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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	dinov2_vitl14_reg_lc	.538
3	dinov2_vitl14_lc	.535
4	dinov2_vitg14_lc	.534
5	resnet50-moclr8deg	.534
6	dinov2_vitl14_lc_res336	.528
7	dinov2_vitl14_reg_lc_featread	.525
8	dinov2_vitg14_reg_lc	.519
9	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.504
10	resnet50_robust_l2_eps1	.503
11	vonegrcnn_47e	.498
12	vonegrcnn_62e_nobn	.494
13	vonegrcnn_52e_full	.490
14	resnet_SIN_IN_FT_IN	.489
15	resnet50_tutorial	.488
16	resnet50-vitoimagevidnet8	.483
17	resnet50-VITO-8deg-cc	.483
18	densenet_201_pytorch	.476
19	resnet50_robust_l2_eps3	.476
20	imagenet_l2_3_0	.476
21	resnet18_imagenet21kP	.470
22	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.469
23	antialias-resnet152	.464
24	vit_base_patch14_reg4_dinov2-lvd142m	.463
25	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.463
26	vit_base_patch14_dinov2-lvd142m	.452
27	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.416
28	resnet_50_v2	.408
29	vgg_16	.407
30	resnext101_32x8d_wsl	.407
31	vgg_19	.404
32	efficientnet_b0_imagenet_full	.399
33	efficientnet_b1_imagenet_full	.396
34	efficientnet_b2_imagenet_full	.396
35	vit_base_patch16_dinov3-lvd1689m	.395
36	convnext_base_fb_in1k_gram_aligned_dinov3	.391
37	resnext101_32x16d_wsl	.387
38	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.381
39	shufflenet_v2_x1_0	.379
40	resnet-152_v2_pytorch	.375
41	alexnet_ks_torevert	.375
42	alexnet	.375
43	voneresnet-50-robust	.372
44	custom_model_cv_18_dagger_408	.371
45	voneresnet-50-non_stochastic	.368
46	ReAlnet09_cornet	.364
47	ReAlnet09	.364
48	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.363
49	grcnn	.361
50	deepeisnn_ei_imagenet_resnet18	.361
51	resnet18_imagenet_full	.358
52	resnet34_imagenet_full	.358
53	ReAlnet04_cornet	.358
54	ReAlnet04	.358
55	ReAlnet01_cornet_fov12	.357
56	voneresnet-50	.357
57	grcnn_109	.356
58	resnet50_hvs_wzc	.356
59	Res2Net50_26w_4s	.354
60	ReAlnet05_cornet	.354
61	ReAlnet05	.354
62	resnet50_ewconplus_wzc_seed2_2__323	.353
63	resnet50-vicregl0p9	.352
64	resnet-50-robust	.352
65	pnasnet_large_pytorch	.351
66	ReAlnet06_cornet	.350
67	ReAlnet06	.350
68	ReAlnet03_cornet	.349
69	ReAlnet03	.349
70	ReAlnet07_cornet	.347
71	ReAlnet07	.347
72	ReAlnet01_cornet	.347
73	ReAlnet01	.347
74	resnet50_ewconplus_wzc_seed5_2__323	.346
75	ReAlnet01_cornet_fov16	.346
76	ReAlnet02_cornet	.346
77	ReAlnet02	.346
78	resnet50_ewconplus_wzc_seed6_2_323	.345
79	resnet50-vicregl0p75	.345
80	convnext_tiny_sup	.344
81	efficientnet_b0	.342
82	ReAlnet08_cornet	.342
83	ReAlnet08	.342
84	ReAlnet10_ALL	.341
85	ReAlnet10_cornet	.338
86	ReAlnet10	.338
87	alexnet_fov4	.336
88	resnet18_ecoset_full	.336
89	convnext_large:fb_in22k_ft_in1k	.336
90	ReAlnet08_ALL	.335
91	ReAlnet07_ALL	.333
92	resnet_101_v2	.333
93	resnet50_ewconplus_wzc	.332
94	ReAlnet03_ALL	.332
95	mobilenet_v2_1_0_224	.330
96	mobilenet_v2_1_4_224	.330
97	mobilenet_v2_1-4_224_pytorch	.330
98	resnet50-SIN_IN	.329
99	ReAlnet05_ALL	.329
100	ReAlnet02_ALL	.328
101	ReAlnet04_ALL	.327
102	ReAlnet01_ALL	.327
103	ReAlnet06_ALL	.326
104	resnet_152_v2	.326
105	focalnet_tiny_lrf_in1k	.326
106	resnext101_32x32d_wsl	.325
107	regnet_y_400mf	.322
108	resnet50_st_wzc_seed7_2_323	.321
109	ReAlnet09_ALL	.320
110	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.320
111	resnet50_st_wzc_2	.319
112	convnext_xlarge:fb_in22k_ft_in1k	.319
113	alexnet_fov12	.319
114	resnet50_imagenet_full	.318
115	resnet34_ecoset_full	.317
116	resnet101_imagenet_full	.316
117	convnext_base_fb_in22k_ft_in1k	.315
118	resnet50-barlow	.313
119	resnet50_st_wzc_seed3_2_323	.312
120	resnet50-vicreg	.311
121	resnet152_imagenet_full	.308
122	resnet_152_v1	.307
123	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.304
124	resnet_101_v1	.297
125	VOneCORnet-S	.296
126	densenet-121	.291
127	inception_v3_pytorch	.291
128	antialiased-r50	.290
129	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.289
130	alexnet_fov16	.289
131	resnet_50_v1	.288
132	resnet50-sup	.288
133	predictive_coding_pc	.288
134	densenet-201	.286
135	BiT-S-R50x1	.286
136	pr1_stl10_disentangled_alpha04	.284
137	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.281
138	AlexNet_SIN	.281
139	resnet50_imagenet_100_seed-0	.279
140	resnext101_32x48d_wsl	.278
141	resnet-18_test_m	.273
142	resnet50-SIN_IN_IN	.272
143	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.271
144	yudixie_resnet50_imagenet1kpret_0_240908	.271
145	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.270
146	yudixie_wbh_object_class_scaling_0415_imagenet	.270
147	ReAlnet01_cornet_fov4	.268
148	resnet50_ecoset_full	.266
149	convnext_tiny:in12k_ft_in1k	.265
150	AT_efficientnet-b2	.265
151	AdvProp_efficientnet-b2	.265
152	deit_large_imagenet_full_seed-0	.263
153	vit_huge_clip336_laion	.260
154	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.260
155	yudixie_resnet18_imagenet1kpret_0_240719	.258
156	convnext_base:clip_laiona_augreg_ft_in1k_384	.257
157	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.256
158	BiT-S-R101x1	.255
159	resnet50-SIN	.254
160	BiT-S-R50x3	.254
161	BiT-S-R152x2	.254
162	vonealexnet	.253
163	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.253
164	resnet101_ecoset_full	.253
165	densenet-169	.250
166	deit_small_imagenet_100_seed-0	.247
167	BiT-S-R101x3	.244
168	effnetb1_272x240	.242
169	convnext_femto_ols:d1_in1k	.242
170	convnext_base_fb_in1k	.241
171	cvt_cvt-21-224-in1k_4_LucyV4	.238
172	resnet152_ecoset_full	.238
173	convnext_small_imagenet_full_seed-0	.238
174	deit_base_imagenet_full_seed-0	.238
175	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.237
176	resnet50_eMMCR_Vanilla	.233
177	AlexNet_SIN_fov12	.232
178	AdvProp_efficientnet-b4	.228
179	deit3_base_patch16_224_fb_in1k	.227
180	inception_v1	.224
181	AlexNet_SIN_fov4	.221
182	convnext_tiny_imagenet_full_seed-0	.220
183	cvt_cvt-21-384-in1k_4_LucyV4	.219
184	eva02_large_in22k	.218
185	cvt_cvt-13-224-in1k_4_LucyV4	.217
186	AlexNet_SIN_fov16	.215
187	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.213
188	resnet_50_v1_spiking	.210
189	convnext_base_imagenet_full_seed-0	.209
190	deit_small_imagenet_full_seed-0	.207
191	resnet50_imagenet_10_seed-0	.206
192	vit_relpos_base_patch16_clsgap_224:sw_in1k	.203
193	vit_large_patch14_clip_224:laion2b_ft_in1k	.202
194	vit_large_patch14_clip_336:laion2b_ft_in1k	.200
195	vit_base_patch16_clip_224:openai_ft_in1k	.199
196	cvt_cvt-13-384-in1k_4_LucyV4	.198
197	convnext_small_imagenet_100_seed-0	.191
198	nasnet_mobile	.191
199	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.188
200	AdvProp_efficientnet-b6	.185
201	efficientnet-b7	.176
202	xception	.175
203	artResNet18_1	.173
204	tv_efficientnet-b1	.169
205	convnext_large_imagenet_full_seed-0	.166
206	vit_large_patch14_clip_224:openai_ft_in1k	.157
207	resnet_50_v1_spiking_l4a	.155
208	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.153
209	pnasnet_large_halfPrecision	.151
210	pnasnet_large	.150
211	deepeisnn_ei_cifar10_resnet18	.144
212	AdvProp_efficientnet-b7	.142
213	convnext_large_clip_laion2b_finelayers	.141
214	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.139
215	persistence_memory	.139
216	persistence_mem	.138
217	deit_small_imagenet_10_seed-0	.133
218	AdvProp_efficientnet-b8	.132
219	resnet_50_v1_spiking_l4	.130
220	resnet18_fair	.129
221	mobilenet_v2_0_5_192	.129
222	inception_v3	.117
223	mobilenet_v2_0_5_224	.117
224	resnet50_imagenet_1_seed-0	.116
225	mobilenet_v2_0_75_160	.115
226	convnext_small_imagenet_10_seed-0	.114
227	convnext_large_in22k_supervised	.114
228	nasnet_large	.113
229	deepeisnn_ei_cifar10_vgg19	.113
230	inception_v4	.110
231	resnet50_primary_visual_cortex	.105
232	mobilenet_v2_1_0_128	.105
233	mobilenet_v2_0_75_192	.103
234	mobilenet_v2_0_75_224	.099
235	yudixie_resnet50_category_class_0_240908	.097
236	mobilenet_v2_1_0_160	.089
237	yudixie_resnet18_object_class_0_240719	.089
238	yudixie_resnet50_object_class_0_240908	.088
239	blt_vs	.086
240	mobilenet_v2_1_0_192	.085
241	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.083
242	convnext_small_imagenet_1_seed-0	.083
243	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.080
244	cv_18_dagger_408_pretrained	.077
245	compact_resnet50_IT	.072
246	yudixie_resnet18_category_class_0_240719	.071
247	yudixie_resnet18_distance_reg_0_240719	.070
248	mobilenet_v2_1_3_224	.070
249	BiT-S-R152x4	.059
250	yudixie_resnet18_distance_translation_rotation_0_240719	.056
251	yudixie_resnet18_distance_rotation_0_240719	.055
252	alexnet_training_seed_09	.053
253	compact_vgg19_IT	.053
254	ViT_L_32_imagenet1k	.052
255	alexnet_training_seed_08	.051
256	alexnet_training_seed_10	.051
257	alexnet_training_seed_05	.051
258	compact_resnet50_robust_IT	.049
259	alexnet_training_seed_01	.048
260	alexnet_training_seed_01_fov12	.048
261	alexnet_training_seed_03	.048
262	yudixie_resnet50_distance_rotation_0_240908	.047
263	alexnet_training_seed_02	.047
264	alexnet_training_seed_01_fov16	.046
265	yudixie_resnet18_distance_translation_0_240719	.046
266	alexnet_training_seed_04	.045
267	briaai_rmbg_1_4	.045
268	alexnet_training_seed_07	.045
269	alexnet_training_seed_01_fov4	.044
270	compact_cornets_V4	.042
271	alexnet_training_seed_06	.041
272	compact_cornets_IT	.041
273	yudixie_resnet50_distance_translation_rotation_0_240908	.040
274	yudixie_resnet50_distance_translation_0_240908	.040
275	yudixie_resnet50_distance_reg_0_240908	.035
276	yudixie_resnet18_rotation_reg_0_240719	.032
277	compact_resnet50_V4	.031
278	deit_small_imagenet_1_seed-0	.029
279	yudixie_resnet50_random_0_240908	.029
280	mobilevit_small	.029
281	yudixie_resnet18_translation_rotation_0_240719	.028
282	yolos_tiny	.025
283	yudixie_resnet50_translation_rotation_0_240908	.024
284	yudixie_resnet18_random_0_240719	.023
285	hmax	.022
286	yudixie_wbh_object_class_scaling_0415_run_3	.022
287	compact_resnet50_robust_V4	.019
288	yudixie_wbh_object_class_scaling_0415_run_0	.017
289	yudixie_wbh_object_class_scaling_0415_run_2	.015
290	yudixie_resnet50_rotation_reg_0_240908	.013
291	resnet_50_v1_spiking_l3	.009
292	yudixie_resnet18_translation_reg_0_240719	.009
293	compact_vgg19_V4	.003
294	yudixie_resnet50_translation_reg_0_240908	.003
295	omnivore_swinB	.000
296	omnivore_swinS	.000
297	convnext_xxlarge_clip
298	vit_large_clip336_openai
299	SimVP
300	antialiased-rnext101_32x8d
301	MIM
302	TAU
303	PredRNN
304	ConvLSTM

Benchmark bibtex

None

Ceiling

0.68.

Note that scores are relative to this ceiling.

Data: Hebart2023

Metric: match