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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024half-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_large_clip_laion2b_finelayers	1.0
2	resnet50_st_wzc_seed6_2_323	1.0
3	cv_18_dagger_408_pretrained	1.0
4	compact_resnet50_V4	1.0
5	resnet_152_v2	1.0
6	mobilenet_v2_0_75_192	1.0
7	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
8	swin_small_patch4_window7_224:ms_in22k_ft_in1k	1.0
9	yudixie_resnet18_random_0_240719	1.0
10	efficientnet_b0_imagenet_full	1.0
11	cvt_cvt-13-384-in1k_4_LucyV4	1.0
12	resnext101_32x32d_wsl	1.0
13	seresnext50_32x4d	1.0
14	yudixie_wbh_object_class_scaling_0415_run_3	1.0
15	yudixie_resnet50_distance_reg_0_240908	1.0
16	yudixie_resnet50_imagenet1kpret_0_240908	1.0
17	yudixie_resnet50_random_0_240908	1.0
18	alexnet_training_seed_07	1.0
19	alexnet_training_seed_05	1.0
20	ReAlnet05_cornet	1.0
21	mobilenet_v2_0_5_224	1.0
22	ReAlnet05	1.0
23	convnext_tiny_sup	1.0
24	artResNet18_1	1.0
25	yudixie_resnet18_rotation_reg_0_240719	1.0
26	resnet50_imagenet_1_seed-0	1.0
27	resnet18_ecoset_full	1.0
28	resnet18_imagenet_full	1.0
29	dinov2_vitl14_reg_lc	.975
30	dinov2_vitg14_reg_lc	.975
31	dinov2_vitl14_lc	.975
32	inception_v4	.975
33	convnext_base_imagenet_full_seed-0	.975
34	resnet50_tutorial	.975
35	resnet-152_v2_pytorch	.975
36	effnetb1_272x240	.975
37	resnext101_32x8d_wsl	.975
38	vit_large_clip336_openai	.942
39	dinov2_vitl14_reg_lc_featread	.942
40	dinov2_vitg14_lc	.942
41	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.942
42	nasnet_mobile	.942
43	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.942
44	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.942
45	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.942
46	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.942
47	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.942
48	vonegrcnn_52e_full	.942
49	vonegrcnn_47e	.942
50	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.942
51	resnet50_st_wzc_seed3_2_323	.882
52	vit_base_patch16_dinov3-lvd1689m	.882
53	pnasnet_large_halfPrecision	.882
54	AlexNet_SIN_fov4	.882
55	resnet50_st_wzc_2	.882
56	convnext_small_imagenet_10_seed-0	.882
57	convnext_small_imagenet_1_seed-0	.882
58	regnety_032	.852
59	convnext_xxlarge_clip	.852
60	resnet_50_v1_spiking_l4a	.852
61	alexnet_training_seed_04	.852
62	resnet50-barlow	.852
63	BiT-S-R152x4	.852
64	resnet152_ecoset_full	.852
65	resnet34_imagenet_full	.852
66	convnext_large_in22k_supervised	.798
67	resnet_50_v1_spiking_l3	.798
68	efficientnet-b7	.798
69	AdvProp_efficientnet-b8	.798
70	focalnet_tiny_lrf_in1k	.798
71	voneresnet-50-non_stochastic	.798
72	eva02_large_in22k	.771
73	deepeisnn_ei_imagenet_resnet18	.771
74	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.771
75	convnext_base_fb_in22k_ft_in1k	.771
76	resnet18_fair	.771
77	grcnn	.771
78	voneresnet-50-robust	.771
79	vit_large_patch14_clip_336:laion2b_ft_in1k	.771
80	vit_large_patch14_clip_224:openai_ft_in1k	.771
81	vit_base_patch16_clip_224:openai_ft_in1k	.771
82	efficientnet_b0	.771
83	convnext_xlarge:fb_in22k_ft_in1k	.771
84	AT_efficientnet-b2	.771
85	AdvProp_efficientnet-b2	.771
86	AdvProp_efficientnet-b4	.771
87	yudixie_resnet18_category_class_0_240719	.771
88	ViT_L_32_imagenet1k	.771
89	resnext101_32x48d_wsl	.771
90	AlexNet_SIN_fov16	.722
91	yudixie_resnet50_category_class_0_240908	.722
92	mobilenet_v2_0_5_192	.722
93	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.722
94	AdvProp_efficientnet-b7	.722
95	yudixie_resnet18_translation_reg_0_240719	.722
96	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.722
97	vit_huge_clip336_laion	.698
98	ReAlnet07_cornet	.698
99	vgg_16	.698
100	ReAlnet07	.698
101	Res2Net50_26w_4s	.698
102	omnivore_swinB	.698
103	vit_large_patch14_clip_224:laion2b_ft_in1k	.698
104	densenet-121	.698
105	BiT-S-R101x3	.698
106	deit_small_imagenet_1_seed-0	.698
107	dinov2_vitl14_lc_res336	.653
108	compact_cornets_V4	.653
109	compact_resnet50_IT	.653
110	mobilenet_v2_1_0_160	.653
111	convnext_base_fb_in1k_gram_aligned_dinov3	.631
112	ReAlnet01_cornet_fov16	.631
113	alexnet_fov16	.631
114	compact_vgg19_IT	.631
115	yudixie_resnet50_object_class_0_240908	.631
116	convnext_large:fb_in22k_ft_in1k	.631
117	yudixie_resnet18_imagenet1kpret_0_240719	.631
118	yudixie_resnet18_distance_translation_rotation_0_240719	.631
119	yudixie_resnet18_translation_rotation_0_240719	.631
120	convnext_tiny_imagenet_full_seed-0	.631
121	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.631
122	compact_resnet50_robust_IT	.591
123	briaai_rmbg_1_4	.591
124	alexnet_training_seed_10	.591
125	alexnet_training_seed_09	.591
126	mobilenet_v2_0_75_160	.591
127	inception_v1	.591
128	resnet50-SIN_IN	.591
129	gabor_filter_entropy_4x4	.571
130	ReAlnet01_cornet_fov4	.571
131	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.571
132	resnet_50_v1_spiking	.571
133	ReAlnet02_cornet	.571
134	mobilenet_v2_1_0_192	.571
135	resnet_152_v1	.571
136	vonegrcnn_62e_nobn	.571
137	ReAlnet02	.571
138	resnet50_robust_l2_eps1	.571
139	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.571
140	convnext_tiny:in12k_ft_in1k	.571
141	BiT-S-R50x1	.571
142	BiT-S-R50x3	.571
143	yudixie_resnet18_distance_rotation_0_240719	.571
144	pnasnet_large_pytorch	.571
145	yudixie_wbh_object_class_scaling_0415_run_1	.535
146	yudixie_wbh_object_class_scaling_0415_imagenet	.535
147	resnet50_hvs_wzc	.535
148	mobilenet_v2_1_3_224	.535
149	VOneCORnet-S	.535
150	resnet50-vicregl0p9	.535
151	alexnet_fov12	.517
152	alexnet_training_seed_06	.517
153	vgg_19	.517
154	voneresnet-50	.517
155	resnet50-vicreg	.517
156	shufflenet_v2_x1_0	.517
157	vit_relpos_base_patch16_clsgap_224:sw_in1k	.517
158	cvt_cvt-21-384-in1k_4_LucyV4	.517
159	resnet50_ecoset_full	.517
160	hmax	.517
161	vit_base_patch14_reg4_dinov2-lvd142m	.484
162	alexnet_training_seed_01_fov16	.484
163	compact_cornets_IT	.484
164	nasnet_large	.484
165	yudixie_resnet18_distance_translation_0_240719	.484
166	resnet50_imagenet_full	.484
167	yudixie_wbh_object_class_scaling_0415_run_0	.467
168	ReAlnet01_cornet_fov12	.467
169	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.467
170	alexnet_training_seed_03	.467
171	deit_small_imagenet_10_seed-0	.467
172	deit_large_imagenet_full_seed-0	.467
173	resnet50_imagenet_100_seed-0	.467
174	yudixie_wbh_object_class_scaling_0415_run_2	.438
175	compact_resnet50_robust_V4	.438
176	resnet_101_v2	.438
177	deepeisnn_ei_cifar10_resnet18	.423
178	yolos_tiny	.423
179	mobilenet_v2_1_4_224	.423
180	grcnn_109	.423
181	densenet-201	.423
182	resnet_SIN_IN_FT_IN	.423
183	antialiased-r50	.423
184	deit_base_imagenet_full_seed-0	.423
185	mobilenet_v2_1-4_224_pytorch	.423
186	alexnet	.423
187	alexnet_training_seed_01	.396
188	mobilenet_v2_1_0_224	.383
189	ReAlnet08_cornet	.383
190	inception_v3	.383
191	ReAlnet08	.383
192	omnivore_swinS	.383
193	convnext_base:clip_laiona_augreg_ft_in1k_384	.383
194	convnext_large_imagenet_full_seed-0	.383
195	cvt_cvt-13-224-in1k_4_LucyV4	.383
196	resnet-50-robust	.383
197	mobilenet_v2_0_75_224	.358
198	resnet50-vicregl0p75	.358
199	gabor_filter_entropy_4x4_v2_forcedCosine	.346
200	convnext_femto_ols:d1_in1k	.346
201	efficientnet_b2_imagenet_full	.346
202	cvt_cvt-21-224-in1k_4_LucyV4	.346
203	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.346
204	predictive_coding_pc	.324
205	vit_base_patch14_dinov2-lvd142m	.324
206	resnet_50_v1	.324
207	resnet50-SIN	.324
208	densenet-169	.324
209	resnet50-sup	.324
210	custom_model_cv_18_dagger_408	.324
211	ReAlnet07_ALL	.313
212	yudixie_resnet50_distance_translation_rotation_0_240908	.313
213	resnet50-SIN_IN_IN	.313
214	BiT-S-R101x1	.313
215	convnext_small_imagenet_100_seed-0	.313
216	convnext_small_imagenet_full_seed-0	.313
217	resnet50_st_wzc_seed7_2_323	.293
218	alexnet_training_seed_01_fov12	.293
219	compact_vgg19_V4	.293
220	resnet50_ewconplus_wzc_seed2_2__323	.283
221	ReAlnet09_cornet	.283
222	mobilenet_v2_1_0_128	.283
223	yudixie_resnet50_rotation_reg_0_240908	.283
224	ReAlnet09	.283
225	resnet50-vitoimagevidnet8	.283
226	resnet-18_test_m	.283
227	resnet50-VITO-8deg-cc	.283
228	blt_vs	.283
229	yudixie_resnet18_object_class_0_240719	.283
230	resnet101_ecoset_full	.283
231	gabor_filter_entropy_4x4_v2	.256
232	ReAlnet01_cornet	.256
233	yudixie_resnet50_distance_translation_0_240908	.256
234	ReAlnet01	.256
235	alexnet_fov4	.240
236	BiT-S-R152x2	.240
237	AlexNet_SIN	.232
238	ReAlnet08_ALL	.232
239	ReAlnet05_ALL	.232
240	yudixie_resnet50_distance_rotation_0_240908	.232
241	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.232
242	deit3_base_patch16_224_fb_in1k	.217
243	resnet_50_v1_spiking_l4	.217
244	resnet101_imagenet_full	.217
245	AdvProp_efficientnet-b6	.210
246	alexnet_training_seed_01_fov4	.196
247	resnet152_imagenet_full	.196
248	ReAlnet10_ALL	.190
249	deepeisnn_ei_cifar10_vgg19	.172
250	ReAlnet10_cornet	.172
251	ReAlnet06_cornet	.172
252	resnet_50_v2	.172
253	yudixie_resnet50_translation_reg_0_240908	.172
254	ReAlnet10	.172
255	ReAlnet06	.172
256	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.172
257	resnet34_ecoset_full	.172
258	eicircuit_resnet18_bn_20260919	.161
259	ReAlnet09_ALL	.155
260	ReAlnet02_ALL	.155
261	ReAlnet03_cornet	.155
262	resnet_101_v1	.155
263	ReAlnet03	.155
264	yudixie_resnet18_distance_reg_0_240719	.155
265	resnet50_imagenet_10_seed-0	.155
266	alexnet_training_seed_08	.145
267	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.141
268	deit_small_imagenet_100_seed-0	.141
269	resnet50_ewconplus_wzc	.132
270	ReAlnet04_cornet	.127
271	ReAlnet04	.127
272	gabor_filter_mean_abs_4x4_v2	.119
273	pr1_stl10_disentangled_alpha04	.115
274	ReAlnet04_ALL	.115
275	xception	.115
276	imagenet_l2_3_0	.115
277	resnet50_robust_l2_eps3	.115
278	efficientnet_b1_imagenet_full	.115
279	yudixie_resnet50_translation_rotation_0_240908	.108
280	alexnet_training_seed_02	.104
281	resnet50-moclr8deg	.104
282	ReAlnet06_ALL	.094
283	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.094
284	AlexNet_SIN_fov12	.085
285	ReAlnet03_ALL	.077
286	ReAlnet01_ALL	.077
287	deit_small_imagenet_full_seed-0	.077
288	resnet50_primary_visual_cortex	.072
289	deit3_base_patch16_224_fb_in22k_ft_in1k
290	convnext_base_fb_in1k
291	resnet50_ewconplus_wzc_seed5_2__323
292	pnasnet_large

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: Ferguson2024half

Metric: value_delta