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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	compact_resnet50_robust_IT	1.0
2	resnet_50_v1_spiking_l4	1.0
3	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
4	compact_vgg19_V4	1.0
5	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
6	densenet-121	1.0
7	compact_cornets_V4	.974
8	alexnet_training_seed_05	.974
9	yudixie_resnet50_translation_reg_0_240908	.974
10	resnext101_32x32d_wsl	.974
11	convnext_large:fb_in22k_ft_in1k	.951
12	alexnet_training_seed_01_fov16	.882
13	alexnet_training_seed_01	.882
14	mobilenet_v2_0_75_224	.882
15	omnivore_swinS	.882
16	vit_base_patch16_clip_224:openai_ft_in1k	.882
17	resnet_SIN_IN_FT_IN	.882
18	AdvProp_efficientnet-b4	.882
19	yudixie_resnet18_category_class_0_240719	.882
20	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.861
21	resnext101_32x8d_wsl	.799
22	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.780
23	cv_18_dagger_408_pretrained	.723
24	mobilenet_v2_1_0_192	.723
25	deit_small_imagenet_10_seed-0	.723
26	cvt_cvt-13-224-in1k_4_LucyV4	.723
27	convnext_xlarge:fb_in22k_ft_in1k	.706
28	mobilenet_v2_1_0_160	.655
29	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.655
30	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.655
31	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.655
32	BiT-S-R152x2	.655
33	focalnet_tiny_lrf_in1k	.655
34	vit_base_patch14_reg4_dinov2:lvd142m	.593
35	resnet152_ecoset_full	.593
36	cvt_cvt-21-224-in1k_4_LucyV4	.593
37	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.579
38	vit_base_patch14_dinov2:lvd142m	.537
39	ReAlnet05_ALL	.537
40	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.537
41	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.537
42	omnivore_swinB	.524
43	resnet_50_v1_spiking	.486
44	nasnet_large	.486
45	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.486
46	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.486
47	yudixie_resnet18_distance_translation_rotation_0_240719	.486
48	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.486
49	effnetb1_272x240	.486
50	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.486
51	cvt_cvt-21-384-in1k_4_LucyV4	.475
52	compact_cornets_IT	.440
53	compact_resnet50_robust_V4	.440
54	custom_model_cv_18_dagger_408	.440
55	compact_resnet50_IT	.440
56	compact_resnet50_V4	.440
57	alexnet_training_seed_01_fov12	.399
58	AlexNet_SIN_fov4	.399
59	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.399
60	ReAlnet03_ALL	.399
61	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.399
62	densenet-169	.399
63	convnext_small_imagenet_1_seed-0	.399
64	yudixie_resnet18_imagenet1kpret_0_240719	.399
65	yudixie_resnet50_distance_reg_0_240908	.361
66	vit_large_patch14_clip_224:openai_ft_in1k	.361
67	vit_large_patch14_clip_224:laion2b_ft_in1k	.361
68	AT_efficientnet-b2	.361
69	BiT-S-R50x3	.361
70	AdvProp_efficientnet-b2	.361
71	AdvProp_efficientnet-b6	.361
72	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.361
73	resnet18_ecoset_full	.361
74	resnext101_32x48d_wsl	.361
75	resnet-18_test_m	.327
76	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.327
77	convnext_small_imagenet_10_seed-0	.327
78	yudixie_resnet18_translation_reg_0_240719	.327
79	deit_large_imagenet_full_seed-0	.327
80	deit_small_imagenet_full_seed-0	.327
81	resnet50_ewconplus_wzc	.296
82	yudixie_resnet50_distance_rotation_0_240908	.296
83	efficientnet-b7	.296
84	convnext_base:clip_laiona_augreg_ft_in1k_384	.296
85	convnext_large_imagenet_full_seed-0	.296
86	resnet34_ecoset_full	.296
87	alexnet_fov12	.268
88	mobilenet_v2_1_3_224	.268
89	resnet50-vicregl0p75	.268
90	deit_small_imagenet_1_seed-0	.268
91	AlexNet_SIN	.243
92	BiT-S-R152x4	.243
93	convnext_tiny_sup	.243
94	resnet34_imagenet_full	.243
95	resnet_101_v2	.220
96	resnet50-vicreg	.220
97	BiT-S-R101x1	.220
98	vonegrcnn_52e_full	.220
99	ReAlnet06_ALL	.199
100	ReAlnet04_ALL	.199
101	mobilenet_v2_1_0_224	.199
102	mobilenet_v2_0_5_224	.199
103	Res2Net50_26w_4s	.199
104	BiT-S-R50x1	.199
105	cvt_cvt-13-384-in1k_4_LucyV4	.199
106	vit_base_patch16_dinov3:lvd1689m	.180
107	ReAlnet10_ALL	.180
108	resnet50_primary_visual_cortex	.180
109	mobilenet_v2_0_75_192	.180
110	resnet50-SIN_IN_IN	.180
111	convnext_base_imagenet_full_seed-0	.180
112	resnet-152_v2_pytorch	.180
113	AlexNet_SIN_fov12	.163
114	ReAlnet01_cornet_fov12	.163
115	ReAlnet08_ALL	.163
116	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.163
117	yudixie_resnet18_distance_translation_0_240719	.163
118	ReAlnet01_ALL	.148
119	resnet_50_v1_spiking_l3	.148
120	pnasnet_large	.148
121	resnet50-moclr8deg	.148
122	BiT-S-R101x3	.148
123	deit_base_imagenet_full_seed-0	.148
124	efficientnet_b2_imagenet_full	.148
125	resnet50_imagenet_full	.148
126	pnasnet_large_pytorch	.148
127	alexnet_fov16	.134
128	ReAlnet02_ALL	.134
129	resnet_50_v2	.134
130	grcnn_109	.134
131	grcnn	.134
132	voneresnet-50	.134
133	resnet50-SIN	.134
134	densenet-201	.134
135	efficientnet_b1_imagenet_full	.134
136	compact_vgg19_IT	.121
137	yudixie_resnet50_imagenet1kpret_0_240908	.121
138	yudixie_resnet50_rotation_reg_0_240908	.121
139	artResNet18_1	.121
140	yudixie_resnet18_object_class_0_240719	.121
141	yudixie_resnet18_translation_rotation_0_240719	.121
142	convnext_small_imagenet_full_seed-0	.121
143	resnet152_imagenet_full	.121
144	ViT_L_32_imagenet1k	.121
145	AlexNet_SIN_fov16	.110
146	alexnet_workflow_trigger	.110
147	xception	.110
148	inception_v3	.110
149	VOneCORnet-S	.110
150	AdvProp_efficientnet-b8	.110
151	antialiased-r50	.110
152	convnext_tiny_imagenet_full_seed-0	.110
153	resnet50_imagenet_10_seed-0	.110
154	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.110
155	alexnet	.110
156	yolos_tiny	.099
157	yudixie_resnet50_distance_translation_rotation_0_240908	.099
158	alexnet_training_seed_08	.099
159	alexnet_training_seed_09	.099
160	vgg_19	.099
161	nasnet_mobile	.099
162	convnext_tiny:in12k_ft_in1k	.099
163	convnext_small_imagenet_100_seed-0	.099
164	yudixie_resnet18_distance_rotation_0_240719	.099
165	deit_small_imagenet_100_seed-0	.099
166	vgg_16	.090
167	yudixie_resnet50_object_class_0_240908	.081
168	mobilenet_v2_1_4_224	.081
169	alexnet_training_seed_02	.081
170	ReAlnet09_cornet	.081
171	mobilenet_v2_0_75_160	.081
172	ReAlnet09	.081
173	shufflenet_v2_x1_0	.081
174	efficientnet_b0	.081
175	mobilenet_v2_1-4_224_pytorch	.081
176	yudixie_resnet18_distance_reg_0_240719	.074
177	alexnet_fov4	.067
178	ReAlnet08_cornet	.067
179	ReAlnet04_cornet	.067
180	resnet_152_v2	.067
181	inception_v4	.067
182	ReAlnet08	.067
183	ReAlnet04	.067
184	ReAlnet01_cornet_fov16	.060
185	resnet_152_v1	.060
186	resnet50_tutorial	.060
187	resnet101_ecoset_full	.060
188	resnet18_imagenet_full	.060
189	alexnet_training_seed_03	.055
190	resnet50-barlow	.055
191	resnet101_imagenet_full	.055
192	ReAlnet09_ALL	.050
193	yudixie_resnet50_category_class_0_240908	.050
194	vonegrcnn_62e_nobn	.050
195	yudixie_resnet18_random_0_240719	.050
196	alexnet_training_seed_01_fov4	.045
197	yudixie_resnet18_rotation_reg_0_240719	.045
198	ReAlnet07_ALL	.041
199	briaai_rmbg_1_4	.041
200	ReAlnet06_cornet	.041
201	mobilenet_v2_0_5_192	.041
202	ReAlnet06	.041
203	vonegrcnn_47e	.041
204	resnet_50_v1_spiking_l4a	.037
205	ReAlnet10_cornet	.037
206	yudixie_resnet50_distance_translation_0_240908	.037
207	ReAlnet10	.037
208	convnext_femto_ols:d1_in1k	.037
209	AdvProp_efficientnet-b7	.037
210	resnet50_imagenet_100_seed-0	.037
211	alexnet_training_seed_07	.033
212	alexnet_training_seed_06	.033
213	mobilenet_v2_1_0_128	.033
214	inception_v1	.030
215	ReAlnet03_cornet	.027
216	resnet_101_v1	.027
217	ReAlnet03	.027
218	resnet50-vicregl0p9	.027
219	resnet50_ecoset_full	.027
220	resnet_50_v1	.025
221	resnet50-vitoimagevidnet8	.025
222	resnet50-VITO-8deg-cc	.025
223	resnet50-sup	.025
224	resnet-50-robust	.025
225	ReAlnet01_cornet_fov4	.022
226	ReAlnet05_cornet	.022
227	ReAlnet05	.022
228	voneresnet-50-robust	.022
229	ReAlnet02_cornet	.020
230	ReAlnet02	.020
231	blt_vs	.020
232	efficientnet_b0_imagenet_full	.018
233	ReAlnet07_cornet	.017
234	yudixie_resnet50_translation_rotation_0_240908	.017
235	ReAlnet07	.017
236	resnet50-SIN_IN	.017
237	yudixie_resnet50_random_0_240908	.015
238	resnet50_robust_l2_eps1	.014
239	alexnet_training_seed_10	.011
240	alexnet_training_seed_04	.011
241	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.011
242	ReAlnet01_cornet	.010
243	ReAlnet01	.010
244	voneresnet-50-non_stochastic	.010
245	imagenet_l2_3_0	.008
246	resnet50_robust_l2_eps3	.008
247	resnet50_imagenet_1_seed-0	.006
248	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