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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-VITO-8deg-cc	.483
10	resnet50-vitoimagevidnet8	.483
11	densenet_201_pytorch	.476
12	imagenet_l2_3_0	.476
13	resnet50_robust_l2_eps3	.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	.358
41	ReAlnet04_cornet	.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	.342
64	ReAlnet08_cornet	.342
65	ReAlnet10_ALL	.341
66	ReAlnet10	.338
67	ReAlnet10_cornet	.338
68	resnet18_ecoset_full	.336
69	convnext_large:fb_in22k_ft_in1k	.336
70	ReAlnet08_ALL	.335
71	ReAlnet07_ALL	.333
72	resnet_101_v2	.333
73	resnet50_ewconplus_wzc	.332
74	ReAlnet03_ALL	.332
75	mobilenet_v2_1_0_224	.330
76	mobilenet_v2_1_4_224	.330
77	mobilenet_v2_1-4_224_pytorch	.330
78	resnet50-SIN_IN	.329
79	ReAlnet05_ALL	.329
80	ReAlnet02_ALL	.328
81	ReAlnet04_ALL	.327
82	ReAlnet01_ALL	.327
83	ReAlnet06_ALL	.326
84	resnet_152_v2	.326
85	focalnet_tiny_lrf_in1k	.326
86	resnext101_32x32d_wsl	.325
87	regnet_y_400mf	.322
88	ReAlnet09_ALL	.320
89	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.320
90	convnext_xlarge:fb_in22k_ft_in1k	.319
91	resnet50_imagenet_full	.318
92	resnet34_ecoset_full	.317
93	resnet101_imagenet_full	.316
94	resnet50-barlow	.313
95	resnet50-vicreg	.311
96	resnet152_imagenet_full	.308
97	resnet_152_v1	.307
98	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.304
99	resnet_101_v1	.297
100	VOneCORnet-S	.296
101	densenet-121	.291
102	inception_v3_pytorch	.291
103	antialiased-r50	.290
104	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.289
105	resnet_50_v1	.288
106	resnet50-sup	.288
107	densenet-201	.286
108	BiT-S-R50x1	.286
109	AlexNet_SIN	.281
110	resnet50_imagenet_100_seed-0	.279
111	resnext101_32x48d_wsl	.278
112	resnet-18_test_m	.273
113	resnet50-SIN_IN_IN	.272
114	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.271
115	yudixie_resnet50_imagenet1kpret_0_240908	.271
116	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.270
117	resnet50_ecoset_full	.266
118	convnext_tiny:in12k_ft_in1k	.265
119	AdvProp_efficientnet-b2	.265
120	AT_efficientnet-b2	.265
121	deit_large_imagenet_full_seed-0	.263
122	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.260
123	yudixie_resnet18_imagenet1kpret_0_240719	.258
124	convnext_base:clip_laiona_augreg_ft_in1k_384	.257
125	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.256
126	BiT-S-R101x1	.255
127	resnet50-SIN	.254
128	BiT-S-R50x3	.254
129	BiT-S-R152x2	.254
130	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.253
131	resnet101_ecoset_full	.253
132	densenet-169	.250
133	deit_small_imagenet_100_seed-0	.247
134	BiT-S-R101x3	.244
135	effnetb1_272x240	.242
136	convnext_femto_ols:d1_in1k	.242
137	cvt_cvt-21-224-in1k_4_LucyV4	.238
138	resnet152_ecoset_full	.238
139	convnext_small_imagenet_full_seed-0	.238
140	deit_base_imagenet_full_seed-0	.238
141	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.237
142	resnet50_eMMCR_Vanilla	.233
143	AdvProp_efficientnet-b4	.228
144	inception_v1	.224
145	convnext_tiny_imagenet_full_seed-0	.220
146	cvt_cvt-21-384-in1k_4_LucyV4	.219
147	cvt_cvt-13-224-in1k_4_LucyV4	.217
148	resnet_50_v1_spiking	.210
149	convnext_base_imagenet_full_seed-0	.209
150	deit_small_imagenet_full_seed-0	.207
151	resnet50_imagenet_10_seed-0	.206
152	vit_relpos_base_patch16_clsgap_224:sw_in1k	.203
153	vit_large_patch14_clip_224:laion2b_ft_in1k	.202
154	vit_large_patch14_clip_336:laion2b_ft_in1k	.200
155	vit_base_patch16_clip_224:openai_ft_in1k	.199
156	cvt_cvt-13-384-in1k_4_LucyV4	.198
157	convnext_small_imagenet_100_seed-0	.191
158	nasnet_mobile	.191
159	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.188
160	AdvProp_efficientnet-b6	.185
161	efficientnet-b7	.176
162	xception	.175
163	artResNet18_1	.173
164	tv_efficientnet-b1	.169
165	convnext_large_imagenet_full_seed-0	.166
166	vit_large_patch14_clip_224:openai_ft_in1k	.157
167	resnet_50_v1_spiking_l4a	.155
168	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.153
169	pnasnet_large	.150
170	AdvProp_efficientnet-b7	.142
171	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.139
172	deit_small_imagenet_10_seed-0	.133
173	AdvProp_efficientnet-b8	.132
174	resnet_50_v1_spiking_l4	.130
175	mobilenet_v2_0_5_192	.129
176	inception_v3	.117
177	mobilenet_v2_0_5_224	.117
178	resnet50_imagenet_1_seed-0	.116
179	mobilenet_v2_0_75_160	.115
180	convnext_small_imagenet_10_seed-0	.114
181	nasnet_large	.113
182	inception_v4	.110
183	resnet50_primary_visual_cortex	.105
184	mobilenet_v2_1_0_128	.105
185	mobilenet_v2_0_75_192	.103
186	mobilenet_v2_0_75_224	.099
187	yudixie_resnet50_category_class_0_240908	.097
188	mobilenet_v2_1_0_160	.089
189	yudixie_resnet18_object_class_0_240719	.089
190	yudixie_resnet50_object_class_0_240908	.088
191	blt_vs	.086
192	mobilenet_v2_1_0_192	.085
193	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.083
194	convnext_small_imagenet_1_seed-0	.083
195	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.080
196	compact_resnet50_IT	.072
197	yudixie_resnet18_category_class_0_240719	.071
198	yudixie_resnet18_distance_reg_0_240719	.070
199	mobilenet_v2_1_3_224	.070
200	BiT-S-R152x4	.059
201	yudixie_resnet18_distance_translation_rotation_0_240719	.056
202	yudixie_resnet18_distance_rotation_0_240719	.055
203	alexnet_training_seed_09	.053
204	compact_vgg19_IT	.053
205	ViT_L_32_imagenet1k	.052
206	alexnet_training_seed_08	.051
207	alexnet_training_seed_10	.051
208	alexnet_training_seed_05	.051
209	compact_resnet50_robust_IT	.049
210	alexnet_training_seed_01	.048
211	alexnet_training_seed_03	.048
212	yudixie_resnet50_distance_rotation_0_240908	.047
213	alexnet_training_seed_02	.047
214	yudixie_resnet18_distance_translation_0_240719	.046
215	alexnet_training_seed_04	.045
216	briaai_rmbg_1_4	.045
217	alexnet_training_seed_07	.045
218	compact_cornets_V4	.042
219	alexnet_training_seed_06	.041
220	compact_cornets_IT	.041
221	yudixie_resnet50_distance_translation_rotation_0_240908	.040
222	yudixie_resnet50_distance_translation_0_240908	.040
223	yudixie_resnet50_distance_reg_0_240908	.035
224	yudixie_resnet18_rotation_reg_0_240719	.032
225	compact_resnet50_V4	.031
226	deit_small_imagenet_1_seed-0	.029
227	yudixie_resnet50_random_0_240908	.029
228	mobilevit_small	.029
229	yudixie_resnet18_translation_rotation_0_240719	.028
230	yolos_tiny	.025
231	yudixie_resnet50_translation_rotation_0_240908	.024
232	yudixie_resnet18_random_0_240719	.023
233	hmax	.022
234	compact_resnet50_robust_V4	.019
235	yudixie_resnet50_rotation_reg_0_240908	.013
236	resnet_50_v1_spiking_l3	.009
237	yudixie_resnet18_translation_reg_0_240719	.009
238	compact_vgg19_V4	.003
239	yudixie_resnet50_translation_reg_0_240908	.003
240	omnivore_swinB	.000
241	omnivore_swinS	.000
242	ConvLSTM
243	PredRNN
244	TAU
245	MIM
246	antialiased-rnext101_32x8d
247	SimVP

Benchmark bibtex

None

Ceiling

0.68.

Note that scores are relative to this ceiling.

Data: Hebart2023

Metric: match