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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Hebart2023_fmri.V4-ridgecv")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	ijepa_vith14_linhead	.476
2	custom_model_cv_18_dagger_408	.459
3	resnet50_supervised_linhead	.452
4	resnet50_byol_linhead	.397
5	dinov2_vitg14_reg_lc	.396
6	resnet50_barlowtwins_linhead	.394
7	resnet50_simsiam_linhead	.391
8	dinov2_vitl14_lc_res336	.385
9	resnet50_vicreg_linhead	.386
10	resnet50_simclr_linhead_01norm	.374
11	dinov2_vitg14_lc	.370
12	cvt_cvt-21-224-in1k_4_LucyV4	.367
13	resnet50_st_wzc_seed7_2_323	.367
14	vit_large_clip336_openai	.365
15	resnet152_imagenet_full	.363
16	resnet152_ecoset_full	.361
17	cvt_cvt-21-384-in1k_4_LucyV4	.358
18	resnet50_st_wzc_seed3_2_323	.358
19	resnet101_imagenet_full	.355
20	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.355
21	deit3_base_patch16_224_fb_in22k_ft_in1k	.355
22	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.352
23	convnext_xxlarge_clip	.351
24	ReAlnet08_cornet	.353
25	ReAlnet01_cornet_fov12	.349
26	ReAlnet04_cornet	.351
27	convnext_large_in22k_supervised	.347
28	ReAlnet07_cornet	.349
29	convnext_large:fb_in22k_ft_in1k	.344
30	resnet101_ecoset_full	.345
31	convnext_xlarge:fb_in22k_ft_in1k	.346
32	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.343
33	ReAlnet05_cornet	.346
34	ReAlnet10_cornet	.346
35	eva02_large_in22k	.341
36	vit_relpos_base_patch16_clsgap_224:sw_in1k	.341
37	ReAlnet01_cornet_fov16	.344
38	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.342
39	ReAlnet06_cornet	.345
40	cvt_cvt-13-224-in1k_4_LucyV4	.342
41	ReAlnet01_cornet	.345
42	ReAlnet03_cornet	.344
43	voneresnet-50	.340
44	ReAlnet09_cornet	.342
45	ReAlnet02_cornet	.342
46	shufflenet_v2_x1_0	.339
47	cvt_cvt-13-384-in1k_4_LucyV4	.338
48	resnet50-moclr8deg	.337
49	convnext_tiny:in12k_ft_in1k	.331
50	resnet50_ewconplus_wzc	.329
51	AT_efficientnet-b2	.330
52	AdvProp_efficientnet-b2	.330
53	resnet50-VITO-8deg-cc	.327
54	resnet50-vitoimagevidnet8	.327
55	deit_small_imagenet_full_seed-0	.325
56	vit_large_patch14_clip_224:openai_ft_in1k	.324
57	resnet50_ewconplus_wzc_seed6_2_323	.324
58	resnet_50_v2	.321
59	vit_large_patch14_clip_336:laion2b_ft_in1k	.322
60	deepeisnn_ei_imagenet_resnet18	.322
61	dinov2_vitl14_reg_lc_featread	.322
62	dinov2_vitl14_reg_lc	.322
63	focalnet_tiny_lrf_in1k	.320
64	efficientnet_b2_imagenet_full	.319
65	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.317
66	AdvProp_efficientnet-b4	.320
67	resnet50_ewconplus_wzc_seed5_2__323	.315
68	alexnet	.315
69	VOneCORnet-S	.316
70	resnet50-SIN	.316
71	seresnext50_32x4d	.316
72	mehrer2020_alexnet_training_seed_04	.313
73	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.313
74	resnet50_ewconplus_wzc_seed2_2__323	.312
75	convnext_tiny_sup	.311
76	mehrer2020_alexnet_training_seed_02	.309
77	Res2Net50_26w_4s	.309
78	mehrer2020_alexnet_training_seed_05	.307
79	mobilenet_v2_1_4_224	.307
80	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.307
81	mehrer2020_alexnet_training_seed_03	.304
82	AdvProp_efficientnet-b8	.306
83	mehrer2020_alexnet_training_seed_01	.302
84	resnet34_ecoset_full	.301
85	resnext101_32x32d_wsl	.302
86	resnet50-SIN_IN	.299
87	mehrer2020_alexnet_training_seed_06	.297
88	resnet18_imagenet_full	.297
89	deit3_base_patch16_224_fb_in1k	.296
90	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.296
91	efficientnet_b0	.298
92	convnext_femto_ols:d1_in1k	.295
93	mehrer2020_alexnet_training_seed_07	.296
94	convnext_tiny_imagenet_full_seed-0	.293
95	resnet_SIN_IN_FT_IN	.293
96	resnet50-SIN_IN_IN	.293
97	mehrer2020_alexnet_training_seed_08	.295
98	convnext_large_clip_laion2b_finelayers	.293
99	mehrer2020_alexnet_training_seed_09	.293
100	resnext101_32x16d_wsl	.292
101	persistence_memory	.292
102	resnext101_32x48d_wsl	.292
103	resnet50_imagenet_full	.290
104	vit_base_patch16_clip_224:openai_ft_in1k	.291
105	resnext101_32x8d_wsl	.289
106	resnet50_st_wzc_seed6_2_323	.288
107	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.289
108	yudixie_wbh_object_class_scaling_0415_imagenet	.288
109	deit_base_imagenet_full_seed-0	.284
110	efficientnet_b1_imagenet_full	.284
111	resnet50-sup	.283
112	resnet50_tutorial	.283
113	resnet_50_v1_spiking	.283
114	resnet_50_v1	.283
115	deit_large_imagenet_full_seed-0	.280
116	antialiased-r50	.281
117	convnext_base_imagenet_full_seed-0	.279
118	resnet34_imagenet_full	.280
119	voneresnet-50-non_stochastic	.278
120	eicircuit_resnet18_bn_20260919	.277
121	resnet18_ecoset_full	.278
122	convnext_large_imagenet_full_seed-0	.276
123	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.275
124	vit_large_patch14_clip_224:laion2b_ft_in1k	.277
125	resnet50_ijepacnn_linhead	.276
126	resnet50_imagenet_100_seed-0	.272
127	resnet50_cpc_linhead	.274
128	predictive_coding_pc	.270
129	resnet50_ecoset_full	.271
130	convnext_small_imagenet_full_seed-0	.270
131	convnext_base:clip_laiona_augreg_ft_in1k_384	.270
132	efficientnet_b0_imagenet_full	.270
133	dinov2_vitl14_lc	.268
134	AlexNet_SIN_fov12	.265
135	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.265
136	pr1_stl10_disentangled_alpha04	.263
137	ReAlnet01_cornet_fov4	.264
138	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.261
139	AlexNet_SIN	.261
140	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.259
141	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.260
142	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.256
143	grcnn	.254
144	mehrer2020_alexnet_training_seed_01_fov16	.252
145	resnet-50-robust	.252
146	resnet50_robust_l2_eps1	.249
147	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.247
148	densenet-121	.244
149	mehrer2020_alexnet_training_seed_01_fov12	.242
150	convnext_base_fb_in1k_gram_aligned_dinov3	.241
151	convnext_base_fb_in1k	.241
152	convnext_small_imagenet_100_seed-0	.235
153	mehrer2020_alexnet_training_seed_10	.231
154	deit_small_imagenet_100_seed-0	.229
155	AdvProp_efficientnet-b7	.235
156	resnet18_fair	.229
157	regnety_032	.230
158	AdvProp_efficientnet-b6	.225
159	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.218
160	convnext_base_fb_in22k_ft_in1k	.218
161	AlexNet_SIN_fov16	.217
162	effnetb1_272x240	.217
163	deepeisnn_ei_cifar10_resnet18	.212
164	mehrer2020_alexnet_training_seed_01_fov4	.209
165	deepeisnn_ei_cifar10_vgg19	.207
166	persistence_mem	.199
167	ViT_L_32_imagenet1k	.200
168	AlexNet_SIN_fov4	.170
169	gabor_filter_entropy_4x4_v2_forcedCosine	.173
170	gabor_filter_entropy_4x4_v2	.170
171	gabor_filter_entropy_4x4	.163
172	yudixie_wbh_object_class_scaling_0415_run_2	.147
173	vonealexnet	.138
174	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.134
175	resnet50_imagenet_10_seed-0	.122
176	alexnet_training_seed_06	.113
177	alexnet_training_seed_02	.111
178	alexnet_training_seed_03	.108
179	yudixie_wbh_object_class_scaling_0415_run_1	.106
180	gabor_filter_mean_abs_4x4_v2	.105
181	alexnet_training_seed_09	.102
182	yudixie_wbh_object_class_scaling_0415_run_0	.104
183	alexnet_training_seed_01	.101
184	alexnet_training_seed_07	.098
185	alexnet_training_seed_10	.096
186	hmax	.096
187	compact_resnet50_robust_V4	.077
188	resnet50_untrained_linhead	.074
189	yudixie_wbh_object_class_scaling_0415_run_3	.072
190	vith14_untrained_linhead	.067
191	resnet50_imagenet_1_seed-0	.047
192	deit_small_imagenet_10_seed-0	.042
193	convnext_small_imagenet_10_seed-0	.038
194	deit_small_imagenet_1_seed-0	.033
195	convnext_small_imagenet_1_seed-0	.031
196	vith14_supervised_linhead
197	convnext_xxlarge:clip_laion2b_soup_ft_in1k
198	vit_huge_clip336_laion
199	pnasnet_large_halfPrecision

Benchmark bibtex

None

Ceiling

0.41.

Note that scores are relative to this ceiling.

Data: Hebart2023_fmri.V4

Metric: ridgecv