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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	convnext_large_imagenet_full_seed-0	.310
37	resnet152_imagenet_full	.310
38	resnet50_st_wzc_seed7_2_323	.309
39	convnext_tiny:in12k_ft_in1k	.309
40	vit_base_patch16_clip_224:openai_ft_in1k	.308
41	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.307
42	ReAlnet01_cornet	.303
43	convnext_base_imagenet_full_seed-0	.303
44	ReAlnet03_cornet	.301
45	vit_large_patch14_clip_224:openai_ft_in1k	.301
46	resnet50-VITO-8deg-cc	.299
47	resnet50-vitoimagevidnet8	.299
48	resnet50_imagenet_full	.298
49	convnext_xxlarge_clip	.297
50	eva02_large_in22k	.299
51	ReAlnet04_cornet	.296
52	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.296
53	resnet50-SIN_IN	.295
54	Res2Net50_26w_4s	.295
55	convnext_large:fb_in22k_ft_in1k	.292
56	resnet50_robust_l2_eps1	.292
57	voneresnet-50	.292
58	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.291
59	dinov2_vitl14_reg_lc_featread	.289
60	dinov2_vitl14_reg_lc	.289
61	pnasnet_large_halfPrecision	.290
62	resnet50_ewconplus_wzc	.287
63	VOneCORnet-S	.285
64	vit_large_patch14_clip_224:laion2b_ft_in1k	.284
65	resnet50_tutorial	.281
66	resnet50_ewconplus_wzc_seed6_2_323	.280
67	resnet50_ewconplus_wzc_seed5_2__323	.279
68	resnet_SIN_IN_FT_IN	.279
69	mobilenet_v2_1_4_224	.278
70	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.276
71	custom_model_cv_18_dagger_408	.275
72	resnet50_ewconplus_wzc_seed2_2__323	.271
73	AdvProp_efficientnet-b7	.268
74	AT_efficientnet-b2	.266
75	AdvProp_efficientnet-b2	.266
76	convnext_large_clip_laion2b_finelayers	.260
77	efficientnet_b2_imagenet_full	.259
78	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.248
79	deit3_base_patch16_224_fb_in22k_ft_in1k	.248
80	AdvProp_efficientnet-b6	.246
81	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.245
82	convnext_small_imagenet_full_seed-0	.243
83	effnetb1_272x240	.243
84	convnext_tiny_imagenet_full_seed-0	.243
85	deepeisnn_ei_imagenet_resnet18	.238
86	AdvProp_efficientnet-b4	.237
87	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.237
88	resnet50-SIN	.233
89	convnext_xlarge:fb_in22k_ft_in1k	.230
90	AdvProp_efficientnet-b8	.230
91	resnext101_32x8d_wsl	.230
92	alexnet	.229
93	yudixie_wbh_object_class_scaling_0415_imagenet	.227
94	deit3_base_patch16_224_fb_in1k	.223
95	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.223
96	cvt_cvt-13-224-in1k_4_LucyV4	.222
97	ViT_L_32_imagenet1k	.221
98	ReAlnet01_cornet_fov4	.217
99	convnext_base_fb_in22k_ft_in1k	.211
100	cvt_cvt-13-384-in1k_4_LucyV4	.210
101	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.203
102	resnet-50-robust	.201
103	dinov2_vitg14_lc	.200
104	cvt_cvt-21-384-in1k_4_LucyV4	.197
105	vit_large_clip336_openai	.194
106	grcnn	.192
107	cvt_cvt-21-224-in1k_4_LucyV4	.191
108	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.191
109	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.189
110	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.183
111	convnext_base_fb_in1k	.178
112	pr1_stl10_disentangled_alpha04	.172
113	AlexNet_SIN	.167
114	convnext_large_in22k_supervised	.157
115	AlexNet_SIN_fov12	.121
116	AlexNet_SIN_fov16	.108
117	resnet18_fair	.105
118	deepeisnn_ei_cifar10_resnet18	.092
119	deepeisnn_ei_cifar10_vgg19	.087
120	convnext_base_fb_in1k_gram_aligned_dinov3	.081
121	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.070
122	hmax	.059
123	yudixie_wbh_object_class_scaling_0415_run_2	.031
124	yudixie_wbh_object_class_scaling_0415_run_0	.030
125	alexnet_training_seed_10	.027
126	alexnet_training_seed_01	.027
127	yudixie_wbh_object_class_scaling_0415_run_3	.025
128	alexnet_training_seed_02	.024
129	alexnet_training_seed_03	.024
130	alexnet_training_seed_06	.024
131	alexnet_training_seed_07	.022
132	alexnet_training_seed_09	.021
133	yudixie_wbh_object_class_scaling_0415_run_1	.020
134	compact_resnet50_robust_V4	.017
135	vit_huge_clip336_laion

Benchmark bibtex

None

Ceiling

0.39.

Note that scores are relative to this ceiling.

Data: Hebart2023_fmri.IT

Metric: ridgecv