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