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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	seresnext50_32x4d	1.0
2	regnety_032	1.0
3	vit_huge_clip336_laion	1.0
4	convnext_xxlarge_clip	1.0
5	vit_large_clip336_openai	1.0
6	dinov2_vitl14_reg_lc_featread	1.0
7	dinov2_vitl14_lc_res336	1.0
8	dinov2_vitl14_reg_lc	1.0
9	eva02_large_in22k	1.0
10	dinov2_vitg14_reg_lc	1.0
11	dinov2_vitg14_lc	1.0
12	dinov2_vitl14_lc	1.0
13	convnext_large_clip_laion2b_finelayers	1.0
14	deit3_base_patch16_224_fb_in1k	1.0
15	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	1.0
16	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	1.0
17	convnext_base_fb_in1k	1.0
18	resnet18_fair	1.0
19	resnet50_st_wzc_seed7_2_323	1.0
20	vit_base_patch16_dinov3-lvd1689m	1.0
21	vit_base_patch14_reg4_dinov2-lvd142m	1.0
22	vit_base_patch14_dinov2-lvd142m	1.0
23	cv_18_dagger_408_pretrained	1.0
24	AlexNet_SIN	1.0
25	ReAlnet06_ALL	1.0
26	efficientnet-b7	1.0
27	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
28	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
29	vit_large_patch14_clip_224:openai_ft_in1k	1.0
30	vit_large_patch14_clip_224:openai_ft_in12k_in1k	1.0
31	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
32	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
33	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
34	omnivore_swinS	1.0
35	efficientnet_b0	1.0
36	omnivore_swinB	1.0
37	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	1.0
38	vit_large_patch14_clip_336:openai_ft_in12k_in1k	1.0
39	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
40	vit_base_patch16_clip_224:openai_ft_in1k	1.0
41	vit_base_patch16_clip_224:openai_ft_in12k_in1k	1.0
42	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	1.0
43	swin_small_patch4_window7_224:ms_in22k_ft_in1k	1.0
44	convnext_xxlarge:clip_laion2b_soup_ft_in1k	1.0
45	convnext_xlarge:fb_in22k_ft_in1k	1.0
46	convnext_large:fb_in22k_ft_in1k	1.0
47	convnext_base:clip_laiona_augreg_ft_in1k_384	1.0
48	AT_efficientnet-b2	1.0
49	resnet50-vicregl0p9	1.0
50	BiT-S-R101x1	1.0
51	BiT-S-R50x1	1.0
52	BiT-S-R152x2	1.0
53	BiT-S-R101x3	1.0
54	BiT-S-R152x4	1.0
55	convnext_tiny_sup	1.0
56	AdvProp_efficientnet-b8	1.0
57	BiT-S-R50x3	1.0
58	resnet50_robust_l2_eps3	1.0
59	imagenet_l2_3_0	1.0
60	AdvProp_efficientnet-b2	1.0
61	AdvProp_efficientnet-b4	1.0
62	AdvProp_efficientnet-b7	1.0
63	AdvProp_efficientnet-b6	1.0
64	convnext_large_imagenet_full_seed-0	1.0
65	convnext_base_imagenet_full_seed-0	1.0
66	yudixie_resnet18_translation_rotation_0_240719	1.0
67	deit_base_imagenet_full_seed-0	1.0
68	efficientnet_b2_imagenet_full	1.0
69	efficientnet_b1_imagenet_full	1.0
70	cvt_cvt-21-384-in1k_4_LucyV4	1.0
71	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	1.0
72	cvt_cvt-13-384-in1k_4_LucyV4	1.0
73	cvt_cvt-21-224-in1k_4_LucyV4	1.0
74	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	1.0
75	cvt_cvt-13-224-in1k_4_LucyV4	1.0
76	cvt_cvt-w24-384-in22k_finetuned-in1k_4	1.0
77	ViT_L_32_imagenet1k	1.0
78	custom_model_cv_18_dagger_408	1.0
79	focalnet_tiny_lrf_in1k	1.0
80	resnet-152_v2_pytorch	1.0
81	predictive_coding_pc	.968
82	resnet_50_v1	.968
83	voneresnet-50	.968
84	resnet50-vitoimagevidnet8	.968
85	resnet50-VITO-8deg-cc	.968
86	densenet-169	.968
87	resnet50-sup	.968
88	resnet50_imagenet_100_seed-0	.968
89	pnasnet_large_halfPrecision	.952
90	resnet50_hvs_wzc	.952
91	yolos_tiny	.952
92	pnasnet_large	.952
93	mobilenet_v2_1_0_192	.952
94	resnet_152_v2	.952
95	inception_v4	.952
96	mobilenet_v2_0_75_160	.952
97	yudixie_resnet18_distance_rotation_0_240719	.952
98	voneresnet-50-non_stochastic	.952
99	gabor_filter_mean_abs_4x4_v2	.877
100	convnext_large_in22k_supervised	.877
101	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.877
102	convnext_base_fb_in1k_gram_aligned_dinov3	.877
103	convnext_base_fb_in22k_ft_in1k	.877
104	ReAlnet01_ALL	.877
105	ReAlnet10_cornet	.877
106	ReAlnet09_cornet	.877
107	grcnn_109	.877
108	vonegrcnn_62e_nobn	.877
109	ReAlnet10	.877
110	ReAlnet09	.877
111	resnet50-SIN_IN_IN	.877
112	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.877
113	resnet_SIN_IN_FT_IN	.877
114	vonegrcnn_47e	.877
115	deit_large_imagenet_full_seed-0	.877
116	efficientnet_b0_imagenet_full	.877
117	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.877
118	resnext101_32x8d_wsl	.877
119	deepeisnn_ei_imagenet_resnet18	.862
120	mobilenet_v2_0_75_224	.862
121	inception_v3	.862
122	voneresnet-50-robust	.862
123	resnet-18_test_m	.862
124	blt_vs	.862
125	convnext_small_imagenet_full_seed-0	.862
126	resnet50_imagenet_full	.862
127	eicircuit_resnet18_bn_20260919	.794
128	yudixie_wbh_object_class_scaling_0415_imagenet	.794
129	yudixie_wbh_object_class_scaling_0415_run_0	.794
130	resnet50_ewconplus_wzc_seed5_2__323	.794
131	resnet50_ewconplus_wzc_seed2_2__323	.794
132	ReAlnet04_ALL	.794
133	resnet_50_v1_spiking_l3	.794
134	ReAlnet02_cornet	.794
135	nasnet_large	.794
136	grcnn	.794
137	ReAlnet02	.794
138	resnet50-vicreg	.794
139	resnet34_imagenet_full	.794
140	effnetb1_272x240	.794
141	resnet50_ewconplus_wzc_seed6_2_323	.780
142	resnet50_st_wzc_seed6_2_323	.780
143	mobilenet_v2_0_5_224	.780
144	densenet-201	.780
145	compact_resnet50_V4	.780
146	resnet50_imagenet_1_seed-0	.780
147	yudixie_wbh_object_class_scaling_0415_run_2	.719
148	alexnet_fov16	.719
149	ReAlnet03_ALL	.719
150	resnet_152_v1	.719
151	convnext_tiny_imagenet_full_seed-0	.719
152	deit_small_imagenet_full_seed-0	.719
153	alexnet	.719
154	resnext101_32x48d_wsl	.719
155	resnext101_32x32d_wsl	.719
156	resnet_101_v2	.706
157	yudixie_resnet50_distance_rotation_0_240908	.706
158	convnext_femto_ols:d1_in1k	.706
159	convnext_small_imagenet_100_seed-0	.706
160	resnet50_st_wzc_seed3_2_323	.650
161	alexnet_training_seed_01_fov12	.650
162	yudixie_resnet50_random_0_240908	.650
163	resnet50_robust_l2_eps1	.650
164	vonegrcnn_52e_full	.650
165	compact_resnet50_robust_IT	.650
166	resnet152_imagenet_full	.650
167	ReAlnet09_ALL	.640
168	ReAlnet05_cornet	.640
169	mobilenet_v2_1_0_128	.640
170	ReAlnet05	.640
171	antialiased-r50	.640
172	mobilenet_v2_1_3_224	.589
173	inception_v1	.589
174	Res2Net50_26w_4s	.589
175	convnext_tiny:in12k_ft_in1k	.589
176	yudixie_resnet18_category_class_0_240719	.589
177	resnet50_ewconplus_wzc	.579
178	resnet_50_v1_spiking_l4a	.579
179	yudixie_resnet50_imagenet1kpret_0_240908	.579
180	resnet_50_v2	.579
181	yudixie_resnet18_imagenet1kpret_0_240719	.579
182	compact_vgg19_V4	.579
183	resnet34_ecoset_full	.579
184	ReAlnet01_cornet_fov16	.533
185	ReAlnet08_ALL	.533
186	pnasnet_large_pytorch	.533
187	resnet50_st_wzc_2	.524
188	yudixie_resnet50_distance_translation_rotation_0_240908	.524
189	ReAlnet10_ALL	.483
190	yudixie_resnet50_distance_reg_0_240908	.483
191	ReAlnet08_cornet	.483
192	ReAlnet08	.483
193	deit_small_imagenet_100_seed-0	.483
194	resnet101_imagenet_full	.483
195	ReAlnet05_ALL	.474
196	mobilenet_v2_0_5_192	.474
197	AlexNet_SIN_fov12	.437
198	xception	.437
199	ReAlnet03_cornet	.437
200	ReAlnet03	.437
201	resnet50-moclr8deg	.437
202	resnet50-vicregl0p75	.437
203	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.430
204	mobilenet_v2_1_4_224	.430
205	nasnet_mobile	.430
206	yudixie_resnet50_distance_translation_0_240908	.430
207	mobilenet_v2_1-4_224_pytorch	.430
208	alexnet_fov12	.396
209	vgg_16	.396
210	shufflenet_v2_x1_0	.396
211	deit_small_imagenet_10_seed-0	.396
212	resnet18_imagenet_full	.396
213	yudixie_resnet50_object_class_0_240908	.389
214	yudixie_resnet50_translation_rotation_0_240908	.389
215	compact_resnet50_robust_V4	.389
216	resnet18_ecoset_full	.389
217	deepeisnn_ei_cifar10_resnet18	.358
218	mobilenet_v2_1_0_224	.358
219	alexnet_training_seed_04	.358
220	ReAlnet07_cornet	.358
221	ReAlnet04_cornet	.358
222	ReAlnet07	.358
223	ReAlnet04	.358
224	yudixie_resnet18_object_class_0_240719	.358
225	yudixie_resnet18_distance_translation_rotation_0_240719	.358
226	resnet_50_v1_spiking	.352
227	resnet_101_v1	.352
228	resnet50-SIN_IN	.352
229	AlexNet_SIN_fov16	.324
230	alexnet_training_seed_06	.324
231	resnet50_ecoset_full	.324
232	alexnet_fov4	.319
233	yudixie_resnet50_rotation_reg_0_240908	.319
234	convnext_small_imagenet_10_seed-0	.319
235	compact_vgg19_IT	.319
236	ReAlnet01_cornet_fov12	.293
237	ReAlnet07_ALL	.293
238	ReAlnet02_ALL	.293
239	ReAlnet06_cornet	.293
240	ReAlnet06	.293
241	resnet50-SIN	.293
242	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.293
243	resnet_50_v1_spiking_l4	.266
244	yudixie_resnet50_translation_reg_0_240908	.266
245	resnet50-barlow	.266
246	densenet-121	.266
247	yudixie_resnet18_rotation_reg_0_240719	.266
248	ReAlnet01_cornet_fov4	.261
249	mobilenet_v2_0_75_192	.261
250	VOneCORnet-S	.261
251	yudixie_resnet18_distance_translation_0_240719	.261
252	resnet50_tutorial	.261
253	pr1_stl10_disentangled_alpha04	.240
254	compact_resnet50_IT	.240
255	resnet50_imagenet_10_seed-0	.240
256	yudixie_resnet18_distance_reg_0_240719	.236
257	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.236
258	resnet152_ecoset_full	.236
259	yudixie_wbh_object_class_scaling_0415_run_3	.218
260	ReAlnet01_cornet	.218
261	ReAlnet01	.218
262	resnet101_ecoset_full	.218
263	resnet-50-robust	.218
264	yudixie_resnet50_category_class_0_240908	.214
265	alexnet_training_seed_01_fov4	.197
266	yudixie_wbh_object_class_scaling_0415_run_1	.178
267	alexnet_training_seed_07	.178
268	mobilenet_v2_1_0_160	.175
269	convnext_small_imagenet_1_seed-0	.175
270	resnet50_primary_visual_cortex	.159
271	deit_small_imagenet_1_seed-0	.159
272	yudixie_resnet18_translation_reg_0_240719	.159
273	alexnet_training_seed_01	.146
274	alexnet_training_seed_05	.146
275	yudixie_resnet18_random_0_240719	.146
276	deepeisnn_ei_cifar10_vgg19	.132
277	alexnet_training_seed_08	.098
278	vgg_19	.098
279	compact_cornets_IT	.098
280	gabor_filter_entropy_4x4_v2_forcedCosine	.089
281	gabor_filter_entropy_4x4_v2	.089
282	alexnet_training_seed_02	.089
283	artResNet18_1	.089
284	compact_cornets_V4	.087
285	alexnet_training_seed_09	.066
286	briaai_rmbg_1_4	.060
287	gabor_filter_entropy_4x4	.054
288	alexnet_training_seed_01_fov16	.054
289	AlexNet_SIN_fov4	.039
290	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.039
291	alexnet_training_seed_03	.022
292	alexnet_training_seed_10
293	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