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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024color-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	cv_18_dagger_408_pretrained	1.0
2	AlexNet_SIN	1.0
3	ReAlnet06_ALL	1.0
4	efficientnet-b7	1.0
5	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
6	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
7	vit_large_patch14_clip_224:openai_ft_in1k	1.0
8	vit_large_patch14_clip_224:openai_ft_in12k_in1k	1.0
9	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
10	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
11	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
12	omnivore_swinS	1.0
13	efficientnet_b0	1.0
14	omnivore_swinB	1.0
15	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	1.0
16	vit_large_patch14_clip_336:openai_ft_in12k_in1k	1.0
17	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
18	vit_base_patch16_clip_224:openai_ft_in1k	1.0
19	vit_base_patch16_clip_224:openai_ft_in12k_in1k	1.0
20	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	1.0
21	swin_small_patch4_window7_224:ms_in22k_ft_in1k	1.0
22	convnext_xxlarge:clip_laion2b_soup_ft_in1k	1.0
23	convnext_xlarge:fb_in22k_ft_in1k	1.0
24	convnext_large:fb_in22k_ft_in1k	1.0
25	convnext_base:clip_laiona_augreg_ft_in1k_384	1.0
26	AT_efficientnet-b2	1.0
27	resnet50-vicregl0p9	1.0
28	BiT-S-R101x1	1.0
29	BiT-S-R50x1	1.0
30	BiT-S-R152x2	1.0
31	BiT-S-R101x3	1.0
32	BiT-S-R152x4	1.0
33	convnext_tiny_sup	1.0
34	AdvProp_efficientnet-b8	1.0
35	BiT-S-R50x3	1.0
36	resnet50_robust_l2_eps3	1.0
37	imagenet_l2_3_0	1.0
38	AdvProp_efficientnet-b2	1.0
39	AdvProp_efficientnet-b4	1.0
40	AdvProp_efficientnet-b7	1.0
41	AdvProp_efficientnet-b6	1.0
42	convnext_large_imagenet_full_seed-0	1.0
43	convnext_base_imagenet_full_seed-0	1.0
44	yudixie_resnet18_translation_rotation_0_240719	1.0
45	deit_base_imagenet_full_seed-0	1.0
46	efficientnet_b2_imagenet_full	1.0
47	efficientnet_b1_imagenet_full	1.0
48	cvt_cvt-21-384-in1k_4_LucyV4	1.0
49	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	1.0
50	cvt_cvt-13-384-in1k_4_LucyV4	1.0
51	cvt_cvt-21-224-in1k_4_LucyV4	1.0
52	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	1.0
53	cvt_cvt-13-224-in1k_4_LucyV4	1.0
54	cvt_cvt-w24-384-in22k_finetuned-in1k_4	1.0
55	ViT_L_32_imagenet1k	1.0
56	custom_model_cv_18_dagger_408	1.0
57	focalnet_tiny_lrf_in1k	1.0
58	resnet-152_v2_pytorch	1.0
59	resnet_50_v1	.968
60	voneresnet-50	.968
61	resnet50-vitoimagevidnet8	.968
62	resnet50-VITO-8deg-cc	.968
63	densenet-169	.968
64	resnet50-sup	.968
65	resnet50_imagenet_100_seed-0	.968
66	yolos_tiny	.952
67	pnasnet_large	.952
68	mobilenet_v2_1_0_192	.952
69	resnet_152_v2	.952
70	inception_v4	.952
71	mobilenet_v2_0_75_160	.952
72	yudixie_resnet18_distance_rotation_0_240719	.952
73	voneresnet-50-non_stochastic	.952
74	ReAlnet01_ALL	.877
75	ReAlnet10_cornet	.877
76	ReAlnet09_cornet	.877
77	grcnn_109	.877
78	vonegrcnn_62e_nobn	.877
79	ReAlnet10	.877
80	ReAlnet09	.877
81	resnet50-SIN_IN_IN	.877
82	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.877
83	resnet_SIN_IN_FT_IN	.877
84	vonegrcnn_47e	.877
85	deit_large_imagenet_full_seed-0	.877
86	efficientnet_b0_imagenet_full	.877
87	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.877
88	resnext101_32x8d_wsl	.877
89	mobilenet_v2_0_75_224	.862
90	inception_v3	.862
91	voneresnet-50-robust	.862
92	resnet-18_test_m	.862
93	blt_vs	.862
94	convnext_small_imagenet_full_seed-0	.862
95	resnet50_imagenet_full	.862
96	ReAlnet04_ALL	.794
97	resnet_50_v1_spiking_l3	.794
98	ReAlnet02_cornet	.794
99	nasnet_large	.794
100	grcnn	.794
101	ReAlnet02	.794
102	resnet50-vicreg	.794
103	resnet34_imagenet_full	.794
104	effnetb1_272x240	.794
105	mobilenet_v2_0_5_224	.780
106	densenet-201	.780
107	compact_resnet50_V4	.780
108	resnet50_imagenet_1_seed-0	.780
109	alexnet_fov16	.719
110	ReAlnet03_ALL	.719
111	alexnet_workflow_trigger	.719
112	resnet_152_v1	.719
113	convnext_tiny_imagenet_full_seed-0	.719
114	deit_small_imagenet_full_seed-0	.719
115	alexnet	.719
116	resnext101_32x48d_wsl	.719
117	resnext101_32x32d_wsl	.719
118	resnet_101_v2	.706
119	yudixie_resnet50_distance_rotation_0_240908	.706
120	convnext_femto_ols:d1_in1k	.706
121	convnext_small_imagenet_100_seed-0	.706
122	alexnet_training_seed_01_fov12	.650
123	yudixie_resnet50_random_0_240908	.650
124	resnet50_robust_l2_eps1	.650
125	vonegrcnn_52e_full	.650
126	compact_resnet50_robust_IT	.650
127	resnet152_imagenet_full	.650
128	ReAlnet09_ALL	.640
129	ReAlnet05_cornet	.640
130	mobilenet_v2_1_0_128	.640
131	ReAlnet05	.640
132	antialiased-r50	.640
133	mobilenet_v2_1_3_224	.589
134	inception_v1	.589
135	Res2Net50_26w_4s	.589
136	convnext_tiny:in12k_ft_in1k	.589
137	yudixie_resnet18_category_class_0_240719	.589
138	resnet50_ewconplus_wzc	.579
139	resnet_50_v1_spiking_l4a	.579
140	yudixie_resnet50_imagenet1kpret_0_240908	.579
141	resnet_50_v2	.579
142	yudixie_resnet18_imagenet1kpret_0_240719	.579
143	compact_vgg19_V4	.579
144	resnet34_ecoset_full	.579
145	ReAlnet01_cornet_fov16	.533
146	ReAlnet08_ALL	.533
147	pnasnet_large_pytorch	.533
148	yudixie_resnet50_distance_translation_rotation_0_240908	.524
149	ReAlnet10_ALL	.483
150	yudixie_resnet50_distance_reg_0_240908	.483
151	ReAlnet08_cornet	.483
152	ReAlnet08	.483
153	deit_small_imagenet_100_seed-0	.483
154	resnet101_imagenet_full	.483
155	ReAlnet05_ALL	.474
156	mobilenet_v2_0_5_192	.474
157	AlexNet_SIN_fov12	.437
158	xception	.437
159	ReAlnet03_cornet	.437
160	ReAlnet03	.437
161	resnet50-moclr8deg	.437
162	resnet50-vicregl0p75	.437
163	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.430
164	mobilenet_v2_1_4_224	.430
165	nasnet_mobile	.430
166	yudixie_resnet50_distance_translation_0_240908	.430
167	mobilenet_v2_1-4_224_pytorch	.430
168	alexnet_fov12	.396
169	vgg_16	.396
170	shufflenet_v2_x1_0	.396
171	deit_small_imagenet_10_seed-0	.396
172	resnet18_imagenet_full	.396
173	yudixie_resnet50_object_class_0_240908	.389
174	yudixie_resnet50_translation_rotation_0_240908	.389
175	compact_resnet50_robust_V4	.389
176	resnet18_ecoset_full	.389
177	mobilenet_v2_1_0_224	.358
178	alexnet_training_seed_04	.358
179	ReAlnet07_cornet	.358
180	ReAlnet04_cornet	.358
181	ReAlnet07	.358
182	ReAlnet04	.358
183	yudixie_resnet18_object_class_0_240719	.358
184	yudixie_resnet18_distance_translation_rotation_0_240719	.358
185	resnet_50_v1_spiking	.352
186	resnet_101_v1	.352
187	resnet50-SIN_IN	.352
188	AlexNet_SIN_fov16	.324
189	alexnet_training_seed_06	.324
190	resnet50_ecoset_full	.324
191	alexnet_fov4	.319
192	yudixie_resnet50_rotation_reg_0_240908	.319
193	convnext_small_imagenet_10_seed-0	.319
194	compact_vgg19_IT	.319
195	ReAlnet01_cornet_fov12	.293
196	ReAlnet07_ALL	.293
197	ReAlnet02_ALL	.293
198	ReAlnet06_cornet	.293
199	ReAlnet06	.293
200	resnet50-SIN	.293
201	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.293
202	resnet_50_v1_spiking_l4	.266
203	yudixie_resnet50_translation_reg_0_240908	.266
204	resnet50-barlow	.266
205	densenet-121	.266
206	yudixie_resnet18_rotation_reg_0_240719	.266
207	ReAlnet01_cornet_fov4	.261
208	mobilenet_v2_0_75_192	.261
209	VOneCORnet-S	.261
210	yudixie_resnet18_distance_translation_0_240719	.261
211	resnet50_tutorial	.261
212	compact_resnet50_IT	.240
213	resnet50_imagenet_10_seed-0	.240
214	yudixie_resnet18_distance_reg_0_240719	.236
215	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.236
216	resnet152_ecoset_full	.236
217	ReAlnet01_cornet	.218
218	ReAlnet01	.218
219	resnet101_ecoset_full	.218
220	resnet-50-robust	.218
221	yudixie_resnet50_category_class_0_240908	.214
222	alexnet_training_seed_01_fov4	.197
223	alexnet_training_seed_07	.178
224	vit_base_patch14_dinov2:lvd142m	.175
225	mobilenet_v2_1_0_160	.175
226	convnext_small_imagenet_1_seed-0	.175
227	vit_base_patch16_dinov3:lvd1689m	.159
228	resnet50_primary_visual_cortex	.159
229	deit_small_imagenet_1_seed-0	.159
230	yudixie_resnet18_translation_reg_0_240719	.159
231	alexnet_training_seed_01	.146
232	alexnet_training_seed_05	.146
233	yudixie_resnet18_random_0_240719	.146
234	vit_base_patch14_reg4_dinov2:lvd142m	.144
235	alexnet_training_seed_08	.098
236	vgg_19	.098
237	compact_cornets_IT	.098
238	alexnet_training_seed_02	.089
239	artResNet18_1	.089
240	compact_cornets_V4	.087
241	alexnet_training_seed_09	.066
242	briaai_rmbg_1_4	.060
243	alexnet_training_seed_01_fov16	.054
244	AlexNet_SIN_fov4	.039
245	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.039
246	alexnet_training_seed_03	.022
247	alexnet_training_seed_10
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

0.90.

Note that scores are relative to this ceiling.

Data: Ferguson2024color

Metric: value_delta