1k	.501
109	mehrer2020_alexnet_training_seed_10	.501
110	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.498
111	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.491
112	resnet18_fair	.491
113	AdvProp_efficientnet-b6	.488
114	resnet18_imagenet_full	.482
115	AdvProp_efficientnet-b7	.482
116	resnet50_imagenet_100_seed-0	.481
117	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.480
118	densenet-121	.478
119	resnet18_ecoset_full	.476
120	efficientnet_b0_imagenet_full	.472
121	resnet34_imagenet_full	.459
122	predictive_coding_pc	.458
123	grcnn	.456
124	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.450
125	deit_small_imagenet_100_seed-0	.440
126	pr1_stl10_disentangled_alpha04	.427
127	ViT_L_32_imagenet1k	.420
128	convnext_small_imagenet_100_seed-0	.406
129	AlexNet_SIN_fov16	.359
130	alexnet_training_seed_02	.325
131	alexnet_training_seed_03	.323
132	alexnet_training_seed_06	.320
133	alexnet_training_seed_01	.314
134	alexnet_training_seed_09	.311
135	alexnet_training_seed_07	.307
136	alexnet_training_seed_10	.305
137	gabor_filter_entropy_4x4_v2_forcedCosine	.284
138	gabor_filter_entropy_4x4_v2	.281
139	gabor_filter_entropy_4x4	.273
140	ReAlnet01_cornet_fov4	.272
141	mehrer2020_alexnet_training_seed_01_fov4	.267
142	hmax	.236
143	AlexNet_SIN_fov4	.201
144	gabor_filter_mean_abs_4x4_v2	.177
145	compact_resnet50_robust_V4	.134
146	convnext_large:fb_in22k_ft_in1k
147	pnasnet_large_halfPrecision
148	ReAlnet07_cornet
149	ReAlnet05_cornet
150	eicircuit_resnet18_bn_20260919
151	vit_huge_clip336_laion
152	vit_large_clip336_openai
153	dinov2_vitl14_lc_res336
154	eva02_large_in22k
155	dinov2_vitg14_reg_lc
156	dinov2_vitg14_lc
157	convnext_base_fb_in1k
158	resnet50_st_wzc_seed6_2_323
159	cvt_cvt-w24-384-in22k_finetuned-in1k_4
160	vit_large_patch14_clip_336:laion2b_ft_in1k
161	vit_large_patch14_clip_336:openai_ft_in12k_in1k
162	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
163	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k
164	vit_large_patch14_clip_224:openai_ft_in12k_in1k
165	deit_base_imagenet_full_seed-0
166	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