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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	deit3_base_patch16_224_fb_in1k	1.0
2	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	1.0
3	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	1.0
4	convnext_base_fb_in1k	1.0
5	resnet18_fair	1.0
6	resnet50_st_wzc_seed7_2_323	1.0
7	resnet50_ewconplus_wzc_seed3_2_323	1.0
8	vit_base_patch16_dinov3-lvd1689m	1.0
9	vit_base_patch14_reg4_dinov2-lvd142m	1.0
10	vit_base_patch14_dinov2-lvd142m	1.0
11	cv_18_dagger_408_pretrained	1.0
12	AlexNet_SIN	1.0
13	ReAlnet06_ALL	1.0
14	efficientnet-b7	1.0
15	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
16	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
17	vit_large_patch14_clip_224:openai_ft_in1k	1.0
18	vit_large_patch14_clip_224:openai_ft_in12k_in1k	1.0
19	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
20	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
21	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
22	omnivore_swinS	1.0
23	efficientnet_b0	1.0
24	omnivore_swinB	1.0
25	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	1.0
26	vit_large_patch14_clip_336:openai_ft_in12k_in1k	1.0
27	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
28	vit_base_patch16_clip_224:openai_ft_in1k	1.0
29	vit_base_patch16_clip_224:openai_ft_in12k_in1k	1.0
30	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	1.0
31	swin_small_patch4_window7_224:ms_in22k_ft_in1k	1.0
32	convnext_xxlarge:clip_laion2b_soup_ft_in1k	1.0
33	convnext_xlarge:fb_in22k_ft_in1k	1.0
34	convnext_large:fb_in22k_ft_in1k	1.0
35	convnext_base:clip_laiona_augreg_ft_in1k_384	1.0
36	AT_efficientnet-b2	1.0
37	resnet50-vicregl0p9	1.0
38	BiT-S-R101x1	1.0
39	BiT-S-R50x1	1.0
40	BiT-S-R152x2	1.0
41	BiT-S-R101x3	1.0
42	BiT-S-R152x4	1.0
43	convnext_tiny_sup	1.0
44	AdvProp_efficientnet-b8	1.0
45	BiT-S-R50x3	1.0
46	resnet50_robust_l2_eps3	1.0
47	imagenet_l2_3_0	1.0
48	AdvProp_efficientnet-b2	1.0
49	AdvProp_efficientnet-b4	1.0
50	AdvProp_efficientnet-b7	1.0
51	AdvProp_efficientnet-b6	1.0
52	convnext_large_imagenet_full_seed-0	1.0
53	convnext_base_imagenet_full_seed-0	1.0
54	yudixie_resnet18_translation_rotation_0_240719	1.0
55	deit_base_imagenet_full_seed-0	1.0
56	efficientnet_b2_imagenet_full	1.0
57	efficientnet_b1_imagenet_full	1.0
58	cvt_cvt-21-384-in1k_4_LucyV4	1.0
59	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	1.0
60	cvt_cvt-13-384-in1k_4_LucyV4	1.0
61	cvt_cvt-21-224-in1k_4_LucyV4	1.0
62	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	1.0
63	cvt_cvt-13-224-in1k_4_LucyV4	1.0
64	cvt_cvt-w24-384-in22k_finetuned-in1k_4	1.0
65	ViT_L_32_imagenet1k	1.0
66	custom_model_cv_18_dagger_408	1.0
67	focalnet_tiny_lrf_in1k	1.0
68	resnet50_ewconplus_wzc_2_323	1.0
69	resnet-152_v2_pytorch	1.0
70	predictive_coding_pc	.968
71	resnet_50_v1	.968
72	voneresnet-50	.968
73	resnet50-vitoimagevidnet8	.968
74	resnet50-VITO-8deg-cc	.968
75	densenet-169	.968
76	resnet50-sup	.968
77	resnet50_imagenet_100_seed-0	.968
78	pnasnet_large_halfPrecision	.952
79	yolos_tiny	.952
80	pnasnet_large	.952
81	mobilenet_v2_1_0_192	.952
82	resnet_152_v2	.952
83	inception_v4	.952
84	mobilenet_v2_0_75_160	.952
85	yudixie_resnet18_distance_rotation_0_240719	.952
86	voneresnet-50-non_stochastic	.952
87	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.877
88	convnext_base_fb_in1k_gram_aligned_dinov3	.877
89	convnext_base_fb_in22k_ft_in1k	.877
90	ReAlnet01_ALL	.877
91	ReAlnet10_cornet	.877
92	ReAlnet09_cornet	.877
93	grcnn_109	.877
94	vonegrcnn_62e_nobn	.877
95	ReAlnet10	.877
96	ReAlnet09	.877
97	resnet50-SIN_IN_IN	.877
98	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.877
99	resnet_SIN_IN_FT_IN	.877
100	vonegrcnn_47e	.877
101	deit_large_imagenet_full_seed-0	.877
102	efficientnet_b0_imagenet_full	.877
103	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.877
104	resnext101_32x8d_wsl	.877
105	deepeisnn_ei_imagenet_resnet18	.862
106	mobilenet_v2_0_75_224	.862
107	inception_v3	.862
108	voneresnet-50-robust	.862
109	resnet-18_test_m	.862
110	blt_vs	.862
111	convnext_small_imagenet_full_seed-0	.862
112	resnet50_imagenet_full	.862
113	yudixie_wbh_object_class_scaling_0415_imagenet	.794
114	yudixie_wbh_object_class_scaling_0415_run_0	.794
115	resnet50_ewconplus_wzc_seed5_2__323	.794
116	resnet50_ewconplus_wzc_seed2_2__323	.794
117	resnet50_ewconplus_noseed_2_323	.794
118	ReAlnet04_ALL	.794
119	resnet_50_v1_spiking_l3	.794
120	ReAlnet02_cornet	.794
121	nasnet_large	.794
122	grcnn	.794
123	ReAlnet02	.794
124	resnet50-vicreg	.794
125	resnet34_imagenet_full	.794
126	effnetb1_272x240	.794
127	resnet50_st_wzc_seed4_2_323	.780
128	resnet50_ewconplus_wzc_seed6_2_323	.780
129	resnet50_st_wzc_seed6_2_323	.780
130	mobilenet_v2_0_5_224	.780
131	densenet-201	.780
132	compact_resnet50_V4	.780
133	resnet50_imagenet_1_seed-0	.780
134	yudixie_wbh_object_class_scaling_0415_run_2	.719
135	alexnet_fov16	.719
136	ReAlnet03_ALL	.719
137	resnet_152_v1	.719
138	convnext_tiny_imagenet_full_seed-0	.719
139	deit_small_imagenet_full_seed-0	.719
140	alexnet	.719
141	resnext101_32x48d_wsl	.719
142	resnext101_32x32d_wsl	.719
143	resnet_101_v2	.706
144	yudixie_resnet50_distance_rotation_0_240908	.706
145	convnext_femto_ols:d1_in1k	.706
146	convnext_small_imagenet_100_seed-0	.706
147	resnet50_st_wzc_seed3_2_323	.650
148	resnet50_st_wzc_seed5_2_323	.650
149	resnet50_ewconplus_wzc_seed4_2_323	.650
150	alexnet_training_seed_01_fov12	.650
151	yudixie_resnet50_random_0_240908	.650
152	resnet50_robust_l2_eps1	.650
153	vonegrcnn_52e_full	.650
154	compact_resnet50_robust_IT	.650
155	resnet152_imagenet_full	.650
156	resnet50_st_wzc_2_323	.640
157	ReAlnet09_ALL	.640
158	ReAlnet05_cornet	.640
159	mobilenet_v2_1_0_128	.640
160	ReAlnet05	.640
161	antialiased-r50	.640
162	resnet50_ewconplus_wzc_seed7_2_323	.589
163	mobilenet_v2_1_3_224	.589
164	inception_v1	.589
165	Res2Net50_26w_4s	.589
166	convnext_tiny:in12k_ft_in1k	.589
167	yudixie_resnet18_category_class_0_240719	.589
168	resnet50_ewconplus_wzc	.579
169	resnet_50_v1_spiking_l4a	.579
170	yudixie_resnet50_imagenet1kpret_0_240908	.579
171	resnet_50_v2	.579
172	yudixie_resnet18_imagenet1kpret_0_240719	.579
173	compact_vgg19_V4	.579
174	resnet34_ecoset_full	.579
175	ReAlnet01_cornet_fov16	.533
176	ReAlnet08_ALL	.533
177	pnasnet_large_pytorch	.533
178	resnet50_st_wzc_2	.524
179	yudixie_resnet50_distance_translation_rotation_0_240908	.524
180	ReAlnet10_ALL	.483
181	yudixie_resnet50_distance_reg_0_240908	.483
182	ReAlnet08_cornet	.483
183	ReAlnet08	.483
184	deit_small_imagenet_100_seed-0	.483
185	resnet101_imagenet_full	.483
186	ReAlnet05_ALL	.474
187	mobilenet_v2_0_5_192	.474
188	resnet50_st_wzc_noseed_2_323	.437
189	AlexNet_SIN_fov12	.437
190	xception	.437
191	ReAlnet03_cornet	.437
192	ReAlnet03	.437
193	resnet50-moclr8deg	.437
194	resnet50-vicregl0p75	.437
195	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.430
196	mobilenet_v2_1_4_224	.430
197	nasnet_mobile	.430
198	yudixie_resnet50_distance_translation_0_240908	.430
199	mobilenet_v2_1-4_224_pytorch	.430
200	aughost_fair_resnet18	.396
201	alexnet_fov12	.396
202	vgg_16	.396
203	shufflenet_v2_x1_0	.396
204	deit_small_imagenet_10_seed-0	.396
205	resnet18_imagenet_full	.396
206	yudixie_resnet50_object_class_0_240908	.389
207	yudixie_resnet50_translation_rotation_0_240908	.389
208	compact_resnet50_robust_V4	.389
209	resnet18_ecoset_full	.389
210	deepeisnn_ei_cifar10_resnet18	.358
211	mobilenet_v2_1_0_224	.358
212	alexnet_training_seed_04	.358
213	ReAlnet07_cornet	.358
214	ReAlnet04_cornet	.358
215	ReAlnet07	.358
216	ReAlnet04	.358
217	yudixie_resnet18_object_class_0_240719	.358
218	yudixie_resnet18_distance_translation_rotation_0_240719	.358
219	resnet_50_v1_spiking	.352
220	resnet_101_v1	.352
221	resnet50-SIN_IN	.352
222	AlexNet_SIN_fov16	.324
223	alexnet_training_seed_06	.324
224	resnet50_ecoset_full	.324
225	alexnet_fov4	.319
226	yudixie_resnet50_rotation_reg_0_240908	.319
227	convnext_small_imagenet_10_seed-0	.319
228	compact_vgg19_IT	.319
229	ReAlnet01_cornet_fov12	.293
230	ReAlnet07_ALL	.293
231	ReAlnet02_ALL	.293
232	ReAlnet06_cornet	.293
233	ReAlnet06	.293
234	resnet50-SIN	.293
235	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.293
236	resnet_50_v1_spiking_l4	.266
237	yudixie_resnet50_translation_reg_0_240908	.266
238	resnet50-barlow	.266
239	densenet-121	.266
240	yudixie_resnet18_rotation_reg_0_240719	.266
241	ReAlnet01_cornet_fov4	.261
242	mobilenet_v2_0_75_192	.261
243	VOneCORnet-S	.261
244	yudixie_resnet18_distance_translation_0_240719	.261
245	resnet50_tutorial	.261
246	pr1_stl10_disentangled_alpha04	.240
247	compact_resnet50_IT	.240
248	resnet50_imagenet_10_seed-0	.240
249	yudixie_resnet18_distance_reg_0_240719	.236
250	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.236
251	resnet152_ecoset_full	.236
252	yudixie_wbh_object_class_scaling_0415_run_3	.218
253	ReAlnet01_cornet	.218
254	ReAlnet01	.218
255	resnet101_ecoset_full	.218
256	resnet-50-robust	.218
257	yudixie_resnet50_category_class_0_240908	.214
258	alexnet_training_seed_01_fov4	.197
259	yudixie_wbh_object_class_scaling_0415_run_1	.178
260	alexnet_training_seed_07	.178
261	mobilenet_v2_1_0_160	.175
262	convnext_small_imagenet_1_seed-0	.175
263	resnet50_primary_visual_cortex	.159
264	deit_small_imagenet_1_seed-0	.159
265	yudixie_resnet18_translation_reg_0_240719	.159
266	alexnet_training_seed_01	.146
267	alexnet_training_seed_05	.146
268	yudixie_resnet18_random_0_240719	.146
269	deepeisnn_ei_cifar10_vgg19	.132
270	alexnet_training_seed_08	.098
271	vgg_19	.098
272	compact_cornets_IT	.098
273	alexnet_training_seed_02	.089
274	artResNet18_1	.089
275	compact_cornets_V4	.087
276	alexnet_training_seed_09	.066
277	briaai_rmbg_1_4	.060
278	alexnet_training_seed_01_fov16	.054
279	AlexNet_SIN_fov4	.039
280	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.039
281	alexnet_training_seed_03	.022
282	alexnet_training_seed_10
283	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