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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024half-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	cvt_cvt-13-384-in1k_4_LucyV4	1.0
2	swin_small_patch4_window7_224:ms_in22k_ft_in1k	1.0
3	efficientnet_b0_imagenet_full	1.0
4	resnext101_32x32d_wsl	1.0
5	mobilenet_v2_0_75_192	1.0
6	compact_resnet50_V4	1.0
7	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
8	yudixie_resnet18_random_0_240719	1.0
9	resnet_152_v2	1.0
10	ReAlnet05	1.0
11	yudixie_resnet50_random_0_240908	1.0
12	yudixie_resnet50_imagenet1kpret_0_240908	1.0
13	alexnet_training_seed_05	1.0
14	resnet18_ecoset_full	1.0
15	yudixie_resnet50_distance_reg_0_240908	1.0
16	mobilenet_v2_0_5_224	1.0
17	resnet18_imagenet_full	1.0
18	alexnet_training_seed_07	1.0
19	resnet50_imagenet_1_seed-0	1.0
20	ReAlnet05_cornet	1.0
21	yudixie_resnet18_rotation_reg_0_240719	1.0
22	artResNet18_1	1.0
23	convnext_tiny_sup	1.0
24	resnet50_tutorial	.975
25	convnext_base_imagenet_full_seed-0	.975
26	inception_v4	.975
27	resnext101_32x8d_wsl	.975
28	effnetb1_272x240	.975
29	resnet-152_v2_pytorch	.975
30	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.942
31	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.942
32	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.942
33	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.942
34	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.942
35	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.942
36	nasnet_mobile	.942
37	vonegrcnn_47e	.942
38	vonegrcnn_52e_full	.942
39	convnext_small_imagenet_1_seed-0	.882
40	convnext_small_imagenet_10_seed-0	.882
41	resnet34_imagenet_full	.852
42	resnet_50_v1_spiking_l4a	.852
43	alexnet_training_seed_04	.852
44	resnet152_ecoset_full	.852
45	resnet50-barlow	.852
46	BiT-S-R152x4	.852
47	voneresnet-50-non_stochastic	.798
48	focalnet_tiny_lrf_in1k	.798
49	efficientnet-b7	.798
50	resnet_50_v1_spiking_l3	.798
51	AdvProp_efficientnet-b8	.798
52	convnext_xlarge:fb_in22k_ft_in1k	.771
53	vit_large_patch14_clip_224:openai_ft_in1k	.771
54	vit_large_patch14_clip_336:laion2b_ft_in1k	.771
55	resnext101_32x48d_wsl	.771
56	ViT_L_32_imagenet1k	.771
57	yudixie_resnet18_category_class_0_240719	.771
58	efficientnet_b0	.771
59	grcnn	.771
60	voneresnet-50-robust	.771
61	AdvProp_efficientnet-b4	.771
62	AdvProp_efficientnet-b2	.771
63	AT_efficientnet-b2	.771
64	vit_base_patch16_clip_224:openai_ft_in1k	.771
65	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.722
66	yudixie_resnet50_category_class_0_240908	.722
67	mobilenet_v2_0_5_192	.722
68	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.722
69	yudixie_resnet18_translation_reg_0_240719	.722
70	AdvProp_efficientnet-b7	.722
71	ReAlnet07	.698
72	vit_large_patch14_clip_224:laion2b_ft_in1k	.698
73	Res2Net50_26w_4s	.698
74	deit_small_imagenet_1_seed-0	.698
75	ReAlnet07_cornet	.698
76	vgg_16	.698
77	omnivore_swinB	.698
78	BiT-S-R101x3	.698
79	densenet-121	.698
80	mobilenet_v2_1_0_160	.653
81	compact_resnet50_IT	.653
82	compact_cornets_V4	.653
83	yudixie_resnet50_object_class_0_240908	.631
84	convnext_large:fb_in22k_ft_in1k	.631
85	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.631
86	convnext_tiny_imagenet_full_seed-0	.631
87	yudixie_resnet18_translation_rotation_0_240719	.631
88	yudixie_resnet18_distance_translation_rotation_0_240719	.631
89	compact_vgg19_IT	.631
90	yudixie_resnet18_imagenet1kpret_0_240719	.631
91	alexnet_training_seed_09	.591
92	mobilenet_v2_0_75_160	.591
93	briaai_rmbg_1_4	.591
94	alexnet_training_seed_10	.591
95	resnet50-SIN_IN	.591
96	compact_resnet50_robust_IT	.591
97	inception_v1	.591
98	resnet_152_v1	.571
99	resnet_50_v1_spiking	.571
100	convnext_tiny:in12k_ft_in1k	.571
101	mobilenet_v2_1_0_192	.571
102	pnasnet_large_pytorch	.571
103	ReAlnet02_cornet	.571
104	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.571
105	yudixie_resnet18_distance_rotation_0_240719	.571
106	resnet50_robust_l2_eps1	.571
107	vonegrcnn_62e_nobn	.571
108	BiT-S-R50x3	.571
109	ReAlnet02	.571
110	BiT-S-R50x1	.571
111	VOneCORnet-S	.535
112	mobilenet_v2_1_3_224	.535
113	resnet50-vicregl0p9	.535
114	cvt_cvt-21-384-in1k_4_LucyV4	.517
115	alexnet_training_seed_06	.517
116	vit_relpos_base_patch16_clsgap_224:sw_in1k	.517
117	resnet50_ecoset_full	.517
118	shufflenet_v2_x1_0	.517
119	resnet50-vicreg	.517
120	vgg_19	.517
121	voneresnet-50	.517
122	hmax	.517
123	resnet50_imagenet_full	.484
124	yudixie_resnet18_distance_translation_0_240719	.484
125	nasnet_large	.484
126	compact_cornets_IT	.484
127	alexnet_training_seed_03	.467
128	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.467
129	deit_small_imagenet_10_seed-0	.467
130	deit_large_imagenet_full_seed-0	.467
131	resnet50_imagenet_100_seed-0	.467
132	compact_resnet50_robust_V4	.438
133	resnet_101_v2	.438
134	deit_base_imagenet_full_seed-0	.423
135	densenet-201	.423
136	mobilenet_v2_1-4_224_pytorch	.423
137	yolos_tiny	.423
138	mobilenet_v2_1_4_224	.423
139	grcnn_109	.423
140	antialiased-r50	.423
141	resnet_SIN_IN_FT_IN	.423
142	alexnet_training_seed_01	.396
143	inception_v3	.383
144	ReAlnet08	.383
145	cvt_cvt-13-224-in1k_4_LucyV4	.383
146	convnext_large_imagenet_full_seed-0	.383
147	resnet-50-robust	.383
148	mobilenet_v2_1_0_224	.383
149	ReAlnet08_cornet	.383
150	omnivore_swinS	.383
151	convnext_base:clip_laiona_augreg_ft_in1k_384	.383
152	mobilenet_v2_0_75_224	.358
153	resnet50-vicregl0p75	.358
154	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.346
155	efficientnet_b2_imagenet_full	.346
156	cvt_cvt-21-224-in1k_4_LucyV4	.346
157	convnext_femto_ols:d1_in1k	.346
158	custom_model_cv_18_dagger_408	.324
159	resnet50-SIN	.324
160	resnet_50_v1	.324
161	resnet50-sup	.324
162	densenet-169	.324
163	yudixie_resnet50_distance_translation_rotation_0_240908	.313
164	convnext_small_imagenet_full_seed-0	.313
165	convnext_small_imagenet_100_seed-0	.313
166	resnet50-SIN_IN_IN	.313
167	ReAlnet07_ALL	.313
168	BiT-S-R101x1	.313
169	compact_vgg19_V4	.293
170	ReAlnet09	.283
171	resnet101_ecoset_full	.283
172	mobilenet_v2_1_0_128	.283
173	blt_vs	.283
174	resnet-18_test_m	.283
175	yudixie_resnet18_object_class_0_240719	.283
176	yudixie_resnet50_rotation_reg_0_240908	.283
177	resnet50-vitoimagevidnet8	.283
178	ReAlnet09_cornet	.283
179	resnet50-VITO-8deg-cc	.283
180	ReAlnet01_cornet	.256
181	yudixie_resnet50_distance_translation_0_240908	.256
182	ReAlnet01	.256
183	BiT-S-R152x2	.240
184	yudixie_resnet50_distance_rotation_0_240908	.232
185	ReAlnet05_ALL	.232
186	ReAlnet08_ALL	.232
187	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.232
188	resnet_50_v1_spiking_l4	.217
189	resnet101_imagenet_full	.217
190	AdvProp_efficientnet-b6	.210
191	resnet152_imagenet_full	.196
192	ReAlnet10_ALL	.190
193	resnet_50_v2	.172
194	ReAlnet06	.172
195	ReAlnet10	.172
196	resnet34_ecoset_full	.172
197	yudixie_resnet50_translation_reg_0_240908	.172
198	ReAlnet06_cornet	.172
199	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.172
200	ReAlnet10_cornet	.172
201	ReAlnet03	.155
202	ReAlnet03_cornet	.155
203	resnet50_imagenet_10_seed-0	.155
204	yudixie_resnet18_distance_reg_0_240719	.155
205	ReAlnet02_ALL	.155
206	resnet_101_v1	.155
207	ReAlnet09_ALL	.155
208	alexnet_training_seed_08	.145
209	deit_small_imagenet_100_seed-0	.141
210	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.141
211	resnet50_ewconplus_wzc	.132
212	ReAlnet04_cornet	.127
213	ReAlnet04	.127
214	efficientnet_b1_imagenet_full	.115
215	xception	.115
216	ReAlnet04_ALL	.115
217	resnet50_robust_l2_eps3	.115
218	imagenet_l2_3_0	.115
219	yudixie_resnet50_translation_rotation_0_240908	.108
220	alexnet_training_seed_02	.104
221	resnet50-moclr8deg	.104
222	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.094
223	ReAlnet06_ALL	.094
224	deit_small_imagenet_full_seed-0	.077
225	ReAlnet01_ALL	.077
226	ReAlnet03_ALL	.077
227	resnet50_primary_visual_cortex	.072
228	pnasnet_large

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

0.90.

Note that scores are relative to this ceiling.

Data: Ferguson2024half

Metric: value_delta