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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Gifford2022.IT-ridgecv")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnext101_32x32d_wsl	.284
2	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.277
3	resnet50-moclr8deg	.278
4	resnext101_32x48d_wsl	.277
5	deit_large_imagenet_full_seed-0	.273
6	resnext101_32x16d_wsl	.269
7	convnext_base:clip_laiona_augreg_ft_in1k_384	.269
8	convnext_tiny:in12k_ft_in1k	.266
9	dinov2_vitl14_lc	.265
10	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.264
11	densenet-121	.262
12	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.261
13	vit_relpos_base_patch16_clsgap_224:sw_in1k	.261
14	vit_large_patch14_clip_336:laion2b_ft_in1k	.259
15	resnet50-vitoimagevidnet8	.259
16	resnet50-VITO-8deg-cc	.259
17	resnet50_tutorial	.257
18	convnext_large_imagenet_full_seed-0	.257
19	resnet50_simsiam_linhead	.257
20	custom_model_cv_18_dagger_408	.257
21	deit_base_imagenet_full_seed-0	.256
22	ReAlnet08_cornet	.253
23	convnext_large:fb_in22k_ft_in1k	.252
24	voneresnet-50	.252
25	ReAlnet03_cornet	.252
26	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.251
27	ReAlnet10_cornet	.252
28	resnet50-SIN_IN_IN	.252
29	ReAlnet01_cornet	.251
30	ReAlnet02_cornet	.251
31	vit_large_patch14_clip_224:openai_ft_in1k	.250
32	ReAlnet05_cornet	.250
33	focalnet_tiny_lrf_in1k	.250
34	resnet50_ewconplus_wzc	.250
35	convnext_small_imagenet_full_seed-0	.250
36	ReAlnet07_cornet	.250
37	resnet50_ewconplus_wzc_seed5_2__323	.249
38	convnext_xlarge:fb_in22k_ft_in1k	.250
39	resnet50_robust_l2_eps1	.248
40	seresnext50_32x4d	.249
41	dinov2_vitl14_reg_lc_featread	.248
42	dinov2_vitl14_reg_lc	.248
43	voneresnet-50-non_stochastic	.247
44	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.247
45	resnet_50_v1	.247
46	resnet50-sup	.247
47	resnet_50_v1_spiking	.247
48	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.247
49	ReAlnet04_cornet	.247
50	ReAlnet09_cornet	.247
51	shufflenet_v2_x1_0	.248
52	VOneCORnet-S	.246
53	resnet50_st_wzc_seed6_2_323	.246
54	resnet152_imagenet_full	.247
55	resnet101_ecoset_full	.246
56	ReAlnet06_cornet	.247
57	antialiased-r50	.246
58	deit_small_imagenet_full_seed-0	.245
59	resnet18_ecoset_full	.246
60	ReAlnet01_cornet_fov12	.244
61	resnet50_ecoset_full	.244
62	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.243
63	resnet101_imagenet_full	.242
64	resnet50_ewconplus_wzc_seed6_2_323	.243
65	convnext_tiny_imagenet_full_seed-0	.243
66	convnext_base_imagenet_full_seed-0	.242
67	resnet50_st_wzc_seed7_2_323	.241
68	resnet152_ecoset_full	.241
69	resnet50_imagenet_full	.241
70	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.240
71	resnet18_imagenet_full	.240
72	resnet50_st_wzc_seed3_2_323	.239
73	resnet50_simclr_linhead_01norm	.239
74	vit_large_patch14_clip_224:laion2b_ft_in1k	.239
75	Res2Net50_26w_4s	.240
76	resnet-50-robust	.239
77	resnet50_barlowtwins_linhead	.237
78	convnext_femto_ols:d1_in1k	.238
79	resnet50_ewconplus_wzc_seed2_2__323	.238
80	resnet50-SIN_IN	.237
81	efficientnet_b0	.236
82	cvt_cvt-13-384-in1k_4_LucyV4	.236
83	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.236
84	ReAlnet01_cornet_fov16	.235
85	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.235
86	resnet34_imagenet_full	.235
87	resnet50-SIN	.235
88	vit_base_patch16_clip_224:openai_ft_in1k	.234
89	cvt_cvt-13-224-in1k_4_LucyV4	.233
90	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.233
91	mobilenet_v2_1_4_224	.232
92	resnet_50_v2	.232
93	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.231
94	AT_efficientnet-b2	.230
95	AdvProp_efficientnet-b2	.230
96	cvt_cvt-21-384-in1k_4_LucyV4	.230
97	resnet50_byol_linhead	.229
98	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.230
99	resnet50_imagenet_100_seed-0	.229
100	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.228
101	resnext101_32x8d_wsl	.226
102	resnet_SIN_IN_FT_IN	.226
103	AdvProp_efficientnet-b6	.224
104	resnet50_vicreg_linhead	.223
105	resnet34_ecoset_full	.223
106	ijepa_vith14_linhead	.223
107	cvt_cvt-21-224-in1k_4_LucyV4	.221
108	vit_large_clip336_openai	.217
109	efficientnet_b1_imagenet_full	.216
110	AdvProp_efficientnet-b7	.215
111	AdvProp_efficientnet-b4	.215
112	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.214
113	effnetb1_272x240	.213
114	AdvProp_efficientnet-b8	.213
115	mehrer2020_alexnet_training_seed_05	.212
116	regnety_032	.210
117	convnext_tiny_sup	.210
118	eicircuit_resnet18_bn_20260919	.210
119	ReAlnet01_cornet_fov4	.212
120	grcnn	.211
121	efficientnet_b0_imagenet_full	.210
122	mehrer2020_alexnet_training_seed_03	.210
123	mehrer2020_alexnet_training_seed_08	.209
124	mehrer2020_alexnet_training_seed_02	.208
125	mehrer2020_alexnet_training_seed_04	.208
126	mehrer2020_alexnet_training_seed_07	.208
127	mehrer2020_alexnet_training_seed_06	.206
128	alexnet	.206
129	mehrer2020_alexnet_training_seed_09	.204
130	mehrer2020_alexnet_training_seed_01	.204
131	mehrer2020_alexnet_training_seed_10	.203
132	efficientnet_b2_imagenet_full	.202
133	mehrer2020_alexnet_training_seed_01_fov12	.200
134	ViT_L_32_imagenet1k	.196
135	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.186
136	mehrer2020_alexnet_training_seed_01_fov16	.183
137	convnext_small_imagenet_100_seed-0	.179
138	AlexNet_SIN_fov12	.176
139	AlexNet_SIN	.173
140	AlexNet_SIN_fov16	.172
141	deit_small_imagenet_100_seed-0	.170
142	mehrer2020_alexnet_training_seed_01_fov4	.167
143	pr1_stl10_disentangled_alpha04	.162
144	predictive_coding_pc	.147
145	AlexNet_SIN_fov4	.144
146	resnet18_fair	.142
147	alexnet_training_seed_06	.103
148	alexnet_training_seed_02	.103
149	gabor_filter_entropy_4x4_v2_forcedCosine	.103
150	gabor_filter_entropy_4x4	.102
151	gabor_filter_entropy_4x4_v2	.101
152	convnext_base_fb_in1k	.098
153	alexnet_training_seed_09	.098
154	alexnet_training_seed_01	.090
155	alexnet_training_seed_03	.087
156	alexnet_training_seed_10	.086
157	alexnet_training_seed_07	.084
158	compact_resnet50_robust_V4	.071
159	gabor_filter_mean_abs_4x4_v2	.066
160	hmax	.050
161	pnasnet_large_halfPrecision
162	vit_huge_clip336_laion
163	convnext_xxlarge_clip
164	dinov2_vitl14_lc_res336
165	eva02_large_in22k
166	dinov2_vitg14_reg_lc
167	dinov2_vitg14_lc

Benchmark bibtex

@article{gifford_large_2022,
	title = {A large and rich {EEG} dataset for modeling human visual object recognition},
	volume = {264},
	issn = {10538119},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811922008758},
	doi = {10.1016/j.neuroimage.2022.119754},
	journal = {NeuroImage},
	author = {Gifford, Alessandro T. and Dwivedi, Kshitij and Roig, Gemma and Cichy, Radoslaw M.},
	year = {2022},
}

Ceiling

0.42.

Note that scores are relative to this ceiling.

Data: Gifford2022.IT

Metric: ridgecv