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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024circle_line-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	shufflenet_v2_x1_0	1.0
2	yudixie_resnet18_distance_translation_0_240719	1.0
3	resnet101_imagenet_full	1.0
4	effnetb1_272x240	1.0
5	yudixie_resnet50_translation_rotation_0_240908	1.0
6	BiT-S-R101x3	1.0
7	deit_small_imagenet_10_seed-0	1.0
8	vit_base_patch14_dinov2-lvd142m	1.0
9	alexnet_training_seed_09	1.0
10	nasnet_large	1.0
11	efficientnet-b7	1.0
12	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	1.0
13	BiT-S-R50x1	1.0
14	convnext_tiny_imagenet_full_seed-0	1.0
15	voneresnet-50-non_stochastic	1.0
16	cvt_cvt-21-224-in1k_4_LucyV4	.935
17	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.935
18	mobilenet_v2_0_75_192	.931
19	vit_base_patch16_clip_224:openai_ft_in1k	.931
20	convnext_tiny_sup	.931
21	resnext101_32x8d_wsl	.931
22	BiT-S-R152x2	.848
23	convnext_small_imagenet_full_seed-0	.848
24	yudixie_resnet50_random_0_240908	.845
25	grcnn_109	.845
26	vonegrcnn_52e_full	.845
27	yudixie_resnet18_translation_reg_0_240719	.845
28	yudixie_resnet18_distance_translation_rotation_0_240719	.845
29	pnasnet_large_pytorch	.845
30	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.770
31	BiT-S-R50x3	.767
32	convnext_small_imagenet_1_seed-0	.767
33	convnext_large_imagenet_full_seed-0	.767
34	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.699
35	mobilenet_v2_1_4_224	.696
36	AdvProp_efficientnet-b4	.696
37	deit_small_imagenet_100_seed-0	.696
38	deit_base_imagenet_full_seed-0	.696
39	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.696
40	mobilenet_v2_1-4_224_pytorch	.696
41	AdvProp_efficientnet-b8	.634
42	alexnet_training_seed_02	.632
43	resnet_101_v2	.632
44	mobilenet_v2_0_75_160	.632
45	convnext_femto_ols:d1_in1k	.632
46	alexnet_training_seed_01_fov12	.632
47	cvt_cvt-13-224-in1k_4_LucyV4	.632
48	alexnet_fov16	.575
49	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.575
50	resnext101_32x48d_wsl	.573
51	yudixie_resnet50_distance_reg_0_240908	.573
52	alexnet_training_seed_06	.573
53	mobilenet_v2_0_75_224	.573
54	vit_relpos_base_patch16_clsgap_224:sw_in1k	.573
55	vit_large_patch14_clip_336:laion2b_ft_in1k	.573
56	vit_large_patch14_clip_224:laion2b_ft_in1k	.573
57	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.573
58	convnext_base:clip_laiona_augreg_ft_in1k_384	.573
59	vit_base_patch14_reg4_dinov2-lvd142m	.573
60	AlexNet_SIN_fov12	.573
61	vonegrcnn_62e_nobn	.522
62	omnivore_swinB	.522
63	convnext_base_imagenet_full_seed-0	.522
64	resnet152_ecoset_full	.522
65	cv_18_dagger_408_pretrained	.522
66	resnet50_primary_visual_cortex	.520
67	yolos_tiny	.520
68	vgg_19	.520
69	Res2Net50_26w_4s	.520
70	convnext_large:fb_in22k_ft_in1k	.520
71	densenet-121	.520
72	resnet50-vicregl0p9	.520
73	AdvProp_efficientnet-b6	.520
74	yudixie_resnet18_rotation_reg_0_240719	.520
75	resnet101_ecoset_full	.520
76	cvt_cvt-13-384-in1k_4_LucyV4	.520
77	xception	.472
78	mobilenet_v2_1_0_128	.472
79	antialiased-r50	.472
80	ViT_L_32_imagenet1k	.472
81	resnet-50-robust	.472
82	resnet_50_v1	.430
83	resnet50-sup	.430
84	ReAlnet10_ALL	.428
85	resnet_50_v2	.428
86	inception_v4	.428
87	mobilenet_v2_0_5_224	.428
88	voneresnet-50	.428
89	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.428
90	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.428
91	convnext_tiny:in12k_ft_in1k	.428
92	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.428
93	BiT-S-R101x1	.428
94	resnext101_32x32d_wsl	.390
95	ReAlnet02_ALL	.389
96	resnet_50_v1_spiking_l3	.389
97	briaai_rmbg_1_4	.389
98	yudixie_resnet50_imagenet1kpret_0_240908	.389
99	mobilenet_v2_1_0_224	.389
100	yudixie_resnet50_translation_reg_0_240908	.389
101	convnext_xlarge:fb_in22k_ft_in1k	.389
102	yudixie_resnet18_object_class_0_240719	.389
103	vit_base_patch16_dinov3-lvd1689m	.389
104	resnet50_ecoset_full	.389
105	compact_resnet50_robust_V4	.353
106	ReAlnet01_ALL	.353
107	resnet_152_v1	.353
108	voneresnet-50-robust	.353
109	resnet50-moclr8deg	.353
110	resnet50-vicreg	.353
111	resnet50-SIN_IN_IN	.353
112	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.353
113	resnet50-vicregl0p75	.353
114	BiT-S-R152x4	.353
115	convnext_small_imagenet_10_seed-0	.353
116	efficientnet_b2_imagenet_full	.353
117	alexnet_training_seed_01_fov16	.353
118	AT_efficientnet-b2	.322
119	AdvProp_efficientnet-b2	.322
120	yudixie_resnet50_category_class_0_240908	.320
121	alexnet_training_seed_01	.320
122	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.320
123	vit_large_patch14_clip_224:openai_ft_in1k	.320
124	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.320
125	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.320
126	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.320
127	yudixie_resnet18_imagenet1kpret_0_240719	.320
128	resnet34_ecoset_full	.320
129	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.320
130	compact_resnet50_robust_IT	.291
131	compact_cornets_V4	.291
132	compact_vgg19_V4	.291
133	ReAlnet06_cornet	.291
134	resnet_101_v1	.291
135	nasnet_mobile	.291
136	inception_v3	.291
137	ReAlnet06	.291
138	resnet50-SIN	.291
139	yudixie_resnet18_distance_rotation_0_240719	.291
140	ReAlnet01_cornet_fov16	.291
141	alexnet_fov12	.291
142	compact_resnet50_V4	.264
143	resnet_50_v1_spiking_l4a	.264
144	resnet_50_v1_spiking	.264
145	yudixie_resnet50_object_class_0_240908	.264
146	grcnn	.264
147	densenet-169	.264
148	yudixie_resnet18_random_0_240719	.264
149	deit_large_imagenet_full_seed-0	.264
150	deit_small_imagenet_full_seed-0	.264
151	efficientnet_b0_imagenet_full	.264
152	resnet50_imagenet_1_seed-0	.264
153	cvt_cvt-21-384-in1k_4_LucyV4	.264
154	omnivore_swinS	.240
155	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.240
156	resnet50_ewconplus_wzc	.239
157	mobilenet_v2_1_3_224	.239
158	alexnet_training_seed_05	.239
159	mobilenet_v2_1_0_192	.239
160	resnet50-vitoimagevidnet8	.239
161	resnet50-VITO-8deg-cc	.239
162	efficientnet_b0	.239
163	vonegrcnn_47e	.239
164	resnet50_imagenet_10_seed-0	.239
165	resnet34_imagenet_full	.239
166	compact_cornets_IT	.217
167	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.217
168	ReAlnet07_cornet	.217
169	mobilenet_v2_0_5_192	.217
170	ReAlnet07	.217
171	resnet_SIN_IN_FT_IN	.217
172	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.217
173	alexnet_fov4	.217
174	pnasnet_large	.198
175	AdvProp_efficientnet-b7	.198
176	pnasnet_large_halfPrecision	.198
177	alexnet_training_seed_08	.197
178	vgg_16	.197
179	aughost_fair_resnet18	.197
180	resnet50_tutorial	.197
181	alexnet_training_seed_03	.179
182	artResNet18_1	.179
183	alexnet_training_seed_01_fov4	.179
184	resnet50_imagenet_full	.179
185	custom_model_cv_18_dagger_408	.179
186	focalnet_tiny_lrf_in1k	.179
187	alexnet_training_seed_10	.162
188	ReAlnet09_cornet	.162
189	mobilenet_v2_1_0_160	.162
190	ReAlnet09	.162
191	efficientnet_b1_imagenet_full	.162
192	compact_vgg19_IT	.147
193	alexnet_training_seed_04	.147
194	ReAlnet10_cornet	.147
195	yudixie_resnet50_rotation_reg_0_240908	.147
196	ReAlnet10	.147
197	alexnet	.147
198	convnext_small_imagenet_100_seed-0	.134
199	AlexNet_SIN_fov4	.134
200	ReAlnet08_ALL	.121
201	ReAlnet03_ALL	.121
202	yudixie_resnet50_distance_translation_rotation_0_240908	.121
203	resnet50-SIN_IN	.121
204	deit_small_imagenet_1_seed-0	.121
205	compact_resnet50_IT	.110
206	ReAlnet05_ALL	.110
207	yudixie_resnet50_distance_rotation_0_240908	.110
208	densenet-201	.110
209	AlexNet_SIN	.110
210	resnet152_imagenet_full	.110
211	ReAlnet09_ALL	.100
212	ReAlnet04_ALL	.100
213	VOneCORnet-S	.100
214	yudixie_resnet18_category_class_0_240719	.100
215	ReAlnet07_ALL	.091
216	ReAlnet05_cornet	.091
217	ReAlnet05	.091
218	resnet_50_v1_spiking_l4	.082
219	yudixie_resnet50_distance_translation_0_240908	.082
220	resnet50-barlow	.082
221	yudixie_resnet18_translation_rotation_0_240719	.082
222	ReAlnet08_cornet	.075
223	ReAlnet08	.075
224	resnet18_ecoset_full	.075
225	alexnet_training_seed_07	.068
226	ReAlnet03_cornet	.068
227	ReAlnet02_cornet	.068
228	ReAlnet03	.068
229	ReAlnet02	.068
230	resnet50_robust_l2_eps3	.068
231	imagenet_l2_3_0	.068
232	yudixie_resnet18_distance_reg_0_240719	.068
233	resnet-18_test_m	.062
234	resnet50_imagenet_100_seed-0	.062
235	ReAlnet06_ALL	.056
236	AlexNet_SIN_fov16	.056
237	ReAlnet01_cornet_fov12	.056
238	ReAlnet04_cornet	.046
239	ReAlnet04	.046
240	resnet50_robust_l2_eps1	.046
241	blt_vs	.046
242	resnet18_imagenet_full	.046
243	ReAlnet01_cornet	.034
244	ReAlnet01	.034
245	inception_v1	.031
246	ReAlnet01_cornet_fov4	.026
247	resnet-152_v2_pytorch	.021
248	resnet_152_v2	.014
249	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

0.88.

Note that scores are relative to this ceiling.

Data: Ferguson2024circle_line

Metric: value_delta