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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	resnet_50_v1_spiking_l4	1.0
2	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
3	compact_resnet50_robust_IT	1.0
4	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
5	compact_vgg19_V4	1.0
6	dinov2_vitg14_lc	1.0
7	dinov2_vitl14_lc_res336	1.0
8	densenet-121	1.0
9	yudixie_wbh_object_class_scaling_0415_run_3	1.0
10	alexnet_training_seed_05	.974
11	resnext101_32x32d_wsl	.974
12	yudixie_resnet50_translation_reg_0_240908	.974
13	compact_cornets_V4	.974
14	convnext_large:fb_in22k_ft_in1k	.951
15	mobilenet_v2_0_75_224	.882
16	vit_base_patch16_clip_224:openai_ft_in1k	.882
17	alexnet_training_seed_01	.882
18	yudixie_resnet18_category_class_0_240719	.882
19	omnivore_swinS	.882
20	alexnet_training_seed_01_fov16	.882
21	AdvProp_efficientnet-b4	.882
22	resnet_SIN_IN_FT_IN	.882
23	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.861
24	resnext101_32x8d_wsl	.799
25	vit_base_patch14_dinov2-lvd142m	.799
26	resnet50_ewconplus_wzc_seed2_2__323	.799
27	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.780
28	vit_base_patch14_reg4_dinov2-lvd142m	.780
29	dinov2_vitg14_reg_lc	.780
30	eva02_large_in22k	.780
31	mobilenet_v2_1_0_192	.723
32	cv_18_dagger_408_pretrained	.723
33	cvt_cvt-13-224-in1k_4_LucyV4	.723
34	deit_small_imagenet_10_seed-0	.723
35	convnext_large_in22k_supervised	.723
36	convnext_xlarge:fb_in22k_ft_in1k	.706
37	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.655
38	mobilenet_v2_1_0_160	.655
39	focalnet_tiny_lrf_in1k	.655
40	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.655
41	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.655
42	BiT-S-R152x2	.655
43	cvt_cvt-21-224-in1k_4_LucyV4	.593
44	resnet152_ecoset_full	.593
45	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.579
46	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.537
47	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.537
48	ReAlnet05_ALL	.537
49	vit_large_clip336_openai	.537
50	omnivore_swinB	.524
51	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.486
52	resnet_50_v1_spiking	.486
53	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.486
54	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.486
55	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.486
56	effnetb1_272x240	.486
57	yudixie_resnet18_distance_translation_rotation_0_240719	.486
58	nasnet_large	.486
59	cvt_cvt-21-384-in1k_4_LucyV4	.475
60	custom_model_cv_18_dagger_408	.440
61	compact_resnet50_V4	.440
62	compact_resnet50_IT	.440
63	compact_resnet50_robust_V4	.440
64	compact_cornets_IT	.440
65	resnet50_st_wzc_seed3_2_323	.440
66	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.399
67	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.399
68	convnext_small_imagenet_1_seed-0	.399
69	yudixie_resnet18_imagenet1kpret_0_240719	.399
70	AlexNet_SIN_fov4	.399
71	ReAlnet03_ALL	.399
72	alexnet_training_seed_01_fov12	.399
73	densenet-169	.399
74	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.399
75	dinov2_vitl14_reg_lc_featread	.399
76	vit_large_patch14_clip_224:laion2b_ft_in1k	.361
77	vit_large_patch14_clip_224:openai_ft_in1k	.361
78	yudixie_resnet50_distance_reg_0_240908	.361
79	AdvProp_efficientnet-b6	.361
80	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.361
81	resnext101_32x48d_wsl	.361
82	resnet18_ecoset_full	.361
83	AdvProp_efficientnet-b2	.361
84	BiT-S-R50x3	.361
85	AT_efficientnet-b2	.361
86	resnet50_st_wzc_seed7_2_323	.361
87	deepeisnn_ei_cifar10_vgg19	.361
88	resnet50_st_wzc_2	.327
89	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.327
90	deit_small_imagenet_full_seed-0	.327
91	convnext_small_imagenet_10_seed-0	.327
92	deit_large_imagenet_full_seed-0	.327
93	resnet-18_test_m	.327
94	yudixie_resnet18_translation_reg_0_240719	.327
95	resnet50_hvs_wzc	.327
96	deit3_base_patch16_224_fb_in1k	.327
97	yudixie_wbh_object_class_scaling_0415_imagenet	.327
98	resnet50_ewconplus_wzc	.296
99	resnet34_ecoset_full	.296
100	convnext_large_imagenet_full_seed-0	.296
101	yudixie_resnet50_distance_rotation_0_240908	.296
102	efficientnet-b7	.296
103	convnext_base:clip_laiona_augreg_ft_in1k_384	.296
104	deepeisnn_ei_imagenet_resnet18	.296
105	convnext_xxlarge_clip	.296
106	vit_huge_clip336_laion	.296
107	alexnet_fov12	.268
108	vit_base_patch16_dinov3-lvd1689m	.268
109	deit_small_imagenet_1_seed-0	.268
110	mobilenet_v2_1_3_224	.268
111	resnet50-vicregl0p75	.268
112	resnet34_imagenet_full	.243
113	AlexNet_SIN	.243
114	convnext_tiny_sup	.243
115	BiT-S-R152x4	.243
116	resnet50-vicreg	.220
117	resnet_101_v2	.220
118	vonegrcnn_52e_full	.220
119	BiT-S-R101x1	.220
120	mobilenet_v2_0_5_224	.199
121	pnasnet_large_halfPrecision	.199
122	cvt_cvt-13-384-in1k_4_LucyV4	.199
123	Res2Net50_26w_4s	.199
124	mobilenet_v2_1_0_224	.199
125	ReAlnet04_ALL	.199
126	ReAlnet06_ALL	.199
127	BiT-S-R50x1	.199
128	ReAlnet10_ALL	.180
129	resnet-152_v2_pytorch	.180
130	convnext_base_imagenet_full_seed-0	.180
131	mobilenet_v2_0_75_192	.180
132	resnet50-SIN_IN_IN	.180
133	resnet50_primary_visual_cortex	.180
134	resnet50_ewconplus_wzc_seed6_2_323	.180
135	dinov2_vitl14_reg_lc	.180
136	yudixie_resnet18_distance_translation_0_240719	.163
137	ReAlnet01_cornet_fov12	.163
138	AlexNet_SIN_fov12	.163
139	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.163
140	ReAlnet08_ALL	.163
141	convnext_large_clip_laion2b_finelayers	.163
142	resnet50_imagenet_full	.148
143	deit_base_imagenet_full_seed-0	.148
144	pnasnet_large_pytorch	.148
145	efficientnet_b2_imagenet_full	.148
146	pnasnet_large	.148
147	resnet_50_v1_spiking_l3	.148
148	ReAlnet01_ALL	.148
149	resnet50-moclr8deg	.148
150	BiT-S-R101x3	.148
151	yudixie_wbh_object_class_scaling_0415_run_0	.148
152	densenet-201	.134
153	alexnet_fov16	.134
154	efficientnet_b1_imagenet_full	.134
155	resnet50-SIN	.134
156	ReAlnet02_ALL	.134
157	grcnn	.134
158	voneresnet-50	.134
159	grcnn_109	.134
160	resnet_50_v2	.134
161	resnet50_st_wzc_seed6_2_323	.134
162	resnet50_ewconplus_wzc_seed5_2__323	.134
163	convnext_base_fb_in1k_gram_aligned_dinov3	.134
164	yudixie_resnet50_imagenet1kpret_0_240908	.121
165	convnext_small_imagenet_full_seed-0	.121
166	ViT_L_32_imagenet1k	.121
167	resnet152_imagenet_full	.121
168	yudixie_resnet18_translation_rotation_0_240719	.121
169	yudixie_resnet50_rotation_reg_0_240908	.121
170	yudixie_resnet18_object_class_0_240719	.121
171	compact_vgg19_IT	.121
172	artResNet18_1	.121
173	inception_v3	.110
174	convnext_tiny_imagenet_full_seed-0	.110
175	alexnet	.110
176	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.110
177	VOneCORnet-S	.110
178	resnet50_imagenet_10_seed-0	.110
179	xception	.110
180	AlexNet_SIN_fov16	.110
181	antialiased-r50	.110
182	AdvProp_efficientnet-b8	.110
183	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.110
184	convnext_tiny:in12k_ft_in1k	.099
185	alexnet_training_seed_08	.099
186	deit_small_imagenet_100_seed-0	.099
187	yolos_tiny	.099
188	convnext_small_imagenet_100_seed-0	.099
189	yudixie_resnet18_distance_rotation_0_240719	.099
190	nasnet_mobile	.099
191	vgg_19	.099
192	yudixie_resnet50_distance_translation_rotation_0_240908	.099
193	alexnet_training_seed_09	.099
194	deit3_base_patch16_224_fb_in22k_ft_in1k	.099
195	vgg_16	.090
196	alexnet_training_seed_02	.081
197	ReAlnet09	.081
198	mobilenet_v2_0_75_160	.081
199	mobilenet_v2_1-4_224_pytorch	.081
200	aughost_fair_resnet18	.081
201	shufflenet_v2_x1_0	.081
202	ReAlnet09_cornet	.081
203	efficientnet_b0	.081
204	mobilenet_v2_1_4_224	.081
205	yudixie_resnet50_object_class_0_240908	.081
206	resnet18_fair	.081
207	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.081
208	yudixie_resnet18_distance_reg_0_240719	.074
209	ReAlnet08	.067
210	alexnet_fov4	.067
211	inception_v4	.067
212	ReAlnet04_cornet	.067
213	ReAlnet08_cornet	.067
214	resnet_152_v2	.067
215	ReAlnet04	.067
216	resnet50_tutorial	.060
217	resnet18_imagenet_full	.060
218	resnet101_ecoset_full	.060
219	ReAlnet01_cornet_fov16	.060
220	resnet_152_v1	.060
221	resnet101_imagenet_full	.055
222	resnet50-barlow	.055
223	alexnet_training_seed_03	.055
224	yudixie_wbh_object_class_scaling_0415_run_2	.055
225	deepeisnn_ei_cifar10_resnet18	.055
226	yudixie_resnet50_category_class_0_240908	.050
227	yudixie_resnet18_random_0_240719	.050
228	vonegrcnn_62e_nobn	.050
229	ReAlnet09_ALL	.050
230	yudixie_resnet18_rotation_reg_0_240719	.045
231	alexnet_training_seed_01_fov4	.045
232	ReAlnet06	.041
233	briaai_rmbg_1_4	.041
234	mobilenet_v2_0_5_192	.041
235	ReAlnet06_cornet	.041
236	vonegrcnn_47e	.041
237	ReAlnet07_ALL	.041
238	ReAlnet10	.037
239	resnet_50_v1_spiking_l4a	.037
240	resnet50_imagenet_100_seed-0	.037
241	yudixie_resnet50_distance_translation_0_240908	.037
242	ReAlnet10_cornet	.037
243	AdvProp_efficientnet-b7	.037
244	convnext_femto_ols:d1_in1k	.037
245	alexnet_training_seed_06	.033
246	mobilenet_v2_1_0_128	.033
247	alexnet_training_seed_07	.033
248	predictive_coding_pc	.033
249	inception_v1	.030
250	ReAlnet03	.027
251	resnet50_ecoset_full	.027
252	ReAlnet03_cornet	.027
253	resnet_101_v1	.027
254	resnet50-vicregl0p9	.027
255	yudixie_wbh_object_class_scaling_0415_run_1	.027
256	resnet-50-robust	.025
257	resnet50-VITO-8deg-cc	.025
258	resnet_50_v1	.025
259	resnet50-vitoimagevidnet8	.025
260	resnet50-sup	.025
261	ReAlnet05	.022
262	ReAlnet01_cornet_fov4	.022
263	ReAlnet05_cornet	.022
264	voneresnet-50-robust	.022
265	blt_vs	.020
266	ReAlnet02_cornet	.020
267	ReAlnet02	.020
268	efficientnet_b0_imagenet_full	.018
269	pr1_stl10_disentangled_alpha04	.018
270	ReAlnet07	.017
271	resnet50-SIN_IN	.017
272	ReAlnet07_cornet	.017
273	yudixie_resnet50_translation_rotation_0_240908	.017
274	yudixie_resnet50_random_0_240908	.015
275	resnet50_robust_l2_eps1	.014
276	alexnet_training_seed_04	.011
277	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.011
278	alexnet_training_seed_10	.011
279	ReAlnet01_cornet	.010
280	voneresnet-50-non_stochastic	.010
281	ReAlnet01	.010
282	resnet50_robust_l2_eps3	.008
283	imagenet_l2_3_0	.008
284	resnet50_imagenet_1_seed-0	.006
285	hmax
286	convnext_base_fb_in1k
287	convnext_base_fb_in22k_ft_in1k
288	dinov2_vitl14_lc

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