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