Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024eighth-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
2	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
3	densenet-121	1.0
4	resnext101_32x32d_wsl	.974
5	alexnet_training_seed_05	.974
6	yudixie_resnet50_translation_reg_0_240908	.974
7	convnext_large:fb_in22k_ft_in1k	.951
8	resnet_SIN_IN_FT_IN	.882
9	mobilenet_v2_0_75_224	.882
10	AdvProp_efficientnet-b4	.882
11	omnivore_swinS	.882
12	yudixie_resnet18_category_class_0_240719	.882
13	alexnet_training_seed_01	.882
14	vit_base_patch16_clip_224:openai_ft_in1k	.882
15	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.861
16	resnext101_32x8d_wsl	.799
17	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.780
18	deit_small_imagenet_10_seed-0	.723
19	mobilenet_v2_1_0_192	.723
20	cvt_cvt-13-224-in1k_4_LucyV4	.723
21	convnext_xlarge:fb_in22k_ft_in1k	.706
22	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.655
23	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.655
24	focalnet_tiny_lrf_in1k	.655
25	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.655
26	BiT-S-R152x2	.655
27	mobilenet_v2_1_0_160	.655
28	cvt_cvt-21-224-in1k_4_LucyV4	.593
29	resnet152_ecoset_full	.593
30	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.579
31	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.537
32	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.537
33	omnivore_swinB	.524
34	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.486
35	effnetb1_272x240	.486
36	yudixie_resnet18_distance_translation_rotation_0_240719	.486
37	nasnet_large	.486
38	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.486
39	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.486
40	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.486
41	cvt_cvt-21-384-in1k_4_LucyV4	.475
42	custom_model_cv_18_dagger_408	.440
43	densenet-169	.399
44	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.399
45	convnext_small_imagenet_1_seed-0	.399
46	yudixie_resnet18_imagenet1kpret_0_240719	.399
47	BiT-S-R50x3	.361
48	vit_large_patch14_clip_224:laion2b_ft_in1k	.361
49	vit_large_patch14_clip_224:openai_ft_in1k	.361
50	yudixie_resnet50_distance_reg_0_240908	.361
51	AdvProp_efficientnet-b6	.361
52	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.361
53	resnext101_32x48d_wsl	.361
54	resnet18_ecoset_full	.361
55	AdvProp_efficientnet-b2	.361
56	AT_efficientnet-b2	.361
57	convnext_small_imagenet_10_seed-0	.327
58	deit_small_imagenet_full_seed-0	.327
59	deit_large_imagenet_full_seed-0	.327
60	resnet-18_test_m	.327
61	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.327
62	yudixie_resnet18_translation_reg_0_240719	.327
63	convnext_base:clip_laiona_augreg_ft_in1k_384	.296
64	convnext_large_imagenet_full_seed-0	.296
65	efficientnet-b7	.296
66	yudixie_resnet50_distance_rotation_0_240908	.296
67	resnet34_ecoset_full	.296
68	deit_small_imagenet_1_seed-0	.268
69	resnet50-vicregl0p75	.268
70	mobilenet_v2_1_3_224	.268
71	convnext_tiny_sup	.243
72	BiT-S-R152x4	.243
73	resnet34_imagenet_full	.243
74	BiT-S-R101x1	.220
75	resnet_101_v2	.220
76	resnet50-vicreg	.220
77	vonegrcnn_52e_full	.220
78	mobilenet_v2_1_0_224	.199
79	BiT-S-R50x1	.199
80	Res2Net50_26w_4s	.199
81	mobilenet_v2_0_5_224	.199
82	cvt_cvt-13-384-in1k_4_LucyV4	.199
83	mobilenet_v2_0_75_192	.180
84	convnext_base_imagenet_full_seed-0	.180
85	resnet-152_v2_pytorch	.180
86	resnet50-SIN_IN_IN	.180
87	yudixie_resnet18_distance_translation_0_240719	.163
88	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.163
89	pnasnet_large_pytorch	.148
90	resnet50_imagenet_full	.148
91	pnasnet_large	.148
92	deit_base_imagenet_full_seed-0	.148
93	efficientnet_b2_imagenet_full	.148
94	resnet50-moclr8deg	.148
95	BiT-S-R101x3	.148
96	resnet50-SIN	.134
97	grcnn_109	.134
98	resnet_50_v2	.134
99	efficientnet_b1_imagenet_full	.134
100	densenet-201	.134
101	grcnn	.134
102	voneresnet-50	.134
103	yudixie_resnet50_imagenet1kpret_0_240908	.121
104	yudixie_resnet50_rotation_reg_0_240908	.121
105	artResNet18_1	.121
106	yudixie_resnet18_translation_rotation_0_240719	.121
107	yudixie_resnet18_object_class_0_240719	.121
108	convnext_small_imagenet_full_seed-0	.121
109	ViT_L_32_imagenet1k	.121
110	resnet152_imagenet_full	.121
111	inception_v3	.110
112	AdvProp_efficientnet-b8	.110
113	antialiased-r50	.110
114	xception	.110
115	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.110
116	VOneCORnet-S	.110
117	resnet50_imagenet_10_seed-0	.110
118	convnext_tiny_imagenet_full_seed-0	.110
119	nasnet_mobile	.099
120	alexnet_training_seed_09	.099
121	alexnet_training_seed_08	.099
122	convnext_tiny:in12k_ft_in1k	.099
123	convnext_small_imagenet_100_seed-0	.099
124	yolos_tiny	.099
125	yudixie_resnet18_distance_rotation_0_240719	.099
126	yudixie_resnet50_distance_translation_rotation_0_240908	.099
127	deit_small_imagenet_100_seed-0	.099
128	vgg_19	.099
129	vgg_16	.090
130	alexnet_training_seed_02	.081
131	mobilenet_v2_1_4_224	.081
132	yudixie_resnet50_object_class_0_240908	.081
133	efficientnet_b0	.081
134	mobilenet_v2_1-4_224_pytorch	.081
135	ReAlnet09_cornet	.081
136	mobilenet_v2_0_75_160	.081
137	shufflenet_v2_x1_0	.081
138	ReAlnet09	.081
139	yudixie_resnet18_distance_reg_0_240719	.074
140	ReAlnet04_cornet	.067
141	ReAlnet04	.067
142	ReAlnet08	.067
143	ReAlnet08_cornet	.067
144	resnet_152_v2	.067
145	inception_v4	.067
146	resnet50_tutorial	.060
147	resnet18_imagenet_full	.060
148	resnet_152_v1	.060
149	resnet101_ecoset_full	.060
150	alexnet_training_seed_03	.055
151	resnet50-barlow	.055
152	resnet101_imagenet_full	.055
153	yudixie_resnet50_category_class_0_240908	.050
154	yudixie_resnet18_random_0_240719	.050
155	vonegrcnn_62e_nobn	.050
156	yudixie_resnet18_rotation_reg_0_240719	.045
157	briaai_rmbg_1_4	.041
158	mobilenet_v2_0_5_192	.041
159	vonegrcnn_47e	.041
160	ReAlnet06	.041
161	ReAlnet06_cornet	.041
162	AdvProp_efficientnet-b7	.037
163	resnet50_imagenet_100_seed-0	.037
164	ReAlnet10	.037
165	yudixie_resnet50_distance_translation_0_240908	.037
166	ReAlnet10_cornet	.037
167	convnext_femto_ols:d1_in1k	.037
168	alexnet_training_seed_06	.033
169	mobilenet_v2_1_0_128	.033
170	alexnet_training_seed_07	.033
171	inception_v1	.030
172	resnet_101_v1	.027
173	resnet50_ecoset_full	.027
174	resnet50-vicregl0p9	.027
175	ReAlnet03	.027
176	ReAlnet03_cornet	.027
177	resnet50-VITO-8deg-cc	.025
178	resnet-50-robust	.025
179	resnet50-vitoimagevidnet8	.025
180	resnet_50_v1	.025
181	resnet50-sup	.025
182	ReAlnet05	.022
183	ReAlnet05_cornet	.022
184	voneresnet-50-robust	.022
185	ReAlnet02	.020
186	blt_vs	.020
187	ReAlnet02_cornet	.020
188	efficientnet_b0_imagenet_full	.018
189	resnet50-SIN_IN	.017
190	yudixie_resnet50_translation_rotation_0_240908	.017
191	ReAlnet07_cornet	.017
192	ReAlnet07	.017
193	yudixie_resnet50_random_0_240908	.015
194	resnet50_robust_l2_eps1	.014
195	alexnet_training_seed_10	.011
196	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.011
197	alexnet_training_seed_04	.011
198	ReAlnet_incorrect	.010
199	ReAlnet01_cornet	.010
200	ReAlnet01	.010
201	voneresnet-50-non_stochastic	.010
202	imagenet_l2_3_0	.008
203	resnet50_robust_l2_eps3	.008
204	resnet50_imagenet_1_seed-0	.006
205	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

0.85.

Note that scores are relative to this ceiling.

Data: Ferguson2024eighth

Metric: value_delta