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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	resnet152_ecoset_full	.352
8	deit_small_imagenet_full_seed-0	.348
9	convnext_femto_ols:d1_in1k	.344
10	resnet50-moclr8deg	.344
11	resnet50_simclr_linhead_01norm	.344
12	dinov2_vitl14_lc_res336	.341
13	predictive_coding_pc	.337
14	dinov2_vitl14_lc	.337
15	resnet101_ecoset_full	.336
16	focalnet_tiny_lrf_in1k	.336
17	dinov2_vitg14_reg_lc	.335
18	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.334
19	convnext_base:clip_laiona_augreg_ft_in1k_384	.333
20	vit_relpos_base_patch16_clsgap_224:sw_in1k	.331
21	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.331
22	resnet_50_v2	.329
23	deit_base_imagenet_full_seed-0	.329
24	antialiased-r50	.327
25	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.326
26	resnet101_imagenet_full	.324
27	resnet50_vicreg_linhead	.321
28	densenet-121	.321
29	resnet_50_v1	.317
30	resnet_50_v1_spiking	.317
31	resnet50-sup	.317
32	shufflenet_v2_x1_0	.318
33	resnet50_st_wzc_seed6_2_323	.316
34	convnext_tiny_sup	.315
35	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.315
36	resnet50_byol_linhead	.315
37	resnet50_simsiam_linhead	.315
38	voneresnet-50-non_stochastic	.313
39	ReAlnet01_cornet_fov12	.313
40	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.313
41	resnet50_st_wzc_seed3_2_323	.312
42	efficientnet_b0	.312
43	resnet50-SIN_IN_IN	.312
44	seresnext50_32x4d	.313
45	convnext_large_imagenet_full_seed-0	.310
46	resnet152_imagenet_full	.310
47	resnet50_st_wzc_seed7_2_323	.309
48	convnext_tiny:in12k_ft_in1k	.309
49	vit_base_patch16_clip_224:openai_ft_in1k	.308
50	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.307
51	resnet50_barlowtwins_linhead	.305
52	resnet50_ecoset_full	.305
53	ReAlnet01_cornet_fov16	.303
54	ReAlnet01_cornet	.303
55	convnext_base_imagenet_full_seed-0	.303
56	ReAlnet03_cornet	.301
57	vit_large_patch14_clip_224:openai_ft_in1k	.301
58	resnet50-VITO-8deg-cc	.299
59	resnet50-vitoimagevidnet8	.299
60	ReAlnet09_cornet	.299
61	ReAlnet02_cornet	.299
62	resnet50_imagenet_full	.298
63	ReAlnet10_cornet	.298
64	ReAlnet07_cornet	.297
65	convnext_xxlarge_clip	.297
66	ReAlnet08_cornet	.297
67	eva02_large_in22k	.299
68	ReAlnet05_cornet	.296
69	ReAlnet04_cornet	.296
70	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.296
71	resnet50-SIN_IN	.295
72	Res2Net50_26w_4s	.295
73	convnext_large:fb_in22k_ft_in1k	.292
74	resnet50_robust_l2_eps1	.292
75	voneresnet-50	.292
76	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.291
77	ReAlnet06_cornet	.290
78	dinov2_vitl14_reg_lc_featread	.289
79	dinov2_vitl14_reg_lc	.289
80	pnasnet_large_halfPrecision	.290
81	resnet50_ewconplus_wzc	.287
82	VOneCORnet-S	.285
83	vit_large_patch14_clip_224:laion2b_ft_in1k	.284
84	resnet34_ecoset_full	.284
85	resnet50_tutorial	.281
86	resnet50_ewconplus_wzc_seed6_2_323	.280
87	resnet50_ewconplus_wzc_seed5_2__323	.279
88	resnet_SIN_IN_FT_IN	.279
89	eicircuit_resnet18_bn_20260919	.279
90	regnety_032	.279
91	mobilenet_v2_1_4_224	.278
92	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.276
93	custom_model_cv_18_dagger_408	.275
94	resnet50_ewconplus_wzc_seed2_2__323	.271
95	resnet34_imagenet_full	.270
96	AdvProp_efficientnet-b7	.268
97	AT_efficientnet-b2	.266
98	AdvProp_efficientnet-b2	.266
99	convnext_large_clip_laion2b_finelayers	.260
100	ijepa_vith14_linhead	.260
101	efficientnet_b2_imagenet_full	.259
102	efficientnet_b1_imagenet_full	.257
103	resnet18_ecoset_full	.255
104	resnet18_imagenet_full	.254
105	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.248
106	deit3_base_patch16_224_fb_in22k_ft_in1k	.248
107	AdvProp_efficientnet-b6	.246
108	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.245
109	convnext_small_imagenet_full_seed-0	.243
110	effnetb1_272x240	.243
111	convnext_tiny_imagenet_full_seed-0	.243
112	efficientnet_b0_imagenet_full	.241
113	resnet50_imagenet_100_seed-0	.240
114	deepeisnn_ei_imagenet_resnet18	.238
115	AdvProp_efficientnet-b4	.237
116	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.237
117	resnet50-SIN	.233
118	convnext_xlarge:fb_in22k_ft_in1k	.230
119	AdvProp_efficientnet-b8	.230
120	resnext101_32x8d_wsl	.230
121	alexnet	.229
122	yudixie_wbh_object_class_scaling_0415_imagenet	.227
123	deit3_base_patch16_224_fb_in1k	.223
124	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.223
125	cvt_cvt-13-224-in1k_4_LucyV4	.222
126	ViT_L_32_imagenet1k	.221
127	ReAlnet01_cornet_fov4	.217
128	convnext_base_fb_in22k_ft_in1k	.211
129	cvt_cvt-13-384-in1k_4_LucyV4	.210
130	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.203
131	mehrer2020_alexnet_training_seed_02	.203
132	resnet-50-robust	.201
133	dinov2_vitg14_lc	.200
134	cvt_cvt-21-384-in1k_4_LucyV4	.197
135	mehrer2020_alexnet_training_seed_09	.194
136	vit_large_clip336_openai	.194
137	mehrer2020_alexnet_training_seed_05	.193
138	grcnn	.192
139	cvt_cvt-21-224-in1k_4_LucyV4	.191
140	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.191
141	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.189
142	mehrer2020_alexnet_training_seed_07	.188
143	mehrer2020_alexnet_training_seed_06	.184
144	mehrer2020_alexnet_training_seed_08	.184
145	mehrer2020_alexnet_training_seed_03	.184
146	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.183
147	mehrer2020_alexnet_training_seed_10	.183
148	mehrer2020_alexnet_training_seed_01_fov12	.181
149	convnext_base_fb_in1k	.178
150	mehrer2020_alexnet_training_seed_04	.175
151	mehrer2020_alexnet_training_seed_01_fov4	.174
152	pr1_stl10_disentangled_alpha04	.172
153	mehrer2020_alexnet_training_seed_01	.168
154	AlexNet_SIN	.167
155	convnext_large_in22k_supervised	.157
156	convnext_small_imagenet_100_seed-0	.137
157	deit_small_imagenet_100_seed-0	.136
158	mehrer2020_alexnet_training_seed_01_fov16	.131
159	AlexNet_SIN_fov12	.121
160	AlexNet_SIN_fov16	.108
161	resnet18_fair	.105
162	AlexNet_SIN_fov4	.106
163	deepeisnn_ei_cifar10_resnet18	.092
164	deepeisnn_ei_cifar10_vgg19	.087
165	convnext_base_fb_in1k_gram_aligned_dinov3	.081
166	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.070
167	hmax	.059
168	yudixie_wbh_object_class_scaling_0415_run_2	.031
169	yudixie_wbh_object_class_scaling_0415_run_0	.030
170	alexnet_training_seed_10	.027
171	alexnet_training_seed_01	.027
172	yudixie_wbh_object_class_scaling_0415_run_3	.025
173	gabor_filter_mean_abs_4x4_v2	.025
174	alexnet_training_seed_02	.024
175	alexnet_training_seed_03	.024
176	alexnet_training_seed_06	.024
177	alexnet_training_seed_07	.022
178	alexnet_training_seed_09	.021
179	yudixie_wbh_object_class_scaling_0415_run_1	.020
180	gabor_filter_entropy_4x4	.016
181	compact_resnet50_robust_V4	.017
182	gabor_filter_entropy_4x4_v2	.014
183	gabor_filter_entropy_4x4_v2_forcedCosine	.013
184	vit_huge_clip336_laion

Benchmark bibtex

None

Ceiling

0.39.

Note that scores are relative to this ceiling.

Data: Hebart2023_fmri.IT

Metric: ridgecv