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	resnet_50_v1_spiking_l4	1.0
2	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
3	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
4	densenet-121	1.0
5	resnext101_32x32d_wsl	.974
6	yudixie_resnet50_translation_reg_0_240908	.974
7	alexnet_training_seed_05	.974
8	convnext_large:fb_in22k_ft_in1k	.951
9	mobilenet_v2_0_75_224	.882
10	alexnet_training_seed_01	.882
11	omnivore_swinS	.882
12	AdvProp_efficientnet-b4	.882
13	resnet_SIN_IN_FT_IN	.882
14	yudixie_resnet18_category_class_0_240719	.882
15	vit_base_patch16_clip_224:openai_ft_in1k	.882
16	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.861
17	resnext101_32x8d_wsl	.799
18	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.780
19	mobilenet_v2_1_0_192	.723
20	cvt_cvt-13-224-in1k_4_LucyV4	.723
21	deit_small_imagenet_10_seed-0	.723
22	convnext_xlarge:fb_in22k_ft_in1k	.706
23	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.655
24	focalnet_tiny_lrf_in1k	.655
25	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.655
26	mobilenet_v2_1_0_160	.655
27	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.655
28	BiT-S-R152x2	.655
29	cvt_cvt-21-224-in1k_4_LucyV4	.593
30	resnet152_ecoset_full	.593
31	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.579
32	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.537
33	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.537
34	omnivore_swinB	.524
35	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.486
36	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.486
37	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.486
38	nasnet_large	.486
39	resnet_50_v1_spiking	.486
40	effnetb1_272x240	.486
41	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.486
42	yudixie_resnet18_distance_translation_rotation_0_240719	.486
43	cvt_cvt-21-384-in1k_4_LucyV4	.475
44	custom_model_cv_18_dagger_408	.440
45	densenet-169	.399
46	yudixie_resnet18_imagenet1kpret_0_240719	.399
47	convnext_small_imagenet_1_seed-0	.399
48	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.399
49	resnext101_32x48d_wsl	.361
50	yudixie_resnet50_distance_reg_0_240908	.361
51	resnet18_ecoset_full	.361
52	AdvProp_efficientnet-b2	.361
53	vit_large_patch14_clip_224:openai_ft_in1k	.361
54	AT_efficientnet-b2	.361
55	vit_large_patch14_clip_224:laion2b_ft_in1k	.361
56	BiT-S-R50x3	.361
57	AdvProp_efficientnet-b6	.361
58	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.361
59	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.327
60	deit_small_imagenet_full_seed-0	.327
61	deit_large_imagenet_full_seed-0	.327
62	convnext_small_imagenet_10_seed-0	.327
63	yudixie_resnet18_translation_reg_0_240719	.327
64	resnet-18_test_m	.327
65	resnet34_ecoset_full	.296
66	convnext_large_imagenet_full_seed-0	.296
67	efficientnet-b7	.296
68	convnext_base:clip_laiona_augreg_ft_in1k_384	.296
69	yudixie_resnet50_distance_rotation_0_240908	.296
70	deit_small_imagenet_1_seed-0	.268
71	mobilenet_v2_1_3_224	.268
72	resnet50-vicregl0p75	.268
73	resnet34_imagenet_full	.243
74	BiT-S-R152x4	.243
75	convnext_tiny_sup	.243
76	vonegrcnn_52e_full	.220
77	resnet50-vicreg	.220
78	resnet_101_v2	.220
79	BiT-S-R101x1	.220
80	Res2Net50_26w_4s	.199
81	mobilenet_v2_1_0_224	.199
82	cvt_cvt-13-384-in1k_4_LucyV4	.199
83	mobilenet_v2_0_5_224	.199
84	BiT-S-R50x1	.199
85	resnet-152_v2_pytorch	.180
86	convnext_base_imagenet_full_seed-0	.180
87	mobilenet_v2_0_75_192	.180
88	resnet50_primary_visual_cortex	.180
89	resnet50-SIN_IN_IN	.180
90	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.163
91	yudixie_resnet18_distance_translation_0_240719	.163
92	resnet_50_v1_spiking_l3	.148
93	resnet50_imagenet_full	.148
94	BiT-S-R101x3	.148
95	deit_base_imagenet_full_seed-0	.148
96	pnasnet_large_pytorch	.148
97	efficientnet_b2_imagenet_full	.148
98	resnet50-moclr8deg	.148
99	pnasnet_large	.148
100	resnet_50_v2	.134
101	efficientnet_b1_imagenet_full	.134
102	voneresnet-50	.134
103	grcnn_109	.134
104	grcnn	.134
105	resnet50-SIN	.134
106	densenet-201	.134
107	artResNet18_1	.121
108	yudixie_resnet18_translation_rotation_0_240719	.121
109	yudixie_resnet18_object_class_0_240719	.121
110	yudixie_resnet50_rotation_reg_0_240908	.121
111	convnext_small_imagenet_full_seed-0	.121
112	resnet152_imagenet_full	.121
113	ViT_L_32_imagenet1k	.121
114	yudixie_resnet50_imagenet1kpret_0_240908	.121
115	AdvProp_efficientnet-b8	.110
116	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.110
117	VOneCORnet-S	.110
118	antialiased-r50	.110
119	resnet50_imagenet_10_seed-0	.110
120	xception	.110
121	convnext_tiny_imagenet_full_seed-0	.110
122	inception_v3	.110
123	deit_small_imagenet_100_seed-0	.099
124	vgg_19	.099
125	yudixie_resnet18_distance_rotation_0_240719	.099
126	alexnet_training_seed_08	.099
127	convnext_small_imagenet_100_seed-0	.099
128	alexnet_training_seed_09	.099
129	convnext_tiny:in12k_ft_in1k	.099
130	nasnet_mobile	.099
131	yolos_tiny	.099
132	yudixie_resnet50_distance_translation_rotation_0_240908	.099
133	vgg_16	.090
134	ReAlnet09_cornet	.081
135	mobilenet_v2_0_75_160	.081
136	efficientnet_b0	.081
137	ReAlnet09	.081
138	yudixie_resnet50_object_class_0_240908	.081
139	mobilenet_v2_1-4_224_pytorch	.081
140	shufflenet_v2_x1_0	.081
141	alexnet_training_seed_02	.081
142	mobilenet_v2_1_4_224	.081
143	yudixie_resnet18_distance_reg_0_240719	.074
144	ReAlnet04_cornet	.067
145	ReAlnet08	.067
146	inception_v4	.067
147	ReAlnet08_cornet	.067
148	resnet_152_v2	.067
149	ReAlnet04	.067
150	resnet18_imagenet_full	.060
151	resnet50_tutorial	.060
152	resnet_152_v1	.060
153	resnet101_ecoset_full	.060
154	resnet50-barlow	.055
155	alexnet_training_seed_03	.055
156	resnet101_imagenet_full	.055
157	vonegrcnn_62e_nobn	.050
158	yudixie_resnet18_random_0_240719	.050
159	yudixie_resnet50_category_class_0_240908	.050
160	yudixie_resnet18_rotation_reg_0_240719	.045
161	briaai_rmbg_1_4	.041
162	ReAlnet06	.041
163	mobilenet_v2_0_5_192	.041
164	vonegrcnn_47e	.041
165	ReAlnet06_cornet	.041
166	ReAlnet10	.037
167	resnet_50_v1_spiking_l4a	.037
168	resnet50_imagenet_100_seed-0	.037
169	ReAlnet10_cornet	.037
170	yudixie_resnet50_distance_translation_0_240908	.037
171	convnext_femto_ols:d1_in1k	.037
172	AdvProp_efficientnet-b7	.037
173	alexnet_training_seed_06	.033
174	alexnet_training_seed_07	.033
175	mobilenet_v2_1_0_128	.033
176	inception_v1	.030
177	resnet50_ecoset_full	.027
178	ReAlnet03_cornet	.027
179	ReAlnet03	.027
180	resnet_101_v1	.027
181	resnet50-vicregl0p9	.027
182	resnet50-vitoimagevidnet8	.025
183	resnet-50-robust	.025
184	resnet_50_v1	.025
185	resnet50-VITO-8deg-cc	.025
186	resnet50-sup	.025
187	voneresnet-50-robust	.022
188	ReAlnet05_cornet	.022
189	ReAlnet05	.022
190	ReAlnet02_cornet	.020
191	ReAlnet02	.020
192	blt_vs	.020
193	efficientnet_b0_imagenet_full	.018
194	ReAlnet07_cornet	.017
195	ReAlnet07	.017
196	yudixie_resnet50_translation_rotation_0_240908	.017
197	resnet50-SIN_IN	.017
198	yudixie_resnet50_random_0_240908	.015
199	resnet50_robust_l2_eps1	.014
200	alexnet_training_seed_10	.011
201	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.011
202	alexnet_training_seed_04	.011
203	voneresnet-50-non_stochastic	.010
204	ReAlnet01	.010
205	ReAlnet01_cornet	.010
206	imagenet_l2_3_0	.008
207	resnet50_robust_l2_eps3	.008
208	resnet50_imagenet_1_seed-0	.006
209	hmax

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