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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024llh-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_base_patch14_reg4_dinov2:lvd142m	1.0
2	alexnet_training_seed_10	1.0
3	alexnet_training_seed_06	1.0
4	alexnet_training_seed_09	1.0
5	mobilenet_v2_1_0_128	1.0
6	resnet50-SIN_IN_IN	1.0
7	vit_large_patch14_clip_224:openai_ft_in1k	1.0
8	convnext_xxlarge:clip_laion2b_soup_ft_in1k	1.0
9	convnext_femto_ols:d1_in1k	1.0
10	deit_small_imagenet_10_seed-0	1.0
11	deit_base_imagenet_full_seed-0	1.0
12	efficientnet_b1_imagenet_full	1.0
13	resnet50_imagenet_100_seed-0	1.0
14	ViT_L_32_imagenet1k	1.0
15	alexnet_training_seed_01_fov16	1.0
16	alexnet_fov4	1.0
17	resnet_50_v1_spiking_l4a	1.0
18	xception	1.0
19	alexnet_training_seed_02	1.0
20	resnet50-moclr8deg	1.0
21	mobilenet_v2_1_0_160	1.0
22	resnet-152_v2_pytorch	1.0
23	vit_base_patch14_dinov2:lvd142m	1.0
24	yudixie_resnet50_object_class_0_240908	1.0
25	alexnet_training_seed_01	1.0
26	mobilenet_v2_1_0_192	1.0
27	efficientnet-b7	1.0
28	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
29	resnet50_tutorial	1.0
30	resnet152_imagenet_full	1.0
31	resnet34_imagenet_full	1.0
32	cvt_cvt-w24-384-in22k_finetuned-in1k_4	1.0
33	vit_base_patch16_dinov3:lvd1689m	1.0
34	compact_resnet50_robust_V4	1.0
35	resnet_50_v1_spiking	1.0
36	vgg_16	1.0
37	efficientnet_b0_imagenet_full	1.0
38	cvt_cvt-13-384-in1k_4_LucyV4	1.0
39	AdvProp_efficientnet-b8	.979
40	cvt_cvt-21-224-in1k_4_LucyV4	.979
41	vonegrcnn_52e_full	.951
42	AdvProp_efficientnet-b4	.951
43	antialiased-r50	.951
44	blt_vs	.951
45	yudixie_resnet18_category_class_0_240719	.951
46	resnet152_ecoset_full	.951
47	pnasnet_large_pytorch	.951
48	alexnet_training_seed_01_fov4	.888
49	mobilenet_v2_1_0_224	.888
50	mobilenet_v2_0_5_224	.888
51	BiT-S-R50x1	.888
52	BiT-S-R50x3	.888
53	AdvProp_efficientnet-b7	.888
54	yudixie_resnet18_distance_rotation_0_240719	.888
55	yudixie_resnet50_imagenet1kpret_0_240908	.862
56	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.862
57	AT_efficientnet-b2	.862
58	AdvProp_efficientnet-b2	.862
59	yudixie_resnet18_distance_translation_0_240719	.862
60	efficientnet_b2_imagenet_full	.862
61	focalnet_tiny_lrf_in1k	.862
62	alexnet_workflow_trigger	.805
63	resnet50_primary_visual_cortex	.805
64	mobilenet_v2_0_75_224	.805
65	inception_v1	.805
66	yudixie_resnet18_object_class_0_240719	.805
67	deit_small_imagenet_100_seed-0	.805
68	resnet18_ecoset_full	.805
69	alexnet	.805
70	resnext101_32x32d_wsl	.805
71	ReAlnet01_cornet_fov4	.782
72	resnet50_robust_l2_eps1	.782
73	resnet50-vicregl0p9	.782
74	BiT-S-R152x4	.782
75	artResNet18_1	.782
76	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.782
77	resnet18_imagenet_full	.782
78	ReAlnet01_cornet_fov16	.730
79	mobilenet_v2_0_75_192	.730
80	inception_v3	.730
81	omnivore_swinB	.730
82	yudixie_resnet18_rotation_reg_0_240719	.730
83	resnet50_imagenet_1_seed-0	.730
84	resnet50-vitoimagevidnet8	.709
85	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.709
86	omnivore_swinS	.709
87	efficientnet_b0	.709
88	resnet50-VITO-8deg-cc	.709
89	BiT-S-R101x1	.709
90	convnext_tiny_sup	.709
91	yudixie_resnet18_translation_rotation_0_240719	.709
92	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.709
93	resnext101_32x48d_wsl	.709
94	mobilenet_v2_1_3_224	.662
95	resnet_SIN_IN_FT_IN	.662
96	alexnet_training_seed_01_fov12	.643
97	alexnet_fov16	.643
98	ReAlnet01_ALL	.643
99	resnet_101_v1	.643
100	mobilenet_v2_0_5_192	.643
101	convnext_xlarge:fb_in22k_ft_in1k	.643
102	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.643
103	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.643
104	yudixie_resnet18_random_0_240719	.643
105	AlexNet_SIN_fov12	.601
106	compact_vgg19_IT	.601
107	alexnet_training_seed_05	.601
108	BiT-S-R152x2	.601
109	AlexNet_SIN_fov4	.583
110	alexnet_training_seed_08	.583
111	resnet_152_v1	.583
112	inception_v4	.583
113	yudixie_resnet50_translation_reg_0_240908	.583
114	shufflenet_v2_x1_0	.583
115	VOneCORnet-S	.583
116	densenet-169	.583
117	BiT-S-R101x3	.583
118	AdvProp_efficientnet-b6	.545
119	ReAlnet01_cornet_fov12	.529
120	AlexNet_SIN	.529
121	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.529
122	yudixie_resnet50_distance_reg_0_240908	.529
123	mobilenet_v2_1_4_224	.529
124	mobilenet_v2_0_75_160	.529
125	vonegrcnn_62e_nobn	.529
126	resnet50-vicreg	.529
127	resnet-18_test_m	.529
128	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.529
129	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.529
130	resnet50-vicregl0p75	.529
131	vonegrcnn_47e	.529
132	yudixie_resnet18_distance_translation_rotation_0_240719	.529
133	mobilenet_v2_1-4_224_pytorch	.529
134	alexnet_training_seed_04	.494
135	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.494
136	resnet_50_v1	.480
137	densenet-201	.480
138	resnet50-sup	.480
139	yudixie_resnet50_translation_rotation_0_240908	.448
140	yudixie_resnet18_distance_reg_0_240719	.448
141	resnet50_ewconplus_wzc	.435
142	compact_resnet50_IT	.435
143	ReAlnet02_ALL	.435
144	yudixie_resnet50_category_class_0_240908	.435
145	nasnet_mobile	.435
146	grcnn	.435
147	cvt_cvt-21-384-in1k_4_LucyV4	.435
148	resnet50_ecoset_full	.435
149	resnet_152_v2	.406
150	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.406
151	convnext_small_imagenet_10_seed-0	.406
152	alexnet_fov12	.395
153	compact_cornets_V4	.395
154	compact_resnet50_V4	.395
155	ReAlnet04_ALL	.395
156	yudixie_resnet50_distance_rotation_0_240908	.395
157	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.395
158	resnet_50_v1_spiking_l4	.369
159	alexnet_training_seed_07	.369
160	resnet50_imagenet_10_seed-0	.369
161	custom_model_cv_18_dagger_408	.369
162	yudixie_resnet50_random_0_240908	.358
163	yudixie_resnet50_rotation_reg_0_240908	.358
164	resnet50-SIN	.358
165	effnetb1_272x240	.358
166	compact_resnet50_robust_IT	.334
167	briaai_rmbg_1_4	.334
168	grcnn_109	.325
169	vit_base_patch16_clip_224:openai_ft_in1k	.325
170	imagenet_l2_3_0	.325
171	resnet50_robust_l2_eps3	.325
172	deit_small_imagenet_full_seed-0	.325
173	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.303
174	Res2Net50_26w_4s	.294
175	densenet-121	.294
176	yudixie_resnet18_translation_reg_0_240719	.294
177	convnext_small_imagenet_full_seed-0	.294
178	deit_large_imagenet_full_seed-0	.275
179	AlexNet_SIN_fov16	.267
180	yolos_tiny	.267
181	resnet_50_v2	.267
182	convnext_tiny:in12k_ft_in1k	.267
183	convnext_base_imagenet_full_seed-0	.267
184	ReAlnet10_ALL	.242
185	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.242
186	pnasnet_large	.242
187	ReAlnet06_cornet	.242
188	ReAlnet02_cornet	.242
189	resnet_101_v2	.242
190	yudixie_resnet50_distance_translation_0_240908	.242
191	ReAlnet06	.242
192	ReAlnet02	.242
193	resnet50-barlow	.242
194	deit_small_imagenet_1_seed-0	.242
195	resnet50_imagenet_full	.242
196	alexnet_training_seed_03	.226
197	ReAlnet06_ALL	.220
198	resnet_50_v1_spiking_l3	.205
199	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.199
200	resnet101_imagenet_full	.199
201	voneresnet-50-non_stochastic	.199
202	ReAlnet09_ALL	.181
203	ReAlnet07_ALL	.181
204	convnext_large_imagenet_full_seed-0	.181
205	yudixie_resnet50_distance_translation_rotation_0_240908	.164
206	ReAlnet09_cornet	.164
207	nasnet_large	.164
208	ReAlnet09	.164
209	voneresnet-50	.164
210	resnet50-SIN_IN	.164
211	cvt_cvt-13-224-in1k_4_LucyV4	.164
212	resnet-50-robust	.164
213	resnet101_ecoset_full	.149
214	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.149
215	compact_cornets_IT	.135
216	ReAlnet05_cornet	.135
217	ReAlnet05	.135
218	vit_large_patch14_clip_336:laion2b_ft_in1k	.135
219	convnext_small_imagenet_1_seed-0	.126
220	vgg_19	.122
221	vit_large_patch14_clip_224:laion2b_ft_in1k	.122
222	voneresnet-50-robust	.111
223	convnext_small_imagenet_100_seed-0	.111
224	yudixie_resnet18_imagenet1kpret_0_240719	.111
225	compact_vgg19_V4	.101
226	ReAlnet08_ALL	.101
227	ReAlnet05_ALL	.101
228	ReAlnet01_cornet	.083
229	ReAlnet01	.083
230	ReAlnet03_ALL	.075
231	cv_18_dagger_408_pretrained	.068
232	convnext_base:clip_laiona_augreg_ft_in1k_384	.068
233	convnext_large:fb_in22k_ft_in1k	.068
234	resnext101_32x8d_wsl	.068
235	ReAlnet03_cornet	.062
236	ReAlnet03	.062
237	ReAlnet10_cornet	.051
238	ReAlnet10	.051
239	ReAlnet08_cornet	.046
240	ReAlnet08	.046
241	resnet34_ecoset_full	.046
242	ReAlnet07_cornet	.042
243	ReAlnet07	.042
244	convnext_tiny_imagenet_full_seed-0	.034
245	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.031
246	ReAlnet04_cornet	.016
247	ReAlnet04	.016
248	hmax

Benchmark bibtex

        @misc{ferguson_ngo_lee_dicarlo_schrimpf_2024,
         title={How Well is Visual Search Asymmetry predicted by a Binary-Choice, Rapid, Accuracy-based Visual-search, Oddball-detection (BRAVO) task?},
         url={osf.io/5ba3n},
         DOI={10.17605/OSF.IO/5BA3N},
         publisher={OSF},
         author={Ferguson, Michael E, Jr and Ngo, Jerry and Lee, Michael and DiCarlo, James and Schrimpf, Martin},
         year={2024},
         month={Jun}
}

Ceiling

0.81.

Note that scores are relative to this ceiling.

Data: Ferguson2024llh

Metric: value_delta