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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024round_f-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	alexnet_training_seed_01	1.0
2	yudixie_resnet50_translation_reg_0_240908	1.0
3	resnet50-vicreg	1.0
4	resnet-18_test_m	1.0
5	convnext_tiny:in12k_ft_in1k	1.0
6	convnext_small_imagenet_10_seed-0	1.0
7	yudixie_resnet18_translation_rotation_0_240719	1.0
8	alexnet_training_seed_01_fov16	1.0
9	ReAlnet01_cornet_fov4	1.0
10	alexnet_fov12	1.0
11	briaai_rmbg_1_4	1.0
12	alexnet_fov16	1.0
13	compact_vgg19_V4	1.0
14	convnext_large_clip_laion2b_finelayers	1.0
15	yudixie_wbh_object_class_scaling_0415_run_0	1.0
16	VOneCORnet-S	1.0
17	vit_base_patch16_clip_224:openai_ft_in1k	1.0
18	pnasnet_large_halfPrecision	.961
19	resnet_101_v1	.961
20	inception_v4	.961
21	convnext_base:clip_laiona_augreg_ft_in1k_384	.961
22	cvt_cvt-13-224-in1k_4_LucyV4	.961
23	resnet-152_v2_pytorch	.961
24	resnext101_32x32d_wsl	.961
25	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.961
26	yudixie_resnet50_random_0_240908	.906
27	inception_v1	.906
28	yudixie_resnet50_distance_rotation_0_240908	.906
29	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.906
30	alexnet_training_seed_01_fov12	.906
31	compact_vgg19_IT	.906
32	compact_resnet50_robust_V4	.906
33	convnext_large_in22k_supervised	.867
34	alexnet_training_seed_10	.867
35	mobilenet_v2_1_0_160	.867
36	resnet_50_v2	.867
37	yudixie_resnet18_rotation_reg_0_240719	.867
38	resnet50_ecoset_full	.867
39	cv_18_dagger_408_pretrained	.867
40	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.867
41	inception_v3	.818
42	convnext_xlarge:fb_in22k_ft_in1k	.818
43	yudixie_resnet18_distance_rotation_0_240719	.818
44	alexnet_training_seed_07	.783
45	alexnet_training_seed_04	.783
46	resnet_152_v2	.783
47	AdvProp_efficientnet-b4	.783
48	deit_small_imagenet_full_seed-0	.783
49	resnet50_imagenet_10_seed-0	.783
50	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.783
51	resnet18_imagenet_full	.783
52	vit_huge_clip336_laion	.739
53	resnet_101_v2	.739
54	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.739
55	AlexNet_SIN_fov16	.739
56	compact_cornets_V4	.739
57	convnext_base_fb_in22k_ft_in1k	.707
58	yolos_tiny	.707
59	ReAlnet02_cornet	.707
60	ReAlnet02	.707
61	BiT-S-R152x2	.707
62	deit_base_imagenet_full_seed-0	.707
63	efficientnet_b2_imagenet_full	.707
64	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.667
65	yudixie_resnet50_distance_translation_rotation_0_240908	.667
66	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.667
67	yudixie_resnet18_random_0_240719	.667
68	resnet101_imagenet_full	.667
69	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.667
70	eicircuit_resnet18_bn_20260919	.638
71	resnet_50_v1_spiking_l4a	.638
72	yudixie_resnet50_object_class_0_240908	.638
73	alexnet_training_seed_02	.638
74	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.638
75	vit_large_patch14_clip_224:openai_ft_in1k	.638
76	BiT-S-R101x1	.638
77	BiT-S-R101x3	.638
78	resnet50_robust_l2_eps3	.638
79	imagenet_l2_3_0	.638
80	cvt_cvt-21-224-in1k_4_LucyV4	.638
81	compact_resnet50_IT	.638
82	ReAlnet08_ALL	.638
83	pr1_stl10_disentangled_alpha04	.602
84	deit_small_imagenet_100_seed-0	.602
85	resnext101_32x8d_wsl	.576
86	pnasnet_large	.576
87	alexnet_training_seed_06	.576
88	yudixie_resnet50_translation_rotation_0_240908	.576
89	efficientnet_b0	.576
90	BiT-S-R50x1	.576
91	ReAlnet01_cornet_fov12	.576
92	dinov2_vitl14_lc	.544
93	convnext_xxlarge_clip	.544
94	dinov2_vitl14_reg_lc_featread	.544
95	dinov2_vitl14_lc_res336	.544
96	dinov2_vitl14_reg_lc	.544
97	eva02_large_in22k	.544
98	dinov2_vitg14_reg_lc	.544
99	dinov2_vitg14_lc	.544
100	vit_base_patch14_reg4_dinov2-lvd142m	.544
101	vit_base_patch16_dinov3-lvd1689m	.544
102	vit_base_patch14_dinov2-lvd142m	.544
103	mobilenet_v2_1_4_224	.544
104	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.544
105	vit_large_patch14_clip_224:laion2b_ft_in1k	.544
106	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.544
107	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.544
108	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.544
109	convnext_large:fb_in22k_ft_in1k	.544
110	compact_resnet50_V4	.544
111	mobilenet_v2_1-4_224_pytorch	.544
112	alexnet_training_seed_09	.520
113	deit_small_imagenet_10_seed-0	.520
114	ReAlnet10_ALL	.520
115	alexnet	.520
116	deepeisnn_ei_imagenet_resnet18	.491
117	seresnext50_32x4d	.470
118	deepeisnn_ei_cifar10_resnet18	.470
119	resnet18_fair	.470
120	ReAlnet09_cornet	.470
121	ReAlnet09	.470
122	resnet50-moclr8deg	.470
123	convnext_base_imagenet_full_seed-0	.470
124	yudixie_resnet18_translation_reg_0_240719	.470
125	alexnet_training_seed_01_fov4	.470
126	convnext_small_imagenet_1_seed-0	.443
127	yudixie_resnet18_distance_reg_0_240719	.443
128	gabor_filter_entropy_4x4_v2_forcedCosine	.424
129	resnet50_ewconplus_wzc_seed6_2_323	.424
130	xception	.424
131	mobilenet_v2_0_75_224	.424
132	voneresnet-50	.424
133	efficientnet_b1_imagenet_full	.424
134	efficientnet_b0_imagenet_full	.424
135	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.424
136	vit_large_patch14_clip_336:laion2b_ft_in1k	.400
137	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.400
138	deit_small_imagenet_1_seed-0	.400
139	resnet_50_v1_spiking	.383
140	convnext_tiny_imagenet_full_seed-0	.383
141	resnet18_ecoset_full	.383
142	ReAlnet06_ALL	.383
143	ReAlnet05_ALL	.383
144	pnasnet_large_pytorch	.383
145	voneresnet-50-non_stochastic	.383
146	compact_resnet50_robust_IT	.361
147	yudixie_resnet50_rotation_reg_0_240908	.346
148	omnivore_swinS	.346
149	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.346
150	artResNet18_1	.346
151	convnext_small_imagenet_100_seed-0	.346
152	yudixie_resnet18_imagenet1kpret_0_240719	.346
153	hmax	.346
154	resnet_50_v1_spiking_l4	.326
155	convnext_femto_ols:d1_in1k	.326
156	resnext101_32x48d_wsl	.326
157	vit_large_clip336_openai	.312
158	yudixie_wbh_object_class_scaling_0415_run_1	.312
159	alexnet_training_seed_03	.312
160	nasnet_large	.312
161	mobilenet_v2_0_5_224	.312
162	resnet152_ecoset_full	.312
163	resnet34_imagenet_full	.312
164	resnet50_ewconplus_wzc	.312
165	ReAlnet09_ALL	.312
166	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.282
167	convnext_base_fb_in1k	.282
168	resnet50_st_wzc_seed7_2_323	.282
169	ReAlnet01_cornet	.282
170	nasnet_mobile	.282
171	ReAlnet01	.282
172	deit_large_imagenet_full_seed-0	.282
173	cvt_cvt-21-384-in1k_4_LucyV4	.282
174	AlexNet_SIN_fov4	.282
175	gabor_filter_entropy_4x4_v2	.255
176	resnet50_ewconplus_wzc_seed2_2__323	.255
177	grcnn	.255
178	shufflenet_v2_x1_0	.255
179	resnet50-SIN	.255
180	resnet50-SIN_IN	.255
181	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.255
182	vonegrcnn_52e_full	.255
183	resnet50_imagenet_100_seed-0	.255
184	ReAlnet01_cornet_fov16	.255
185	ReAlnet07_ALL	.255
186	ReAlnet02_ALL	.255
187	custom_model_cv_18_dagger_408	.255
188	deepeisnn_ei_cifar10_vgg19	.240
189	yudixie_wbh_object_class_scaling_0415_imagenet	.230
190	yudixie_resnet50_distance_reg_0_240908	.230
191	yudixie_resnet50_distance_translation_0_240908	.230
192	omnivore_swinB	.230
193	densenet-121	.230
194	resnet_SIN_IN_FT_IN	.230
195	yudixie_resnet18_distance_translation_rotation_0_240719	.230
196	yudixie_resnet18_category_class_0_240719	.230
197	ReAlnet04_ALL	.230
198	ReAlnet03_ALL	.230
199	ReAlnet07_cornet	.208
200	ReAlnet04_cornet	.208
201	ReAlnet03_cornet	.208
202	ReAlnet03	.208
203	ReAlnet07	.208
204	ReAlnet04	.208
205	convnext_small_imagenet_full_seed-0	.208
206	AlexNet_SIN_fov12	.208
207	resnet50_st_wzc_2	.208
208	ReAlnet01_ALL	.208
209	resnet_50_v1_spiking_l3	.208
210	ReAlnet08_cornet	.187
211	vgg_16	.187
212	ReAlnet08	.187
213	resnet50-vitoimagevidnet8	.187
214	resnet50_robust_l2_eps1	.187
215	resnet50-VITO-8deg-cc	.187
216	AT_efficientnet-b2	.187
217	resnet50-vicregl0p9	.187
218	AdvProp_efficientnet-b2	.187
219	AdvProp_efficientnet-b6	.187
220	AlexNet_SIN	.187
221	regnety_032	.169
222	deit3_base_patch16_224_fb_in1k	.169
223	resnet50_ewconplus_wzc_seed5_2__323	.169
224	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.169
225	convnext_large_imagenet_full_seed-0	.169
226	blt_vs	.160
227	yudixie_resnet18_object_class_0_240719	.160
228	convnext_base_fb_in1k_gram_aligned_dinov3	.153
229	ReAlnet05_cornet	.153
230	mobilenet_v2_0_5_192	.153
231	ReAlnet05	.153
232	voneresnet-50-robust	.153
233	AdvProp_efficientnet-b8	.153
234	vonegrcnn_47e	.153
235	resnet101_ecoset_full	.153
236	yudixie_resnet50_category_class_0_240908	.138
237	ReAlnet10_cornet	.138
238	mobilenet_v2_0_75_160	.138
239	vonegrcnn_62e_nobn	.138
240	ReAlnet10	.138
241	densenet-201	.138
242	cvt_cvt-13-384-in1k_4_LucyV4	.138
243	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.138
244	compact_cornets_IT	.138
245	gabor_filter_entropy_4x4	.125
246	yudixie_wbh_object_class_scaling_0415_run_3	.125
247	resnet50_imagenet_1_seed-0	.125
248	yudixie_wbh_object_class_scaling_0415_run_2	.113
249	ReAlnet06_cornet	.113
250	ReAlnet06	.113
251	BiT-S-R50x3	.113
252	yudixie_resnet18_distance_translation_0_240719	.113
253	resnet152_imagenet_full	.113
254	alexnet_training_seed_08	.102
255	resnet_152_v1	.102
256	grcnn_109	.102
257	BiT-S-R152x4	.102
258	ViT_L_32_imagenet1k	.102
259	resnet-50-robust	.102
260	gabor_filter_mean_abs_4x4_v2	.092
261	resnet50_primary_visual_cortex	.092
262	mobilenet_v2_0_75_192	.092
263	efficientnet-b7	.092
264	vit_relpos_base_patch16_clsgap_224:sw_in1k	.092
265	resnet34_ecoset_full	.092
266	resnet50_imagenet_full	.092
267	alexnet_fov4	.092
268	mobilenet_v2_1_0_128	.083
269	mobilenet_v2_1_0_224	.075
270	resnet50-barlow	.075
271	densenet-169	.075
272	resnet50-vicregl0p75	.075
273	convnext_tiny_sup	.075
274	resnet50_tutorial	.075
275	resnet50_hvs_wzc	.068
276	effnetb1_272x240	.068
277	yudixie_resnet50_imagenet1kpret_0_240908	.061
278	mobilenet_v2_1_3_224	.061
279	AdvProp_efficientnet-b7	.055
280	focalnet_tiny_lrf_in1k	.055
281	alexnet_training_seed_05	.050
282	Res2Net50_26w_4s	.050
283	resnet50_st_wzc_seed6_2_323	.045
284	resnet_50_v1	.033
285	resnet50-sup	.033
286	mobilenet_v2_1_0_192	.030
287	antialiased-r50	.027
288	predictive_coding_pc	.024
289	resnet50-SIN_IN_IN	.018
290	vgg_19	.012
291	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3

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

0.87.

Note that scores are relative to this ceiling.

Data: Ferguson2024round_f

Metric: value_delta