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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Hebart2023_fmri.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	.395
2	resnext101_32x32d_wsl	.390
3	resnext101_32x48d_wsl	.384
4	resnext101_32x16d_wsl	.380
5	deit_large_imagenet_full_seed-0	.365
6	vit_large_patch14_clip_336:laion2b_ft_in1k	.354
7	deit_small_imagenet_full_seed-0	.348
8	convnext_femto_ols:d1_in1k	.344
9	resnet50-moclr8deg	.344
10	dinov2_vitl14_lc_res336	.341
11	predictive_coding_pc	.337
12	dinov2_vitl14_lc	.337
13	focalnet_tiny_lrf_in1k	.336
14	dinov2_vitg14_reg_lc	.335
15	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.334
16	convnext_base:clip_laiona_augreg_ft_in1k_384	.333
17	vit_relpos_base_patch16_clsgap_224:sw_in1k	.331
18	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.331
19	resnet_50_v2	.329
20	deit_base_imagenet_full_seed-0	.329
21	antialiased-r50	.327
22	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.326
23	densenet-121	.321
24	resnet_50_v1	.317
25	resnet_50_v1_spiking	.317
26	resnet50-sup	.317
27	shufflenet_v2_x1_0	.318
28	resnet50_st_wzc_seed6_2_323	.316
29	convnext_tiny_sup	.315
30	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.315
31	voneresnet-50-non_stochastic	.313
32	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.313
33	resnet50_st_wzc_seed3_2_323	.312
34	efficientnet_b0	.312
35	resnet50-SIN_IN_IN	.312
36	seresnext50_32x4d	.313
37	convnext_large_imagenet_full_seed-0	.310
38	resnet152_imagenet_full	.310
39	resnet50_st_wzc_seed7_2_323	.309
40	convnext_tiny:in12k_ft_in1k	.309
41	vit_base_patch16_clip_224:openai_ft_in1k	.308
42	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.307
43	ReAlnet01_cornet	.303
44	convnext_base_imagenet_full_seed-0	.303
45	ReAlnet03_cornet	.301
46	vit_large_patch14_clip_224:openai_ft_in1k	.301
47	resnet50-VITO-8deg-cc	.299
48	resnet50-vitoimagevidnet8	.299
49	resnet50_imagenet_full	.298
50	convnext_xxlarge_clip	.297
51	eva02_large_in22k	.299
52	ReAlnet04_cornet	.296
53	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.296
54	resnet50-SIN_IN	.295
55	Res2Net50_26w_4s	.295
56	convnext_large:fb_in22k_ft_in1k	.292
57	resnet50_robust_l2_eps1	.292
58	voneresnet-50	.292
59	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.291
60	dinov2_vitl14_reg_lc_featread	.289
61	dinov2_vitl14_reg_lc	.289
62	pnasnet_large_halfPrecision	.290
63	resnet50_ewconplus_wzc	.287
64	VOneCORnet-S	.285
65	vit_large_patch14_clip_224:laion2b_ft_in1k	.284
66	resnet50_tutorial	.281
67	resnet50_ewconplus_wzc_seed6_2_323	.280
68	resnet50_ewconplus_wzc_seed5_2__323	.279
69	resnet_SIN_IN_FT_IN	.279
70	eicircuit_resnet18_bn_20260919	.279
71	regnety_032	.279
72	mobilenet_v2_1_4_224	.278
73	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.276
74	custom_model_cv_18_dagger_408	.275
75	resnet50_ewconplus_wzc_seed2_2__323	.271
76	AdvProp_efficientnet-b7	.268
77	AT_efficientnet-b2	.266
78	AdvProp_efficientnet-b2	.266
79	convnext_large_clip_laion2b_finelayers	.260
80	efficientnet_b2_imagenet_full	.259
81	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.248
82	deit3_base_patch16_224_fb_in22k_ft_in1k	.248
83	AdvProp_efficientnet-b6	.246
84	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.245
85	convnext_small_imagenet_full_seed-0	.243
86	effnetb1_272x240	.243
87	convnext_tiny_imagenet_full_seed-0	.243
88	deepeisnn_ei_imagenet_resnet18	.238
89	AdvProp_efficientnet-b4	.237
90	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.237
91	resnet50-SIN	.233
92	convnext_xlarge:fb_in22k_ft_in1k	.230
93	AdvProp_efficientnet-b8	.230
94	resnext101_32x8d_wsl	.230
95	alexnet	.229
96	yudixie_wbh_object_class_scaling_0415_imagenet	.227
97	deit3_base_patch16_224_fb_in1k	.223
98	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.223
99	cvt_cvt-13-224-in1k_4_LucyV4	.222
100	ViT_L_32_imagenet1k	.221
101	ReAlnet01_cornet_fov4	.217
102	convnext_base_fb_in22k_ft_in1k	.211
103	cvt_cvt-13-384-in1k_4_LucyV4	.210
104	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.203
105	resnet-50-robust	.201
106	dinov2_vitg14_lc	.200
107	cvt_cvt-21-384-in1k_4_LucyV4	.197
108	vit_large_clip336_openai	.194
109	grcnn	.192
110	cvt_cvt-21-224-in1k_4_LucyV4	.191
111	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.191
112	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.189
113	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.183
114	convnext_base_fb_in1k	.178
115	pr1_stl10_disentangled_alpha04	.172
116	AlexNet_SIN	.167
117	convnext_large_in22k_supervised	.157
118	AlexNet_SIN_fov12	.121
119	AlexNet_SIN_fov16	.108
120	resnet18_fair	.105
121	deepeisnn_ei_cifar10_resnet18	.092
122	deepeisnn_ei_cifar10_vgg19	.087
123	convnext_base_fb_in1k_gram_aligned_dinov3	.081
124	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.070
125	hmax	.059
126	yudixie_wbh_object_class_scaling_0415_run_2	.031
127	yudixie_wbh_object_class_scaling_0415_run_0	.030
128	alexnet_training_seed_10	.027
129	alexnet_training_seed_01	.027
130	yudixie_wbh_object_class_scaling_0415_run_3	.025
131	gabor_filter_mean_abs_4x4_v2	.025
132	alexnet_training_seed_02	.024
133	alexnet_training_seed_03	.024
134	alexnet_training_seed_06	.024
135	alexnet_training_seed_07	.022
136	alexnet_training_seed_09	.021
137	yudixie_wbh_object_class_scaling_0415_run_1	.020
138	gabor_filter_entropy_4x4	.016
139	compact_resnet50_robust_V4	.017
140	gabor_filter_entropy_4x4_v2	.014
141	gabor_filter_entropy_4x4_v2_forcedCosine	.013
142	vit_huge_clip336_laion

Benchmark bibtex

None

Ceiling

0.39.

Note that scores are relative to this ceiling.

Data: Hebart2023_fmri.IT

Metric: ridgecv