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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	dinov2_vitl14_lc_res336	1.0
5	dinov2_vitg14_lc	1.0
6	compact_vgg19_V4	1.0
7	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
8	yudixie_wbh_object_class_scaling_0415_run_3	1.0
9	densenet-121	1.0
10	compact_cornets_V4	.974
11	alexnet_training_seed_05	.974
12	yudixie_resnet50_translation_reg_0_240908	.974
13	resnext101_32x32d_wsl	.974
14	convnext_large:fb_in22k_ft_in1k	.951
15	dinov2_vitl14_lc	.882
16	alexnet_training_seed_01_fov16	.882
17	alexnet_training_seed_01	.882
18	mobilenet_v2_0_75_224	.882
19	omnivore_swinS	.882
20	vit_base_patch16_clip_224:openai_ft_in1k	.882
21	resnet_SIN_IN_FT_IN	.882
22	AdvProp_efficientnet-b4	.882
23	yudixie_resnet18_category_class_0_240719	.882
24	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.861
25	resnext101_32x8d_wsl	.799
26	regnety_032	.799
27	resnet50_ewconplus_wzc_seed2_2__323	.799
28	vit_base_patch14_dinov2-lvd142m	.799
29	eva02_large_in22k	.780
30	dinov2_vitg14_reg_lc	.780
31	vit_base_patch14_reg4_dinov2-lvd142m	.780
32	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.780
33	convnext_large_in22k_supervised	.723
34	cv_18_dagger_408_pretrained	.723
35	mobilenet_v2_1_0_192	.723
36	deit_small_imagenet_10_seed-0	.723
37	cvt_cvt-13-224-in1k_4_LucyV4	.723
38	convnext_xlarge:fb_in22k_ft_in1k	.706
39	mobilenet_v2_1_0_160	.655
40	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.655
41	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.655
42	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.655
43	BiT-S-R152x2	.655
44	focalnet_tiny_lrf_in1k	.655
45	resnet152_ecoset_full	.593
46	cvt_cvt-21-224-in1k_4_LucyV4	.593
47	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.579
48	vit_large_clip336_openai	.537
49	ReAlnet05_ALL	.537
50	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.537
51	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.537
52	omnivore_swinB	.524
53	resnet_50_v1_spiking	.486
54	nasnet_large	.486
55	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.486
56	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.486
57	yudixie_resnet18_distance_translation_rotation_0_240719	.486
58	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.486
59	effnetb1_272x240	.486
60	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.486
61	cvt_cvt-21-384-in1k_4_LucyV4	.475
62	resnet50_st_wzc_seed3_2_323	.440
63	compact_cornets_IT	.440
64	compact_resnet50_robust_V4	.440
65	custom_model_cv_18_dagger_408	.440
66	compact_resnet50_IT	.440
67	compact_resnet50_V4	.440
68	gabor_filter_entropy_4x4	.399
69	dinov2_vitl14_reg_lc_featread	.399
70	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.399
71	alexnet_training_seed_01_fov12	.399
72	AlexNet_SIN_fov4	.399
73	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.399
74	ReAlnet03_ALL	.399
75	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.399
76	densenet-169	.399
77	convnext_small_imagenet_1_seed-0	.399
78	yudixie_resnet18_imagenet1kpret_0_240719	.399
79	deepeisnn_ei_cifar10_vgg19	.361
80	resnet50_st_wzc_seed7_2_323	.361
81	yudixie_resnet50_distance_reg_0_240908	.361
82	vit_large_patch14_clip_224:openai_ft_in1k	.361
83	vit_large_patch14_clip_224:laion2b_ft_in1k	.361
84	AT_efficientnet-b2	.361
85	BiT-S-R50x3	.361
86	AdvProp_efficientnet-b2	.361
87	AdvProp_efficientnet-b6	.361
88	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.361
89	resnet18_ecoset_full	.361
90	resnext101_32x48d_wsl	.361
91	yudixie_wbh_object_class_scaling_0415_imagenet	.327
92	deit3_base_patch16_224_fb_in1k	.327
93	resnet50_st_wzc_2	.327
94	resnet50_hvs_wzc	.327
95	resnet-18_test_m	.327
96	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.327
97	convnext_small_imagenet_10_seed-0	.327
98	yudixie_resnet18_translation_reg_0_240719	.327
99	deit_large_imagenet_full_seed-0	.327
100	deit_small_imagenet_full_seed-0	.327
101	vit_huge_clip336_laion	.296
102	convnext_xxlarge_clip	.296
103	deepeisnn_ei_imagenet_resnet18	.296
104	resnet50_ewconplus_wzc	.296
105	yudixie_resnet50_distance_rotation_0_240908	.296
106	efficientnet-b7	.296
107	convnext_base:clip_laiona_augreg_ft_in1k_384	.296
108	convnext_large_imagenet_full_seed-0	.296
109	resnet34_ecoset_full	.296
110	seresnext50_32x4d	.268
111	vit_base_patch16_dinov3-lvd1689m	.268
112	alexnet_fov12	.268
113	mobilenet_v2_1_3_224	.268
114	resnet50-vicregl0p75	.268
115	deit_small_imagenet_1_seed-0	.268
116	AlexNet_SIN	.243
117	BiT-S-R152x4	.243
118	convnext_tiny_sup	.243
119	resnet34_imagenet_full	.243
120	resnet_101_v2	.220
121	resnet50-vicreg	.220
122	BiT-S-R101x1	.220
123	vonegrcnn_52e_full	.220
124	pnasnet_large_halfPrecision	.199
125	ReAlnet06_ALL	.199
126	ReAlnet04_ALL	.199
127	mobilenet_v2_1_0_224	.199
128	mobilenet_v2_0_5_224	.199
129	Res2Net50_26w_4s	.199
130	BiT-S-R50x1	.199
131	cvt_cvt-13-384-in1k_4_LucyV4	.199
132	dinov2_vitl14_reg_lc	.180
133	resnet50_ewconplus_wzc_seed6_2_323	.180
134	ReAlnet10_ALL	.180
135	resnet50_primary_visual_cortex	.180
136	mobilenet_v2_0_75_192	.180
137	resnet50-SIN_IN_IN	.180
138	convnext_base_imagenet_full_seed-0	.180
139	resnet-152_v2_pytorch	.180
140	convnext_large_clip_laion2b_finelayers	.163
141	AlexNet_SIN_fov12	.163
142	ReAlnet01_cornet_fov12	.163
143	ReAlnet08_ALL	.163
144	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.163
145	yudixie_resnet18_distance_translation_0_240719	.163
146	gabor_filter_entropy_4x4_v2_forcedCosine	.148
147	gabor_filter_entropy_4x4_v2	.148
148	yudixie_wbh_object_class_scaling_0415_run_0	.148
149	ReAlnet01_ALL	.148
150	resnet_50_v1_spiking_l3	.148
151	pnasnet_large	.148
152	resnet50-moclr8deg	.148
153	BiT-S-R101x3	.148
154	deit_base_imagenet_full_seed-0	.148
155	efficientnet_b2_imagenet_full	.148
156	resnet50_imagenet_full	.148
157	pnasnet_large_pytorch	.148
158	convnext_base_fb_in1k_gram_aligned_dinov3	.134
159	resnet50_ewconplus_wzc_seed5_2__323	.134
160	resnet50_st_wzc_seed6_2_323	.134
161	alexnet_fov16	.134
162	ReAlnet02_ALL	.134
163	resnet_50_v2	.134
164	grcnn_109	.134
165	grcnn	.134
166	voneresnet-50	.134
167	resnet50-SIN	.134
168	densenet-201	.134
169	efficientnet_b1_imagenet_full	.134
170	compact_vgg19_IT	.121
171	yudixie_resnet50_imagenet1kpret_0_240908	.121
172	yudixie_resnet50_rotation_reg_0_240908	.121
173	artResNet18_1	.121
174	yudixie_resnet18_object_class_0_240719	.121
175	yudixie_resnet18_translation_rotation_0_240719	.121
176	convnext_small_imagenet_full_seed-0	.121
177	resnet152_imagenet_full	.121
178	ViT_L_32_imagenet1k	.121
179	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.110
180	AlexNet_SIN_fov16	.110
181	xception	.110
182	inception_v3	.110
183	VOneCORnet-S	.110
184	AdvProp_efficientnet-b8	.110
185	antialiased-r50	.110
186	convnext_tiny_imagenet_full_seed-0	.110
187	resnet50_imagenet_10_seed-0	.110
188	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.110
189	alexnet	.110
190	deit3_base_patch16_224_fb_in22k_ft_in1k	.099
191	yolos_tiny	.099
192	yudixie_resnet50_distance_translation_rotation_0_240908	.099
193	alexnet_training_seed_08	.099
194	alexnet_training_seed_09	.099
195	vgg_19	.099
196	nasnet_mobile	.099
197	convnext_tiny:in12k_ft_in1k	.099
198	convnext_small_imagenet_100_seed-0	.099
199	yudixie_resnet18_distance_rotation_0_240719	.099
200	deit_small_imagenet_100_seed-0	.099
201	vgg_16	.090
202	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.081
203	resnet18_fair	.081
204	yudixie_resnet50_object_class_0_240908	.081
205	mobilenet_v2_1_4_224	.081
206	alexnet_training_seed_02	.081
207	ReAlnet09_cornet	.081
208	mobilenet_v2_0_75_160	.081
209	ReAlnet09	.081
210	shufflenet_v2_x1_0	.081
211	efficientnet_b0	.081
212	mobilenet_v2_1-4_224_pytorch	.081
213	yudixie_resnet18_distance_reg_0_240719	.074
214	alexnet_fov4	.067
215	ReAlnet08_cornet	.067
216	ReAlnet04_cornet	.067
217	resnet_152_v2	.067
218	inception_v4	.067
219	ReAlnet08	.067
220	ReAlnet04	.067
221	ReAlnet01_cornet_fov16	.060
222	resnet_152_v1	.060
223	resnet50_tutorial	.060
224	resnet101_ecoset_full	.060
225	resnet18_imagenet_full	.060
226	deepeisnn_ei_cifar10_resnet18	.055
227	yudixie_wbh_object_class_scaling_0415_run_2	.055
228	alexnet_training_seed_03	.055
229	resnet50-barlow	.055
230	resnet101_imagenet_full	.055
231	ReAlnet09_ALL	.050
232	yudixie_resnet50_category_class_0_240908	.050
233	vonegrcnn_62e_nobn	.050
234	yudixie_resnet18_random_0_240719	.050
235	alexnet_training_seed_01_fov4	.045
236	yudixie_resnet18_rotation_reg_0_240719	.045
237	eicircuit_resnet18_bn_20260919	.041
238	ReAlnet07_ALL	.041
239	briaai_rmbg_1_4	.041
240	ReAlnet06_cornet	.041
241	mobilenet_v2_0_5_192	.041
242	ReAlnet06	.041
243	vonegrcnn_47e	.041
244	resnet_50_v1_spiking_l4a	.037
245	ReAlnet10_cornet	.037
246	yudixie_resnet50_distance_translation_0_240908	.037
247	ReAlnet10	.037
248	convnext_femto_ols:d1_in1k	.037
249	AdvProp_efficientnet-b7	.037
250	resnet50_imagenet_100_seed-0	.037
251	predictive_coding_pc	.033
252	alexnet_training_seed_07	.033
253	alexnet_training_seed_06	.033
254	mobilenet_v2_1_0_128	.033
255	inception_v1	.030
256	gabor_filter_mean_abs_4x4_v2	.027
257	yudixie_wbh_object_class_scaling_0415_run_1	.027
258	ReAlnet03_cornet	.027
259	resnet_101_v1	.027
260	ReAlnet03	.027
261	resnet50-vicregl0p9	.027
262	resnet50_ecoset_full	.027
263	resnet_50_v1	.025
264	resnet50-vitoimagevidnet8	.025
265	resnet50-VITO-8deg-cc	.025
266	resnet50-sup	.025
267	resnet-50-robust	.025
268	ReAlnet01_cornet_fov4	.022
269	ReAlnet05_cornet	.022
270	ReAlnet05	.022
271	voneresnet-50-robust	.022
272	ReAlnet02_cornet	.020
273	ReAlnet02	.020
274	blt_vs	.020
275	pr1_stl10_disentangled_alpha04	.018
276	efficientnet_b0_imagenet_full	.018
277	ReAlnet07_cornet	.017
278	yudixie_resnet50_translation_rotation_0_240908	.017
279	ReAlnet07	.017
280	resnet50-SIN_IN	.017
281	yudixie_resnet50_random_0_240908	.015
282	resnet50_robust_l2_eps1	.014
283	alexnet_training_seed_10	.011
284	alexnet_training_seed_04	.011
285	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.011
286	ReAlnet01_cornet	.010
287	ReAlnet01	.010
288	voneresnet-50-non_stochastic	.010
289	imagenet_l2_3_0	.008
290	resnet50_robust_l2_eps3	.008
291	resnet50_imagenet_1_seed-0	.006
292	convnext_base_fb_in22k_ft_in1k
293	convnext_base_fb_in1k
294	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