Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Hebart2023-match")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	bp_resnet50_julios	1.0
2	resnet50-moclr8deg	.534
3	resnet50_robust_l2_eps1	.503
4	vonegrcnn_47e	.498
5	vonegrcnn_62e_nobn	.494
6	vonegrcnn_52e_full	.490
7	resnet_SIN_IN_FT_IN	.489
8	resnet50_tutorial	.488
9	resnet50-vitoimagevidnet8	.483
10	resnet50-VITO-8deg-cc	.483
11	densenet_201_pytorch	.476
12	resnet50_robust_l2_eps3	.476
13	imagenet_l2_3_0	.476
14	resnet18_imagenet21kP	.470
15	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.469
16	antialias-resnet152	.464
17	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.463
18	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.416
19	resnet_50_v2	.408
20	vgg_16	.407
21	resnext101_32x8d_wsl	.407
22	vgg_19	.404
23	efficientnet_b0_imagenet_full	.399
24	efficientnet_b1_imagenet_full	.396
25	efficientnet_b2_imagenet_full	.396
26	resnext101_32x16d_wsl	.387
27	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.381
28	shufflenet_v2_x1_0	.379
29	resnet-152_v2_pytorch	.375
30	alexnet_ks_torevert	.375
31	voneresnet-50-robust	.372
32	custom_model_cv_18_dagger_408	.371
33	voneresnet-50-non_stochastic	.368
34	ReAlnet09	.364
35	ReAlnet09_cornet	.364
36	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.363
37	grcnn	.361
38	resnet18_imagenet_full	.358
39	resnet34_imagenet_full	.358
40	ReAlnet04_cornet	.358
41	ReAlnet04	.358
42	voneresnet-50	.357
43	grcnn_109	.356
44	Res2Net50_26w_4s	.354
45	ReAlnet05	.354
46	ReAlnet05_cornet	.354
47	resnet50-vicregl0p9	.352
48	resnet-50-robust	.352
49	pnasnet_large_pytorch	.351
50	ReAlnet06	.350
51	ReAlnet06_cornet	.350
52	ReAlnet03_cornet	.349
53	ReAlnet03	.349
54	ReAlnet07	.347
55	ReAlnet07_cornet	.347
56	ReAlnet01_cornet	.347
57	ReAlnet01	.347
58	ReAlnet02_cornet	.346
59	ReAlnet02	.346
60	resnet50-vicregl0p75	.345
61	convnext_tiny_sup	.344
62	efficientnet_b0	.342
63	ReAlnet08_cornet	.342
64	ReAlnet08	.342
65	ReAlnet10_cornet	.338
66	ReAlnet10	.338
67	resnet18_ecoset_full	.336
68	convnext_large:fb_in22k_ft_in1k	.336
69	resnet_101_v2	.333
70	mobilenet_v2_1_0_224	.330
71	mobilenet_v2_1_4_224	.330
72	mobilenet_v2_1-4_224_pytorch	.330
73	resnet50-SIN_IN	.329
74	resnet_152_v2	.326
75	focalnet_tiny_lrf_in1k	.326
76	resnext101_32x32d_wsl	.325
77	regnet_y_400mf	.322
78	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.320
79	convnext_xlarge:fb_in22k_ft_in1k	.319
80	resnet50_imagenet_full	.318
81	resnet34_ecoset_full	.317
82	resnet101_imagenet_full	.316
83	resnet50-barlow	.313
84	resnet50-vicreg	.311
85	resnet152_imagenet_full	.308
86	resnet_152_v1	.307
87	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.304
88	resnet_101_v1	.297
89	VOneCORnet-S	.296
90	densenet-121	.291
91	inception_v3_pytorch	.291
92	antialiased-r50	.290
93	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.289
94	resnet_50_v1	.288
95	resnet50-sup	.288
96	densenet-201	.286
97	BiT-S-R50x1	.286
98	AlexNet_SIN	.281
99	resnet50_imagenet_100_seed-0	.279
100	resnext101_32x48d_wsl	.278
101	resnet-18_test_m	.273
102	resnet50-SIN_IN_IN	.272
103	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.271
104	yudixie_resnet50_imagenet1kpret_0_240908	.271
105	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.270
106	resnet50_ecoset_full	.266
107	convnext_tiny:in12k_ft_in1k	.265
108	AT_efficientnet-b2	.265
109	AdvProp_efficientnet-b2	.265
110	deit_large_imagenet_full_seed-0	.263
111	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.260
112	yudixie_resnet18_imagenet1kpret_0_240719	.258
113	convnext_base:clip_laiona_augreg_ft_in1k_384	.257
114	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.256
115	BiT-S-R101x1	.255
116	resnet50-SIN	.254
117	BiT-S-R50x3	.254
118	BiT-S-R152x2	.254
119	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.253
120	resnet101_ecoset_full	.253
121	densenet-169	.250
122	deit_small_imagenet_100_seed-0	.247
123	BiT-S-R101x3	.244
124	effnetb1_272x240	.242
125	convnext_femto_ols:d1_in1k	.242
126	cvt_cvt-21-224-in1k_4_LucyV4	.238
127	resnet152_ecoset_full	.238
128	convnext_small_imagenet_full_seed-0	.238
129	deit_base_imagenet_full_seed-0	.238
130	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.237
131	resnet50_eMMCR_Vanilla	.233
132	AdvProp_efficientnet-b4	.228
133	inception_v1	.224
134	convnext_tiny_imagenet_full_seed-0	.220
135	cvt_cvt-21-384-in1k_4_LucyV4	.219
136	cvt_cvt-13-224-in1k_4_LucyV4	.217
137	resnet_50_v1_spiking	.210
138	convnext_base_imagenet_full_seed-0	.209
139	deit_small_imagenet_full_seed-0	.207
140	resnet50_imagenet_10_seed-0	.206
141	vit_relpos_base_patch16_clsgap_224:sw_in1k	.203
142	vit_large_patch14_clip_224:laion2b_ft_in1k	.202
143	vit_large_patch14_clip_336:laion2b_ft_in1k	.200
144	vit_base_patch16_clip_224:openai_ft_in1k	.199
145	cvt_cvt-13-384-in1k_4_LucyV4	.198
146	convnext_small_imagenet_100_seed-0	.191
147	nasnet_mobile	.191
148	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.188
149	AdvProp_efficientnet-b6	.185
150	efficientnet-b7	.176
151	xception	.175
152	artResNet18_1	.173
153	tv_efficientnet-b1	.169
154	convnext_large_imagenet_full_seed-0	.166
155	vit_large_patch14_clip_224:openai_ft_in1k	.157
156	resnet_50_v1_spiking_l4a	.155
157	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.153
158	pnasnet_large	.150
159	AdvProp_efficientnet-b7	.142
160	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.139
161	deit_small_imagenet_10_seed-0	.133
162	AdvProp_efficientnet-b8	.132
163	resnet_50_v1_spiking_l4	.130
164	mobilenet_v2_0_5_192	.129
165	inception_v3	.117
166	mobilenet_v2_0_5_224	.117
167	resnet50_imagenet_1_seed-0	.116
168	mobilenet_v2_0_75_160	.115
169	convnext_small_imagenet_10_seed-0	.114
170	nasnet_large	.113
171	inception_v4	.110
172	resnet50_primary_visual_cortex	.105
173	mobilenet_v2_1_0_128	.105
174	mobilenet_v2_0_75_192	.103
175	mobilenet_v2_0_75_224	.099
176	yudixie_resnet50_category_class_0_240908	.097
177	mobilenet_v2_1_0_160	.089
178	yudixie_resnet18_object_class_0_240719	.089
179	yudixie_resnet50_object_class_0_240908	.088
180	blt_vs	.086
181	mobilenet_v2_1_0_192	.085
182	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.083
183	convnext_small_imagenet_1_seed-0	.083
184	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.080
185	yudixie_resnet18_category_class_0_240719	.071
186	yudixie_resnet18_distance_reg_0_240719	.070
187	mobilenet_v2_1_3_224	.070
188	BiT-S-R152x4	.059
189	yudixie_resnet18_distance_translation_rotation_0_240719	.056
190	yudixie_resnet18_distance_rotation_0_240719	.055
191	alexnet_training_seed_09	.053
192	ViT_L_32_imagenet1k	.052
193	alexnet_training_seed_08	.051
194	alexnet_training_seed_10	.051
195	alexnet_training_seed_05	.051
196	alexnet_training_seed_01	.048
197	alexnet_training_seed_03	.048
198	yudixie_resnet50_distance_rotation_0_240908	.047
199	alexnet_training_seed_02	.047
200	yudixie_resnet18_distance_translation_0_240719	.046
201	alexnet_training_seed_04	.045
202	briaai_rmbg_1_4	.045
203	alexnet_training_seed_07	.045
204	alexnet_training_seed_06	.041
205	yudixie_resnet50_distance_translation_rotation_0_240908	.040
206	yudixie_resnet50_distance_translation_0_240908	.040
207	yudixie_resnet50_distance_reg_0_240908	.035
208	yudixie_resnet18_rotation_reg_0_240719	.032
209	deit_small_imagenet_1_seed-0	.029
210	yudixie_resnet50_random_0_240908	.029
211	mobilevit_small	.029
212	yudixie_resnet18_translation_rotation_0_240719	.028
213	yolos_tiny	.025
214	yudixie_resnet50_translation_rotation_0_240908	.024
215	yudixie_resnet18_random_0_240719	.023
216	hmax	.022
217	yudixie_resnet50_rotation_reg_0_240908	.013
218	resnet_50_v1_spiking_l3	.009
219	yudixie_resnet18_translation_reg_0_240719	.009
220	yudixie_resnet50_translation_reg_0_240908	.003
221	omnivore_swinB	.000
222	omnivore_swinS	.000
223	antialiased-rnext101_32x8d
224	MIM
225	TAU
226	PredRNN
227	SimVP
228	ConvLSTM

Benchmark bibtex

None

Ceiling

0.68.

Note that scores are relative to this ceiling.

Data: Hebart2023

Metric: match