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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	vit_base_patch16_dinov3-lvd1689m	1.0
2	vit_base_patch14_reg4_dinov2-lvd142m	1.0
3	vit_base_patch14_dinov2-lvd142m	1.0
4	cv_18_dagger_408_pretrained	1.0
5	AlexNet_SIN	1.0
6	ReAlnet06_ALL	1.0
7	efficientnet-b7	1.0
8	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
9	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
10	vit_large_patch14_clip_224:openai_ft_in1k	1.0
11	vit_large_patch14_clip_224:openai_ft_in12k_in1k	1.0
12	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
13	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
14	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
15	omnivore_swinS	1.0
16	efficientnet_b0	1.0
17	omnivore_swinB	1.0
18	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	1.0
19	vit_large_patch14_clip_336:openai_ft_in12k_in1k	1.0
20	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
21	vit_base_patch16_clip_224:openai_ft_in1k	1.0
22	vit_base_patch16_clip_224:openai_ft_in12k_in1k	1.0
23	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	1.0
24	swin_small_patch4_window7_224:ms_in22k_ft_in1k	1.0
25	convnext_xxlarge:clip_laion2b_soup_ft_in1k	1.0
26	convnext_xlarge:fb_in22k_ft_in1k	1.0
27	convnext_large:fb_in22k_ft_in1k	1.0
28	convnext_base:clip_laiona_augreg_ft_in1k_384	1.0
29	AT_efficientnet-b2	1.0
30	resnet50-vicregl0p9	1.0
31	BiT-S-R101x1	1.0
32	BiT-S-R50x1	1.0
33	BiT-S-R152x2	1.0
34	BiT-S-R101x3	1.0
35	BiT-S-R152x4	1.0
36	convnext_tiny_sup	1.0
37	AdvProp_efficientnet-b8	1.0
38	BiT-S-R50x3	1.0
39	resnet50_robust_l2_eps3	1.0
40	imagenet_l2_3_0	1.0
41	AdvProp_efficientnet-b2	1.0
42	AdvProp_efficientnet-b4	1.0
43	AdvProp_efficientnet-b7	1.0
44	AdvProp_efficientnet-b6	1.0
45	convnext_large_imagenet_full_seed-0	1.0
46	convnext_base_imagenet_full_seed-0	1.0
47	yudixie_resnet18_translation_rotation_0_240719	1.0
48	deit_base_imagenet_full_seed-0	1.0
49	efficientnet_b2_imagenet_full	1.0
50	efficientnet_b1_imagenet_full	1.0
51	cvt_cvt-21-384-in1k_4_LucyV4	1.0
52	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	1.0
53	cvt_cvt-13-384-in1k_4_LucyV4	1.0
54	cvt_cvt-21-224-in1k_4_LucyV4	1.0
55	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	1.0
56	cvt_cvt-13-224-in1k_4_LucyV4	1.0
57	cvt_cvt-w24-384-in22k_finetuned-in1k_4	1.0
58	ViT_L_32_imagenet1k	1.0
59	custom_model_cv_18_dagger_408	1.0
60	focalnet_tiny_lrf_in1k	1.0
61	resnet-152_v2_pytorch	1.0
62	resnet_50_v1	.968
63	voneresnet-50	.968
64	resnet50-vitoimagevidnet8	.968
65	resnet50-VITO-8deg-cc	.968
66	densenet-169	.968
67	resnet50-sup	.968
68	resnet50_imagenet_100_seed-0	.968
69	pnasnet_large_halfPrecision	.952
70	yolos_tiny	.952
71	pnasnet_large	.952
72	mobilenet_v2_1_0_192	.952
73	resnet_152_v2	.952
74	inception_v4	.952
75	mobilenet_v2_0_75_160	.952
76	yudixie_resnet18_distance_rotation_0_240719	.952
77	voneresnet-50-non_stochastic	.952
78	ReAlnet01_ALL	.877
79	ReAlnet10_cornet	.877
80	ReAlnet09_cornet	.877
81	grcnn_109	.877
82	vonegrcnn_62e_nobn	.877
83	ReAlnet10	.877
84	ReAlnet09	.877
85	resnet50-SIN_IN_IN	.877
86	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.877
87	resnet_SIN_IN_FT_IN	.877
88	vonegrcnn_47e	.877
89	deit_large_imagenet_full_seed-0	.877
90	efficientnet_b0_imagenet_full	.877
91	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.877
92	resnext101_32x8d_wsl	.877
93	mobilenet_v2_0_75_224	.862
94	inception_v3	.862
95	voneresnet-50-robust	.862
96	resnet-18_test_m	.862
97	blt_vs	.862
98	convnext_small_imagenet_full_seed-0	.862
99	resnet50_imagenet_full	.862
100	ReAlnet04_ALL	.794
101	resnet_50_v1_spiking_l3	.794
102	ReAlnet02_cornet	.794
103	nasnet_large	.794
104	grcnn	.794
105	ReAlnet02	.794
106	resnet50-vicreg	.794
107	resnet34_imagenet_full	.794
108	effnetb1_272x240	.794
109	mobilenet_v2_0_5_224	.780
110	densenet-201	.780
111	compact_resnet50_V4	.780
112	resnet50_imagenet_1_seed-0	.780
113	alexnet_fov16	.719
114	ReAlnet03_ALL	.719
115	resnet_152_v1	.719
116	convnext_tiny_imagenet_full_seed-0	.719
117	deit_small_imagenet_full_seed-0	.719
118	alexnet	.719
119	resnext101_32x48d_wsl	.719
120	resnext101_32x32d_wsl	.719
121	resnet_101_v2	.706
122	yudixie_resnet50_distance_rotation_0_240908	.706
123	convnext_femto_ols:d1_in1k	.706
124	convnext_small_imagenet_100_seed-0	.706
125	alexnet_training_seed_01_fov12	.650
126	yudixie_resnet50_random_0_240908	.650
127	resnet50_robust_l2_eps1	.650
128	vonegrcnn_52e_full	.650
129	compact_resnet50_robust_IT	.650
130	resnet152_imagenet_full	.650
131	ReAlnet09_ALL	.640
132	ReAlnet05_cornet	.640
133	mobilenet_v2_1_0_128	.640
134	ReAlnet05	.640
135	antialiased-r50	.640
136	mobilenet_v2_1_3_224	.589
137	inception_v1	.589
138	Res2Net50_26w_4s	.589
139	convnext_tiny:in12k_ft_in1k	.589
140	yudixie_resnet18_category_class_0_240719	.589
141	resnet50_ewconplus_wzc	.579
142	resnet_50_v1_spiking_l4a	.579
143	yudixie_resnet50_imagenet1kpret_0_240908	.579
144	resnet_50_v2	.579
145	yudixie_resnet18_imagenet1kpret_0_240719	.579
146	compact_vgg19_V4	.579
147	resnet34_ecoset_full	.579
148	ReAlnet01_cornet_fov16	.533
149	ReAlnet08_ALL	.533
150	pnasnet_large_pytorch	.533
151	yudixie_resnet50_distance_translation_rotation_0_240908	.524
152	ReAlnet10_ALL	.483
153	yudixie_resnet50_distance_reg_0_240908	.483
154	ReAlnet08_cornet	.483
155	ReAlnet08	.483
156	deit_small_imagenet_100_seed-0	.483
157	resnet101_imagenet_full	.483
158	ReAlnet05_ALL	.474
159	mobilenet_v2_0_5_192	.474
160	AlexNet_SIN_fov12	.437
161	xception	.437
162	ReAlnet03_cornet	.437
163	ReAlnet03	.437
164	resnet50-moclr8deg	.437
165	resnet50-vicregl0p75	.437
166	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.430
167	mobilenet_v2_1_4_224	.430
168	nasnet_mobile	.430
169	yudixie_resnet50_distance_translation_0_240908	.430
170	mobilenet_v2_1-4_224_pytorch	.430
171	aughost_fair_resnet18	.396
172	alexnet_fov12	.396
173	vgg_16	.396
174	shufflenet_v2_x1_0	.396
175	deit_small_imagenet_10_seed-0	.396
176	resnet18_imagenet_full	.396
177	yudixie_resnet50_object_class_0_240908	.389
178	yudixie_resnet50_translation_rotation_0_240908	.389
179	compact_resnet50_robust_V4	.389
180	resnet18_ecoset_full	.389
181	mobilenet_v2_1_0_224	.358
182	alexnet_training_seed_04	.358
183	ReAlnet07_cornet	.358
184	ReAlnet04_cornet	.358
185	ReAlnet07	.358
186	ReAlnet04	.358
187	yudixie_resnet18_object_class_0_240719	.358
188	yudixie_resnet18_distance_translation_rotation_0_240719	.358
189	resnet_50_v1_spiking	.352
190	resnet_101_v1	.352
191	resnet50-SIN_IN	.352
192	AlexNet_SIN_fov16	.324
193	alexnet_training_seed_06	.324
194	resnet50_ecoset_full	.324
195	alexnet_fov4	.319
196	yudixie_resnet50_rotation_reg_0_240908	.319
197	convnext_small_imagenet_10_seed-0	.319
198	compact_vgg19_IT	.319
199	ReAlnet01_cornet_fov12	.293
200	ReAlnet07_ALL	.293
201	ReAlnet02_ALL	.293
202	ReAlnet06_cornet	.293
203	ReAlnet06	.293
204	resnet50-SIN	.293
205	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.293
206	resnet_50_v1_spiking_l4	.266
207	yudixie_resnet50_translation_reg_0_240908	.266
208	resnet50-barlow	.266
209	densenet-121	.266
210	yudixie_resnet18_rotation_reg_0_240719	.266
211	ReAlnet01_cornet_fov4	.261
212	mobilenet_v2_0_75_192	.261
213	VOneCORnet-S	.261
214	yudixie_resnet18_distance_translation_0_240719	.261
215	resnet50_tutorial	.261
216	compact_resnet50_IT	.240
217	resnet50_imagenet_10_seed-0	.240
218	yudixie_resnet18_distance_reg_0_240719	.236
219	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.236
220	resnet152_ecoset_full	.236
221	ReAlnet01_cornet	.218
222	ReAlnet01	.218
223	resnet101_ecoset_full	.218
224	resnet-50-robust	.218
225	yudixie_resnet50_category_class_0_240908	.214
226	alexnet_training_seed_01_fov4	.197
227	alexnet_training_seed_07	.178
228	mobilenet_v2_1_0_160	.175
229	convnext_small_imagenet_1_seed-0	.175
230	resnet50_primary_visual_cortex	.159
231	deit_small_imagenet_1_seed-0	.159
232	yudixie_resnet18_translation_reg_0_240719	.159
233	alexnet_training_seed_01	.146
234	alexnet_training_seed_05	.146
235	yudixie_resnet18_random_0_240719	.146
236	alexnet_training_seed_08	.098
237	vgg_19	.098
238	compact_cornets_IT	.098
239	alexnet_training_seed_02	.089
240	artResNet18_1	.089
241	compact_cornets_V4	.087
242	alexnet_training_seed_09	.066
243	briaai_rmbg_1_4	.060
244	alexnet_training_seed_01_fov16	.054
245	AlexNet_SIN_fov4	.039
246	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.039
247	alexnet_training_seed_03	.022
248	alexnet_training_seed_10
249	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