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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	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.277
2	resnet50-moclr8deg	.278
3	resnext101_32x48d_wsl	.277
4	deit_large_imagenet_full_seed-0	.273
5	convnext_base:clip_laiona_augreg_ft_in1k_384	.269
6	convnext_tiny:in12k_ft_in1k	.266
7	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.264
8	densenet-121	.262
9	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.261
10	vit_relpos_base_patch16_clsgap_224:sw_in1k	.261
11	vit_large_patch14_clip_336:laion2b_ft_in1k	.259
12	resnet50-vitoimagevidnet8	.259
13	resnet50-VITO-8deg-cc	.259
14	resnet50_tutorial	.257
15	convnext_large_imagenet_full_seed-0	.257
16	custom_model_cv_18_dagger_408	.257
17	deit_base_imagenet_full_seed-0	.256
18	convnext_large:fb_in22k_ft_in1k	.252
19	voneresnet-50	.252
20	ReAlnet03_cornet	.252
21	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.251
22	resnet50-SIN_IN_IN	.252
23	ReAlnet01_cornet	.251
24	vit_large_patch14_clip_224:openai_ft_in1k	.250
25	focalnet_tiny_lrf_in1k	.250
26	resnet50_ewconplus_wzc	.250
27	convnext_small_imagenet_full_seed-0	.250
28	resnet50_ewconplus_wzc_seed5_2__323	.249
29	convnext_xlarge:fb_in22k_ft_in1k	.250
30	resnet50_robust_l2_eps1	.248
31	resnet50_ewconplus_wzc_seed7_2_323	.249
32	resnet50_ewconplus_wzc_2_323	.249
33	voneresnet-50-non_stochastic	.247
34	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.247
35	resnet_50_v1	.247
36	resnet50-sup	.247
37	resnet_50_v1_spiking	.247
38	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.247
39	ReAlnet04_cornet	.247
40	shufflenet_v2_x1_0	.248
41	resnet50_ewconplus_wzc_seed3_2_323	.246
42	VOneCORnet-S	.246
43	resnet50_st_wzc_seed6_2_323	.246
44	resnet152_imagenet_full	.247
45	resnet50_st_wzc_noseed_2_323	.246
46	antialiased-r50	.246
47	deit_small_imagenet_full_seed-0	.245
48	resnet50_st_wzc_seed4_2_323	.245
49	resnet50_ewconplus_wzc_seed4_2_323	.244
50	resnet50_st_wzc_seed5_2_323	.242
51	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.243
52	resnet50_st_wzc_2_323	.242
53	resnet50_ewconplus_wzc_seed6_2_323	.243
54	convnext_tiny_imagenet_full_seed-0	.243
55	convnext_base_imagenet_full_seed-0	.242
56	resnet50_st_wzc_seed7_2_323	.241
57	resnet50_imagenet_full	.241
58	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.240
59	resnet50_st_wzc_seed3_2_323	.239
60	vit_large_patch14_clip_224:laion2b_ft_in1k	.239
61	Res2Net50_26w_4s	.240
62	resnet-50-robust	.239
63	convnext_femto_ols:d1_in1k	.238
64	resnet50_ewconplus_wzc_seed2_2__323	.238
65	resnet50-SIN_IN	.237
66	efficientnet_b0	.236
67	cvt_cvt-13-384-in1k_4_LucyV4	.236
68	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.236
69	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.235
70	resnet50-SIN	.235
71	vit_base_patch16_clip_224:openai_ft_in1k	.234
72	cvt_cvt-13-224-in1k_4_LucyV4	.233
73	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.233
74	mobilenet_v2_1_4_224	.232
75	resnet_50_v2	.232
76	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.231
77	AT_efficientnet-b2	.230
78	AdvProp_efficientnet-b2	.230
79	cvt_cvt-21-384-in1k_4_LucyV4	.230
80	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.230
81	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.228
82	resnext101_32x8d_wsl	.226
83	resnet_SIN_IN_FT_IN	.226
84	AdvProp_efficientnet-b6	.224
85	cvt_cvt-21-224-in1k_4_LucyV4	.221
86	AdvProp_efficientnet-b7	.215
87	AdvProp_efficientnet-b4	.215
88	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.214
89	effnetb1_272x240	.213
90	AdvProp_efficientnet-b8	.213
91	convnext_tiny_sup	.210
92	ReAlnet01_cornet_fov4	.212
93	grcnn	.211
94	alexnet	.206
95	efficientnet_b2_imagenet_full	.202
96	ViT_L_32_imagenet1k	.196
97	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.186
98	AlexNet_SIN_fov12	.176
99	AlexNet_SIN	.173
100	AlexNet_SIN_fov16	.172
101	pr1_stl10_disentangled_alpha04	.162
102	predictive_coding_pc	.147
103	resnet18_fair	.142
104	alexnet_training_seed_06	.103
105	alexnet_training_seed_02	.103
106	convnext_base_fb_in1k	.098
107	alexnet_training_seed_09	.098
108	alexnet_training_seed_01	.090
109	alexnet_training_seed_03	.087
110	alexnet_training_seed_10	.086
111	alexnet_training_seed_07	.084
112	compact_resnet50_robust_V4	.071
113	hmax	.050
114	pnasnet_large_halfPrecision
115	resnext101_32x32d_wsl
116	resnext101_32x16d_wsl

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