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