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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-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	vit_base_patch14_reg4_dinov2-lvd142m	.463
18	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.463
19	vit_base_patch14_dinov2-lvd142m	.452
20	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.416
21	resnet_50_v2	.408
22	vgg_16	.407
23	resnext101_32x8d_wsl	.407
24	vgg_19	.404
25	efficientnet_b0_imagenet_full	.399
26	efficientnet_b1_imagenet_full	.396
27	efficientnet_b2_imagenet_full	.396
28	vit_base_patch16_dinov3-lvd1689m	.395
29	resnext101_32x16d_wsl	.387
30	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.381
31	shufflenet_v2_x1_0	.379
32	resnet-152_v2_pytorch	.375
33	alexnet_ks_torevert	.375
34	alexnet	.375
35	voneresnet-50-robust	.372
36	custom_model_cv_18_dagger_408	.371
37	voneresnet-50-non_stochastic	.368
38	ReAlnet09_cornet	.364
39	ReAlnet09	.364
40	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.363
41	grcnn	.361
42	resnet18_imagenet_full	.358
43	resnet34_imagenet_full	.358
44	ReAlnet04_cornet	.358
45	ReAlnet04	.358
46	ReAlnet01_cornet_fov12	.357
47	voneresnet-50	.357
48	grcnn_109	.356
49	Res2Net50_26w_4s	.354
50	ReAlnet05_cornet	.354
51	ReAlnet05	.354
52	resnet50-vicregl0p9	.352
53	resnet-50-robust	.352
54	pnasnet_large_pytorch	.351
55	ReAlnet06_cornet	.350
56	ReAlnet06	.350
57	ReAlnet03_cornet	.349
58	ReAlnet03	.349
59	ReAlnet07_cornet	.347
60	ReAlnet07	.347
61	ReAlnet01_cornet	.347
62	ReAlnet01	.347
63	ReAlnet01_cornet_fov16	.346
64	ReAlnet02_cornet	.346
65	ReAlnet02	.346
66	resnet50-vicregl0p75	.345
67	convnext_tiny_sup	.344
68	efficientnet_b0	.342
69	ReAlnet08_cornet	.342
70	ReAlnet08	.342
71	ReAlnet10_ALL	.341
72	ReAlnet10_cornet	.338
73	ReAlnet10	.338
74	alexnet_fov4	.336
75	resnet18_ecoset_full	.336
76	convnext_large:fb_in22k_ft_in1k	.336
77	ReAlnet08_ALL	.335
78	ReAlnet07_ALL	.333
79	resnet_101_v2	.333
80	resnet50_ewconplus_wzc	.332
81	ReAlnet03_ALL	.332
82	mobilenet_v2_1_0_224	.330
83	mobilenet_v2_1_4_224	.330
84	mobilenet_v2_1-4_224_pytorch	.330
85	resnet50-SIN_IN	.329
86	ReAlnet05_ALL	.329
87	ReAlnet02_ALL	.328
88	ReAlnet04_ALL	.327
89	ReAlnet01_ALL	.327
90	ReAlnet06_ALL	.326
91	resnet_152_v2	.326
92	focalnet_tiny_lrf_in1k	.326
93	resnext101_32x32d_wsl	.325
94	regnet_y_400mf	.322
95	ReAlnet09_ALL	.320
96	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.320
97	convnext_xlarge:fb_in22k_ft_in1k	.319
98	alexnet_fov12	.319
99	resnet50_imagenet_full	.318
100	resnet34_ecoset_full	.317
101	resnet101_imagenet_full	.316
102	resnet50-barlow	.313
103	resnet50-vicreg	.311
104	resnet152_imagenet_full	.308
105	resnet_152_v1	.307
106	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.304
107	resnet_101_v1	.297
108	VOneCORnet-S	.296
109	densenet-121	.291
110	inception_v3_pytorch	.291
111	antialiased-r50	.290
112	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.289
113	alexnet_fov16	.289
114	resnet_50_v1	.288
115	resnet50-sup	.288
116	densenet-201	.286
117	BiT-S-R50x1	.286
118	AlexNet_SIN	.281
119	resnet50_imagenet_100_seed-0	.279
120	resnext101_32x48d_wsl	.278
121	resnet-18_test_m	.273
122	resnet50-SIN_IN_IN	.272
123	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.271
124	yudixie_resnet50_imagenet1kpret_0_240908	.271
125	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.270
126	ReAlnet01_cornet_fov4	.268
127	resnet50_ecoset_full	.266
128	convnext_tiny:in12k_ft_in1k	.265
129	AT_efficientnet-b2	.265
130	AdvProp_efficientnet-b2	.265
131	deit_large_imagenet_full_seed-0	.263
132	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.260
133	yudixie_resnet18_imagenet1kpret_0_240719	.258
134	convnext_base:clip_laiona_augreg_ft_in1k_384	.257
135	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.256
136	BiT-S-R101x1	.255
137	resnet50-SIN	.254
138	BiT-S-R50x3	.254
139	BiT-S-R152x2	.254
140	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.253
141	resnet101_ecoset_full	.253
142	densenet-169	.250
143	deit_small_imagenet_100_seed-0	.247
144	BiT-S-R101x3	.244
145	effnetb1_272x240	.242
146	convnext_femto_ols:d1_in1k	.242
147	cvt_cvt-21-224-in1k_4_LucyV4	.238
148	resnet152_ecoset_full	.238
149	convnext_small_imagenet_full_seed-0	.238
150	deit_base_imagenet_full_seed-0	.238
151	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.237
152	resnet50_eMMCR_Vanilla	.233
153	AlexNet_SIN_fov12	.232
154	AdvProp_efficientnet-b4	.228
155	inception_v1	.224
156	AlexNet_SIN_fov4	.221
157	convnext_tiny_imagenet_full_seed-0	.220
158	cvt_cvt-21-384-in1k_4_LucyV4	.219
159	cvt_cvt-13-224-in1k_4_LucyV4	.217
160	AlexNet_SIN_fov16	.215
161	resnet_50_v1_spiking	.210
162	convnext_base_imagenet_full_seed-0	.209
163	deit_small_imagenet_full_seed-0	.207
164	resnet50_imagenet_10_seed-0	.206
165	vit_relpos_base_patch16_clsgap_224:sw_in1k	.203
166	vit_large_patch14_clip_224:laion2b_ft_in1k	.202
167	vit_large_patch14_clip_336:laion2b_ft_in1k	.200
168	vit_base_patch16_clip_224:openai_ft_in1k	.199
169	cvt_cvt-13-384-in1k_4_LucyV4	.198
170	convnext_small_imagenet_100_seed-0	.191
171	nasnet_mobile	.191
172	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.188
173	AdvProp_efficientnet-b6	.185
174	efficientnet-b7	.176
175	xception	.175
176	artResNet18_1	.173
177	tv_efficientnet-b1	.169
178	convnext_large_imagenet_full_seed-0	.166
179	vit_large_patch14_clip_224:openai_ft_in1k	.157
180	resnet_50_v1_spiking_l4a	.155
181	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.153
182	pnasnet_large_halfPrecision	.151
183	pnasnet_large	.150
184	aughost_fair_resnet18	.143
185	AdvProp_efficientnet-b7	.142
186	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.139
187	deit_small_imagenet_10_seed-0	.133
188	AdvProp_efficientnet-b8	.132
189	resnet_50_v1_spiking_l4	.130
190	mobilenet_v2_0_5_192	.129
191	inception_v3	.117
192	mobilenet_v2_0_5_224	.117
193	resnet50_imagenet_1_seed-0	.116
194	mobilenet_v2_0_75_160	.115
195	convnext_small_imagenet_10_seed-0	.114
196	nasnet_large	.113
197	inception_v4	.110
198	resnet50_primary_visual_cortex	.105
199	mobilenet_v2_1_0_128	.105
200	mobilenet_v2_0_75_192	.103
201	mobilenet_v2_0_75_224	.099
202	yudixie_resnet50_category_class_0_240908	.097
203	mobilenet_v2_1_0_160	.089
204	yudixie_resnet18_object_class_0_240719	.089
205	yudixie_resnet50_object_class_0_240908	.088
206	blt_vs	.086
207	mobilenet_v2_1_0_192	.085
208	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.083
209	convnext_small_imagenet_1_seed-0	.083
210	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.080
211	cv_18_dagger_408_pretrained	.077
212	compact_resnet50_IT	.072
213	yudixie_resnet18_category_class_0_240719	.071
214	yudixie_resnet18_distance_reg_0_240719	.070
215	mobilenet_v2_1_3_224	.070
216	BiT-S-R152x4	.059
217	yudixie_resnet18_distance_translation_rotation_0_240719	.056
218	yudixie_resnet18_distance_rotation_0_240719	.055
219	alexnet_training_seed_09	.053
220	compact_vgg19_IT	.053
221	ViT_L_32_imagenet1k	.052
222	alexnet_training_seed_08	.051
223	alexnet_training_seed_10	.051
224	alexnet_training_seed_05	.051
225	compact_resnet50_robust_IT	.049
226	alexnet_training_seed_01	.048
227	alexnet_training_seed_01_fov12	.048
228	alexnet_training_seed_03	.048
229	yudixie_resnet50_distance_rotation_0_240908	.047
230	alexnet_training_seed_02	.047
231	alexnet_training_seed_01_fov16	.046
232	yudixie_resnet18_distance_translation_0_240719	.046
233	alexnet_training_seed_04	.045
234	briaai_rmbg_1_4	.045
235	alexnet_training_seed_07	.045
236	alexnet_training_seed_01_fov4	.044
237	compact_cornets_V4	.042
238	alexnet_training_seed_06	.041
239	compact_cornets_IT	.041
240	yudixie_resnet50_distance_translation_rotation_0_240908	.040
241	yudixie_resnet50_distance_translation_0_240908	.040
242	yudixie_resnet50_distance_reg_0_240908	.035
243	yudixie_resnet18_rotation_reg_0_240719	.032
244	compact_resnet50_V4	.031
245	deit_small_imagenet_1_seed-0	.029
246	yudixie_resnet50_random_0_240908	.029
247	mobilevit_small	.029
248	yudixie_resnet18_translation_rotation_0_240719	.028
249	yolos_tiny	.025
250	yudixie_resnet50_translation_rotation_0_240908	.024
251	yudixie_resnet18_random_0_240719	.023
252	hmax	.022
253	compact_resnet50_robust_V4	.019
254	yudixie_resnet50_rotation_reg_0_240908	.013
255	resnet_50_v1_spiking_l3	.009
256	yudixie_resnet18_translation_reg_0_240719	.009
257	compact_vgg19_V4	.003
258	yudixie_resnet50_translation_reg_0_240908	.003
259	omnivore_swinB	.000
260	omnivore_swinS	.000
261	SimVP
262	antialiased-rnext101_32x8d
263	MIM
264	TAU
265	PredRNN
266	ConvLSTM

Benchmark bibtex

None

Ceiling

0.68.

Note that scores are relative to this ceiling.

Data: Hebart2023

Metric: match