Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024quarter-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	yudixie_resnet50_translation_reg_0_240908	1.0
2	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	1.0
3	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
4	mobilenet_v2_0_5_224	1.0
5	omnivore_swinB	1.0
6	mobilenet_v2_0_75_160	1.0
7	resnet_50_v1	1.0
8	resnet50_ecoset_full	1.0
9	resnet50-sup	1.0
10	alexnet_training_seed_04	1.0
11	resnext101_32x8d_wsl	1.0
12	resnet34_ecoset_full	.950
13	deit_large_imagenet_full_seed-0	.950
14	convnext_large:fb_in22k_ft_in1k	.950
15	inception_v4	.950
16	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.950
17	resnet_152_v1	.950
18	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.950
19	ViT_L_32_imagenet1k	.950
20	effnetb1_272x240	.950
21	resnet18_imagenet_full	.950
22	mobilenet_v2_0_75_192	.950
23	resnet18_ecoset_full	.923
24	resnext101_32x48d_wsl	.923
25	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.923
26	BiT-S-R152x2	.923
27	cvt_cvt-21-224-in1k_4_LucyV4	.860
28	convnext_tiny:in12k_ft_in1k	.836
29	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.836
30	antialiased-r50	.836
31	resnet50_tutorial	.836
32	resnet_SIN_IN_FT_IN	.836
33	mobilenet_v2_1_0_224	.836
34	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.779
35	vit_large_patch14_clip_336:laion2b_ft_in1k	.779
36	deit_small_imagenet_1_seed-0	.757
37	grcnn	.757
38	yudixie_resnet50_category_class_0_240908	.757
39	efficientnet_b0	.757
40	yudixie_resnet18_distance_reg_0_240719	.706
41	yudixie_resnet18_imagenet1kpret_0_240719	.706
42	alexnet_training_seed_05	.706
43	cvt_cvt-21-384-in1k_4_LucyV4	.706
44	BiT-S-R152x4	.706
45	voneresnet-50-non_stochastic	.686
46	resnet_50_v2	.686
47	yudixie_resnet50_distance_translation_rotation_0_240908	.686
48	AT_efficientnet-b2	.686
49	AdvProp_efficientnet-b2	.686
50	convnext_base_imagenet_full_seed-0	.686
51	convnext_large_imagenet_full_seed-0	.686
52	convnext_xlarge:fb_in22k_ft_in1k	.639
53	convnext_small_imagenet_full_seed-0	.621
54	convnext_tiny_sup	.621
55	efficientnet-b7	.621
56	artResNet18_1	.579
57	alexnet_training_seed_10	.563
58	alexnet_training_seed_06	.563
59	efficientnet_b2_imagenet_full	.563
60	resnet50_imagenet_1_seed-0	.563
61	resnet50_robust_l2_eps3	.563
62	imagenet_l2_3_0	.563
63	AdvProp_efficientnet-b7	.563
64	vonegrcnn_62e_nobn	.563
65	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.524
66	cvt_cvt-13-224-in1k_4_LucyV4	.524
67	densenet-201	.510
68	deit_base_imagenet_full_seed-0	.510
69	yudixie_resnet50_imagenet1kpret_0_240908	.510
70	mobilenet_v2_1_0_128	.510
71	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.510
72	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.510
73	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.510
74	resnet50-SIN_IN_IN	.510
75	densenet-121	.510
76	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.510
77	resnet50_robust_l2_eps1	.510
78	resnet50-vicregl0p75	.510
79	vgg_16	.510
80	resnet50_imagenet_100_seed-0	.510
81	BiT-S-R50x3	.510
82	resnet101_imagenet_full	.510
83	custom_model_cv_18_dagger_408	.475
84	resnext101_32x32d_wsl	.475
85	resnet50-vicreg	.475
86	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.475
87	BiT-S-R50x1	.475
88	deit_small_imagenet_full_seed-0	.475
89	resnet-152_v2_pytorch	.461
90	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.461
91	inception_v3	.461
92	densenet-169	.461
93	vonegrcnn_47e	.461
94	mobilenet_v2_1_0_160	.430
95	yudixie_resnet50_rotation_reg_0_240908	.430
96	yudixie_resnet18_random_0_240719	.430
97	vit_relpos_base_patch16_clsgap_224:sw_in1k	.430
98	Res2Net50_26w_4s	.418
99	mobilenet_v2_0_5_192	.418
100	grcnn_109	.418
101	yudixie_resnet18_distance_rotation_0_240719	.418
102	vit_base_patch16_clip_224:openai_ft_in1k	.390
103	yudixie_resnet18_distance_translation_0_240719	.390
104	focalnet_tiny_lrf_in1k	.379
105	vit_large_patch14_clip_224:openai_ft_in1k	.379
106	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.379
107	blt_vs	.379
108	convnext_base:clip_laiona_augreg_ft_in1k_384	.379
109	resnet50_imagenet_10_seed-0	.379
110	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.353
111	xception	.343
112	yudixie_resnet18_distance_translation_rotation_0_240719	.343
113	resnet-18_test_m	.320
114	alexnet_training_seed_02	.311
115	ReAlnet05	.311
116	ReAlnet05_cornet	.311
117	yudixie_resnet50_distance_rotation_0_240908	.311
118	resnet152_ecoset_full	.289
119	nasnet_mobile	.289
120	yudixie_resnet50_distance_translation_0_240908	.281
121	efficientnet_b1_imagenet_full	.281
122	deit_small_imagenet_10_seed-0	.281
123	deit_small_imagenet_100_seed-0	.281
124	cvt_cvt-13-384-in1k_4_LucyV4	.262
125	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.262
126	ReAlnet07_cornet	.255
127	ReAlnet07	.255
128	resnet152_imagenet_full	.255
129	vgg_19	.255
130	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.255
131	resnet50-SIN	.255
132	resnet50-barlow	.231
133	yudixie_resnet18_translation_rotation_0_240719	.231
134	BiT-S-R101x3	.231
135	mobilenet_v2_1-4_224_pytorch	.231
136	resnet34_imagenet_full	.231
137	resnet-50-robust	.231
138	mobilenet_v2_1_4_224	.231
139	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.215
140	VOneCORnet-S	.209
141	ReAlnet_incorrect	.209
142	efficientnet_b0_imagenet_full	.209
143	yudixie_resnet50_distance_reg_0_240908	.209
144	ReAlnet01	.209
145	ReAlnet01_cornet	.209
146	ReAlnet10	.209
147	ReAlnet10_cornet	.209
148	BiT-S-R101x1	.189
149	resnet50_imagenet_full	.189
150	convnext_small_imagenet_1_seed-0	.189
151	ReAlnet03	.171
152	ReAlnet09_cornet	.171
153	inception_v1	.171
154	resnet101_ecoset_full	.171
155	resnet_101_v1	.171
156	alexnet_training_seed_07	.171
157	ReAlnet03_cornet	.171
158	shufflenet_v2_x1_0	.171
159	omnivore_swinS	.171
160	yudixie_resnet50_translation_rotation_0_240908	.171
161	ReAlnet09	.171
162	voneresnet-50	.155
163	resnet_152_v2	.155
164	convnext_small_imagenet_10_seed-0	.155
165	ReAlnet04	.155
166	ReAlnet02_cornet	.155
167	ReAlnet02	.155
168	resnet50-vicregl0p9	.155
169	ReAlnet04_cornet	.155
170	resnet50-VITO-8deg-cc	.141
171	alexnet_training_seed_01	.141
172	ReAlnet06_cornet	.141
173	resnet50-vitoimagevidnet8	.141
174	mobilenet_v2_1_0_192	.141
175	AdvProp_efficientnet-b8	.141
176	convnext_small_imagenet_100_seed-0	.141
177	yudixie_resnet50_object_class_0_240908	.141
178	ReAlnet06	.141
179	convnext_femto_ols:d1_in1k	.141
180	yudixie_resnet50_random_0_240908	.127
181	ReAlnet08	.127
182	pnasnet_large_pytorch	.127
183	ReAlnet08_cornet	.127
184	resnet_101_v2	.119
185	AdvProp_efficientnet-b4	.115
186	mobilenet_v2_1_3_224	.115
187	alexnet_training_seed_08	.115
188	yudixie_resnet18_object_class_0_240719	.104
189	alexnet_training_seed_03	.095
190	vonegrcnn_52e_full	.095
191	yudixie_resnet18_translation_reg_0_240719	.095
192	yudixie_resnet18_rotation_reg_0_240719	.086
193	voneresnet-50-robust	.078
194	alexnet_training_seed_09	.078
195	resnet50-SIN_IN	.078
196	convnext_tiny_imagenet_full_seed-0	.070
197	resnet50-moclr8deg	.064
198	yudixie_resnet18_category_class_0_240719	.064
199	nasnet_large	.047
200	AdvProp_efficientnet-b6	.043
201	pnasnet_large	X
202	mobilenet_v2_0_75_224	X
203	hmax	X

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: Ferguson2024quarter

Metric: value_delta