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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	cvt_cvt-13-384-in1k_4_LucyV4	1.0
2	cv_18_dagger_408_pretrained	1.0
3	swin_small_patch4_window7_224:ms_in22k_ft_in1k	1.0
4	efficientnet_b0_imagenet_full	1.0
5	resnext101_32x32d_wsl	1.0
6	mobilenet_v2_0_75_192	1.0
7	compact_resnet50_V4	1.0
8	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
9	yudixie_resnet18_random_0_240719	1.0
10	resnet_152_v2	1.0
11	resnet50_st_wzc_seed6_2_323	1.0
12	ReAlnet05	1.0
13	yudixie_resnet50_random_0_240908	1.0
14	yudixie_resnet50_imagenet1kpret_0_240908	1.0
15	alexnet_training_seed_05	1.0
16	resnet18_ecoset_full	1.0
17	yudixie_resnet50_distance_reg_0_240908	1.0
18	mobilenet_v2_0_5_224	1.0
19	resnet18_imagenet_full	1.0
20	alexnet_training_seed_07	1.0
21	resnet50_imagenet_1_seed-0	1.0
22	ReAlnet05_cornet	1.0
23	yudixie_resnet18_rotation_reg_0_240719	1.0
24	artResNet18_1	1.0
25	convnext_tiny_sup	1.0
26	yudixie_wbh_object_class_scaling_0415_run_3	1.0
27	resnet50_tutorial	.975
28	convnext_base_imagenet_full_seed-0	.975
29	inception_v4	.975
30	resnext101_32x8d_wsl	.975
31	effnetb1_272x240	.975
32	resnet-152_v2_pytorch	.975
33	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.942
34	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.942
35	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.942
36	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.942
37	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.942
38	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.942
39	nasnet_mobile	.942
40	vonegrcnn_47e	.942
41	vonegrcnn_52e_full	.942
42	resnet50_st_wzc_seed4_2_323	.942
43	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.942
44	resnet50_st_wzc_2	.882
45	resnet50_st_wzc_seed3_2_323	.882
46	vit_base_patch16_dinov3-lvd1689m	.882
47	convnext_small_imagenet_1_seed-0	.882
48	convnext_small_imagenet_10_seed-0	.882
49	pnasnet_large_halfPrecision	.882
50	AlexNet_SIN_fov4	.882
51	resnet50_st_wzc_seed5_2_323	.882
52	resnet34_imagenet_full	.852
53	resnet_50_v1_spiking_l4a	.852
54	alexnet_training_seed_04	.852
55	resnet152_ecoset_full	.852
56	resnet50-barlow	.852
57	BiT-S-R152x4	.852
58	voneresnet-50-non_stochastic	.798
59	focalnet_tiny_lrf_in1k	.798
60	efficientnet-b7	.798
61	resnet_50_v1_spiking_l3	.798
62	AdvProp_efficientnet-b8	.798
63	convnext_xlarge:fb_in22k_ft_in1k	.771
64	vit_large_patch14_clip_224:openai_ft_in1k	.771
65	vit_large_patch14_clip_336:laion2b_ft_in1k	.771
66	resnext101_32x48d_wsl	.771
67	ViT_L_32_imagenet1k	.771
68	yudixie_resnet18_category_class_0_240719	.771
69	efficientnet_b0	.771
70	grcnn	.771
71	voneresnet-50-robust	.771
72	AdvProp_efficientnet-b4	.771
73	AdvProp_efficientnet-b2	.771
74	AT_efficientnet-b2	.771
75	vit_base_patch16_clip_224:openai_ft_in1k	.771
76	resnet18_fair	.771
77	convnext_base_fb_in22k_ft_in1k	.771
78	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.771
79	deepeisnn_ei_imagenet_resnet18	.771
80	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.722
81	yudixie_resnet50_category_class_0_240908	.722
82	mobilenet_v2_0_5_192	.722
83	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.722
84	yudixie_resnet18_translation_reg_0_240719	.722
85	AlexNet_SIN_fov16	.722
86	AdvProp_efficientnet-b7	.722
87	resnet50_st_wzc_2_323	.722
88	ReAlnet07	.698
89	vit_large_patch14_clip_224:laion2b_ft_in1k	.698
90	Res2Net50_26w_4s	.698
91	deit_small_imagenet_1_seed-0	.698
92	ReAlnet07_cornet	.698
93	vgg_16	.698
94	omnivore_swinB	.698
95	BiT-S-R101x3	.698
96	densenet-121	.698
97	mobilenet_v2_1_0_160	.653
98	compact_resnet50_IT	.653
99	compact_cornets_V4	.653
100	yudixie_resnet50_object_class_0_240908	.631
101	convnext_large:fb_in22k_ft_in1k	.631
102	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.631
103	convnext_tiny_imagenet_full_seed-0	.631
104	alexnet_fov16	.631
105	yudixie_resnet18_translation_rotation_0_240719	.631
106	yudixie_resnet18_distance_translation_rotation_0_240719	.631
107	compact_vgg19_IT	.631
108	yudixie_resnet18_imagenet1kpret_0_240719	.631
109	ReAlnet01_cornet_fov16	.631
110	convnext_base_fb_in1k_gram_aligned_dinov3	.631
111	alexnet_training_seed_09	.591
112	mobilenet_v2_0_75_160	.591
113	briaai_rmbg_1_4	.591
114	alexnet_training_seed_10	.591
115	resnet50-SIN_IN	.591
116	compact_resnet50_robust_IT	.591
117	inception_v1	.591
118	resnet_152_v1	.571
119	resnet_50_v1_spiking	.571
120	convnext_tiny:in12k_ft_in1k	.571
121	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.571
122	mobilenet_v2_1_0_192	.571
123	pnasnet_large_pytorch	.571
124	ReAlnet02_cornet	.571
125	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.571
126	ReAlnet01_cornet_fov4	.571
127	yudixie_resnet18_distance_rotation_0_240719	.571
128	resnet50_robust_l2_eps1	.571
129	vonegrcnn_62e_nobn	.571
130	BiT-S-R50x3	.571
131	ReAlnet02	.571
132	BiT-S-R50x1	.571
133	VOneCORnet-S	.535
134	mobilenet_v2_1_3_224	.535
135	resnet50-vicregl0p9	.535
136	yudixie_wbh_object_class_scaling_0415_imagenet	.535
137	yudixie_wbh_object_class_scaling_0415_run_1	.535
138	cvt_cvt-21-384-in1k_4_LucyV4	.517
139	alexnet_training_seed_06	.517
140	vit_relpos_base_patch16_clsgap_224:sw_in1k	.517
141	resnet50_ecoset_full	.517
142	alexnet_fov12	.517
143	shufflenet_v2_x1_0	.517
144	resnet50-vicreg	.517
145	vgg_19	.517
146	voneresnet-50	.517
147	hmax	.517
148	resnet50_imagenet_full	.484
149	vit_base_patch14_reg4_dinov2-lvd142m	.484
150	yudixie_resnet18_distance_translation_0_240719	.484
151	nasnet_large	.484
152	compact_cornets_IT	.484
153	alexnet_training_seed_01_fov16	.484
154	alexnet_training_seed_03	.467
155	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.467
156	deit_small_imagenet_10_seed-0	.467
157	deit_large_imagenet_full_seed-0	.467
158	resnet50_imagenet_100_seed-0	.467
159	ReAlnet01_cornet_fov12	.467
160	yudixie_wbh_object_class_scaling_0415_run_0	.467
161	compact_resnet50_robust_V4	.438
162	resnet_101_v2	.438
163	yudixie_wbh_object_class_scaling_0415_run_2	.438
164	deit_base_imagenet_full_seed-0	.423
165	densenet-201	.423
166	mobilenet_v2_1-4_224_pytorch	.423
167	yolos_tiny	.423
168	alexnet	.423
169	mobilenet_v2_1_4_224	.423
170	grcnn_109	.423
171	antialiased-r50	.423
172	resnet_SIN_IN_FT_IN	.423
173	resnet50_st_wzc_noseed_2_323	.423
174	deepeisnn_ei_cifar10_resnet18	.423
175	alexnet_training_seed_01	.396
176	inception_v3	.383
177	ReAlnet08	.383
178	cvt_cvt-13-224-in1k_4_LucyV4	.383
179	convnext_large_imagenet_full_seed-0	.383
180	resnet-50-robust	.383
181	mobilenet_v2_1_0_224	.383
182	ReAlnet08_cornet	.383
183	omnivore_swinS	.383
184	convnext_base:clip_laiona_augreg_ft_in1k_384	.383
185	mobilenet_v2_0_75_224	.358
186	resnet50-vicregl0p75	.358
187	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.346
188	efficientnet_b2_imagenet_full	.346
189	cvt_cvt-21-224-in1k_4_LucyV4	.346
190	convnext_femto_ols:d1_in1k	.346
191	custom_model_cv_18_dagger_408	.324
192	vit_base_patch14_dinov2-lvd142m	.324
193	resnet50-SIN	.324
194	resnet_50_v1	.324
195	resnet50-sup	.324
196	densenet-169	.324
197	predictive_coding_pc	.324
198	yudixie_resnet50_distance_translation_rotation_0_240908	.313
199	convnext_small_imagenet_full_seed-0	.313
200	convnext_small_imagenet_100_seed-0	.313
201	resnet50-SIN_IN_IN	.313
202	ReAlnet07_ALL	.313
203	BiT-S-R101x1	.313
204	resnet50_ewconplus_wzc_seed7_2_323	.313
205	compact_vgg19_V4	.293
206	alexnet_training_seed_01_fov12	.293
207	resnet50_st_wzc_seed7_2_323	.293
208	resnet50_ewconplus_wzc_2_323	.293
209	ReAlnet09	.283
210	resnet101_ecoset_full	.283
211	mobilenet_v2_1_0_128	.283
212	blt_vs	.283
213	resnet-18_test_m	.283
214	yudixie_resnet18_object_class_0_240719	.283
215	yudixie_resnet50_rotation_reg_0_240908	.283
216	resnet50-vitoimagevidnet8	.283
217	ReAlnet09_cornet	.283
218	resnet50-VITO-8deg-cc	.283
219	resnet50_ewconplus_wzc_seed2_2__323	.283
220	ReAlnet01_cornet	.256
221	yudixie_resnet50_distance_translation_0_240908	.256
222	ReAlnet01	.256
223	alexnet_fov4	.240
224	BiT-S-R152x2	.240
225	AlexNet_SIN	.232
226	aughost_fair_resnet18	.232
227	yudixie_resnet50_distance_rotation_0_240908	.232
228	ReAlnet05_ALL	.232
229	ReAlnet08_ALL	.232
230	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.232
231	resnet_50_v1_spiking_l4	.217
232	resnet101_imagenet_full	.217
233	deit3_base_patch16_224_fb_in1k	.217
234	AdvProp_efficientnet-b6	.210
235	resnet152_imagenet_full	.196
236	alexnet_training_seed_01_fov4	.196
237	ReAlnet10_ALL	.190
238	resnet_50_v2	.172
239	ReAlnet06	.172
240	ReAlnet10	.172
241	resnet34_ecoset_full	.172
242	yudixie_resnet50_translation_reg_0_240908	.172
243	ReAlnet06_cornet	.172
244	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.172
245	ReAlnet10_cornet	.172
246	deepeisnn_ei_cifar10_vgg19	.172
247	ReAlnet03	.155
248	ReAlnet03_cornet	.155
249	resnet50_imagenet_10_seed-0	.155
250	yudixie_resnet18_distance_reg_0_240719	.155
251	ReAlnet02_ALL	.155
252	resnet_101_v1	.155
253	ReAlnet09_ALL	.155
254	alexnet_training_seed_08	.145
255	deit_small_imagenet_100_seed-0	.141
256	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.141
257	resnet50_ewconplus_wzc	.132
258	resnet50_ewconplus_noseed_2_323	.132
259	ReAlnet04_cornet	.127
260	ReAlnet04	.127
261	efficientnet_b1_imagenet_full	.115
262	xception	.115
263	ReAlnet04_ALL	.115
264	resnet50_robust_l2_eps3	.115
265	imagenet_l2_3_0	.115
266	pr1_stl10_disentangled_alpha04	.115
267	yudixie_resnet50_translation_rotation_0_240908	.108
268	alexnet_training_seed_02	.104
269	resnet50-moclr8deg	.104
270	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.094
271	ReAlnet06_ALL	.094
272	AlexNet_SIN_fov12	.085
273	deit_small_imagenet_full_seed-0	.077
274	ReAlnet01_ALL	.077
275	ReAlnet03_ALL	.077
276	resnet50_primary_visual_cortex	.072
277	resnet50_ewconplus_wzc_seed5_2__323
278	pnasnet_large
279	convnext_base_fb_in1k
280	deit3_base_patch16_224_fb_in22k_ft_in1k

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