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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024round_f-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	aughost_fair_resnet18	1.0
2	alexnet_training_seed_01	1.0
3	yudixie_resnet50_translation_reg_0_240908	1.0
4	resnet50-vicreg	1.0
5	resnet-18_test_m	1.0
6	convnext_tiny:in12k_ft_in1k	1.0
7	convnext_small_imagenet_10_seed-0	1.0
8	yudixie_resnet18_translation_rotation_0_240719	1.0
9	alexnet_training_seed_01_fov16	1.0
10	ReAlnet01_cornet_fov4	1.0
11	alexnet_fov12	1.0
12	briaai_rmbg_1_4	1.0
13	alexnet_fov16	1.0
14	compact_vgg19_V4	1.0
15	VOneCORnet-S	1.0
16	vit_base_patch16_clip_224:openai_ft_in1k	1.0
17	pnasnet_large_halfPrecision	.961
18	resnet_101_v1	.961
19	inception_v4	.961
20	convnext_base:clip_laiona_augreg_ft_in1k_384	.961
21	cvt_cvt-13-224-in1k_4_LucyV4	.961
22	resnet-152_v2_pytorch	.961
23	resnext101_32x32d_wsl	.961
24	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.961
25	yudixie_resnet50_random_0_240908	.906
26	inception_v1	.906
27	yudixie_resnet50_distance_rotation_0_240908	.906
28	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.906
29	alexnet_training_seed_01_fov12	.906
30	compact_vgg19_IT	.906
31	compact_resnet50_robust_V4	.906
32	alexnet_training_seed_10	.867
33	mobilenet_v2_1_0_160	.867
34	resnet_50_v2	.867
35	yudixie_resnet18_rotation_reg_0_240719	.867
36	resnet50_ecoset_full	.867
37	cv_18_dagger_408_pretrained	.867
38	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.867
39	inception_v3	.818
40	convnext_xlarge:fb_in22k_ft_in1k	.818
41	yudixie_resnet18_distance_rotation_0_240719	.818
42	alexnet_training_seed_07	.783
43	alexnet_training_seed_04	.783
44	resnet_152_v2	.783
45	AdvProp_efficientnet-b4	.783
46	deit_small_imagenet_full_seed-0	.783
47	resnet50_imagenet_10_seed-0	.783
48	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.783
49	resnet18_imagenet_full	.783
50	resnet_101_v2	.739
51	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.739
52	AlexNet_SIN_fov16	.739
53	compact_cornets_V4	.739
54	yolos_tiny	.707
55	ReAlnet02_cornet	.707
56	ReAlnet02	.707
57	BiT-S-R152x2	.707
58	deit_base_imagenet_full_seed-0	.707
59	efficientnet_b2_imagenet_full	.707
60	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.667
61	yudixie_resnet50_distance_translation_rotation_0_240908	.667
62	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.667
63	yudixie_resnet18_random_0_240719	.667
64	resnet101_imagenet_full	.667
65	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.667
66	resnet_50_v1_spiking_l4a	.638
67	yudixie_resnet50_object_class_0_240908	.638
68	alexnet_training_seed_02	.638
69	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.638
70	vit_large_patch14_clip_224:openai_ft_in1k	.638
71	BiT-S-R101x1	.638
72	BiT-S-R101x3	.638
73	resnet50_robust_l2_eps3	.638
74	imagenet_l2_3_0	.638
75	cvt_cvt-21-224-in1k_4_LucyV4	.638
76	compact_resnet50_IT	.638
77	ReAlnet08_ALL	.638
78	deit_small_imagenet_100_seed-0	.602
79	resnext101_32x8d_wsl	.576
80	pnasnet_large	.576
81	alexnet_training_seed_06	.576
82	yudixie_resnet50_translation_rotation_0_240908	.576
83	efficientnet_b0	.576
84	BiT-S-R50x1	.576
85	ReAlnet01_cornet_fov12	.576
86	vit_base_patch14_reg4_dinov2-lvd142m	.544
87	vit_base_patch16_dinov3-lvd1689m	.544
88	vit_base_patch14_dinov2-lvd142m	.544
89	mobilenet_v2_1_4_224	.544
90	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.544
91	vit_large_patch14_clip_224:laion2b_ft_in1k	.544
92	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.544
93	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.544
94	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.544
95	convnext_large:fb_in22k_ft_in1k	.544
96	compact_resnet50_V4	.544
97	mobilenet_v2_1-4_224_pytorch	.544
98	alexnet_training_seed_09	.520
99	deit_small_imagenet_10_seed-0	.520
100	ReAlnet10_ALL	.520
101	alexnet	.520
102	ReAlnet09_cornet	.470
103	ReAlnet09	.470
104	resnet50-moclr8deg	.470
105	convnext_base_imagenet_full_seed-0	.470
106	yudixie_resnet18_translation_reg_0_240719	.470
107	alexnet_training_seed_01_fov4	.470
108	convnext_small_imagenet_1_seed-0	.443
109	yudixie_resnet18_distance_reg_0_240719	.443
110	xception	.424
111	mobilenet_v2_0_75_224	.424
112	voneresnet-50	.424
113	efficientnet_b1_imagenet_full	.424
114	efficientnet_b0_imagenet_full	.424
115	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.424
116	vit_large_patch14_clip_336:laion2b_ft_in1k	.400
117	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.400
118	deit_small_imagenet_1_seed-0	.400
119	resnet_50_v1_spiking	.383
120	convnext_tiny_imagenet_full_seed-0	.383
121	resnet18_ecoset_full	.383
122	ReAlnet06_ALL	.383
123	ReAlnet05_ALL	.383
124	pnasnet_large_pytorch	.383
125	voneresnet-50-non_stochastic	.383
126	compact_resnet50_robust_IT	.361
127	yudixie_resnet50_rotation_reg_0_240908	.346
128	omnivore_swinS	.346
129	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.346
130	artResNet18_1	.346
131	convnext_small_imagenet_100_seed-0	.346
132	yudixie_resnet18_imagenet1kpret_0_240719	.346
133	hmax	.346
134	resnet_50_v1_spiking_l4	.326
135	convnext_femto_ols:d1_in1k	.326
136	resnext101_32x48d_wsl	.326
137	alexnet_training_seed_03	.312
138	nasnet_large	.312
139	mobilenet_v2_0_5_224	.312
140	resnet152_ecoset_full	.312
141	resnet34_imagenet_full	.312
142	resnet50_ewconplus_wzc	.312
143	ReAlnet09_ALL	.312
144	ReAlnet01_cornet	.282
145	nasnet_mobile	.282
146	ReAlnet01	.282
147	deit_large_imagenet_full_seed-0	.282
148	cvt_cvt-21-384-in1k_4_LucyV4	.282
149	AlexNet_SIN_fov4	.282
150	grcnn	.255
151	shufflenet_v2_x1_0	.255
152	resnet50-SIN	.255
153	resnet50-SIN_IN	.255
154	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.255
155	vonegrcnn_52e_full	.255
156	resnet50_imagenet_100_seed-0	.255
157	ReAlnet01_cornet_fov16	.255
158	ReAlnet07_ALL	.255
159	ReAlnet02_ALL	.255
160	custom_model_cv_18_dagger_408	.255
161	yudixie_resnet50_distance_reg_0_240908	.230
162	yudixie_resnet50_distance_translation_0_240908	.230
163	omnivore_swinB	.230
164	densenet-121	.230
165	resnet_SIN_IN_FT_IN	.230
166	yudixie_resnet18_distance_translation_rotation_0_240719	.230
167	yudixie_resnet18_category_class_0_240719	.230
168	ReAlnet04_ALL	.230
169	ReAlnet03_ALL	.230
170	ReAlnet07_cornet	.208
171	ReAlnet04_cornet	.208
172	ReAlnet03_cornet	.208
173	ReAlnet03	.208
174	ReAlnet07	.208
175	ReAlnet04	.208
176	convnext_small_imagenet_full_seed-0	.208
177	AlexNet_SIN_fov12	.208
178	ReAlnet01_ALL	.208
179	resnet_50_v1_spiking_l3	.208
180	ReAlnet08_cornet	.187
181	vgg_16	.187
182	ReAlnet08	.187
183	resnet50-vitoimagevidnet8	.187
184	resnet50_robust_l2_eps1	.187
185	resnet50-VITO-8deg-cc	.187
186	AT_efficientnet-b2	.187
187	resnet50-vicregl0p9	.187
188	AdvProp_efficientnet-b2	.187
189	AdvProp_efficientnet-b6	.187
190	AlexNet_SIN	.187
191	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.169
192	convnext_large_imagenet_full_seed-0	.169
193	blt_vs	.160
194	yudixie_resnet18_object_class_0_240719	.160
195	ReAlnet05_cornet	.153
196	mobilenet_v2_0_5_192	.153
197	ReAlnet05	.153
198	voneresnet-50-robust	.153
199	AdvProp_efficientnet-b8	.153
200	vonegrcnn_47e	.153
201	resnet101_ecoset_full	.153
202	yudixie_resnet50_category_class_0_240908	.138
203	ReAlnet10_cornet	.138
204	mobilenet_v2_0_75_160	.138
205	vonegrcnn_62e_nobn	.138
206	ReAlnet10	.138
207	densenet-201	.138
208	cvt_cvt-13-384-in1k_4_LucyV4	.138
209	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.138
210	compact_cornets_IT	.138
211	resnet50_imagenet_1_seed-0	.125
212	ReAlnet06_cornet	.113
213	ReAlnet06	.113
214	BiT-S-R50x3	.113
215	yudixie_resnet18_distance_translation_0_240719	.113
216	resnet152_imagenet_full	.113
217	alexnet_training_seed_08	.102
218	resnet_152_v1	.102
219	grcnn_109	.102
220	BiT-S-R152x4	.102
221	ViT_L_32_imagenet1k	.102
222	resnet-50-robust	.102
223	resnet50_primary_visual_cortex	.092
224	mobilenet_v2_0_75_192	.092
225	efficientnet-b7	.092
226	vit_relpos_base_patch16_clsgap_224:sw_in1k	.092
227	resnet34_ecoset_full	.092
228	resnet50_imagenet_full	.092
229	alexnet_fov4	.092
230	mobilenet_v2_1_0_128	.083
231	mobilenet_v2_1_0_224	.075
232	resnet50-barlow	.075
233	densenet-169	.075
234	resnet50-vicregl0p75	.075
235	convnext_tiny_sup	.075
236	resnet50_tutorial	.075
237	effnetb1_272x240	.068
238	yudixie_resnet50_imagenet1kpret_0_240908	.061
239	mobilenet_v2_1_3_224	.061
240	AdvProp_efficientnet-b7	.055
241	focalnet_tiny_lrf_in1k	.055
242	alexnet_training_seed_05	.050
243	Res2Net50_26w_4s	.050
244	resnet_50_v1	.033
245	resnet50-sup	.033
246	mobilenet_v2_1_0_192	.030
247	antialiased-r50	.027
248	resnet50-SIN_IN_IN	.018
249	vgg_19	.012

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

0.87.

Note that scores are relative to this ceiling.

Data: Ferguson2024round_f

Metric: value_delta