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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	yudixie_wbh_object_class_scaling_0415_run_3	1.0
14	yudixie_resnet50_distance_reg_0_240908	1.0
15	yudixie_resnet50_imagenet1kpret_0_240908	1.0
16	yudixie_resnet50_random_0_240908	1.0
17	alexnet_training_seed_07	1.0
18	alexnet_training_seed_05	1.0
19	ReAlnet05_cornet	1.0
20	mobilenet_v2_0_5_224	1.0
21	ReAlnet05	1.0
22	convnext_tiny_sup	1.0
23	artResNet18_1	1.0
24	yudixie_resnet18_rotation_reg_0_240719	1.0
25	resnet50_imagenet_1_seed-0	1.0
26	resnet18_ecoset_full	1.0
27	resnet18_imagenet_full	1.0
28	dinov2_vitl14_reg_lc	.975
29	dinov2_vitg14_reg_lc	.975
30	inception_v4	.975
31	convnext_base_imagenet_full_seed-0	.975
32	resnet50_tutorial	.975
33	resnet-152_v2_pytorch	.975
34	effnetb1_272x240	.975
35	resnext101_32x8d_wsl	.975
36	vit_large_clip336_openai	.942
37	dinov2_vitl14_reg_lc_featread	.942
38	dinov2_vitg14_lc	.942
39	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.942
40	nasnet_mobile	.942
41	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.942
42	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.942
43	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.942
44	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.942
45	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.942
46	vonegrcnn_52e_full	.942
47	vonegrcnn_47e	.942
48	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.942
49	resnet50_st_wzc_seed3_2_323	.882
50	vit_base_patch16_dinov3-lvd1689m	.882
51	pnasnet_large_halfPrecision	.882
52	AlexNet_SIN_fov4	.882
53	resnet50_st_wzc_2	.882
54	convnext_small_imagenet_10_seed-0	.882
55	convnext_small_imagenet_1_seed-0	.882
56	convnext_xxlarge_clip	.852
57	resnet_50_v1_spiking_l4a	.852
58	alexnet_training_seed_04	.852
59	resnet50-barlow	.852
60	BiT-S-R152x4	.852
61	resnet152_ecoset_full	.852
62	resnet34_imagenet_full	.852
63	convnext_large_in22k_supervised	.798
64	resnet_50_v1_spiking_l3	.798
65	efficientnet-b7	.798
66	AdvProp_efficientnet-b8	.798
67	focalnet_tiny_lrf_in1k	.798
68	voneresnet-50-non_stochastic	.798
69	eva02_large_in22k	.771
70	deepeisnn_ei_imagenet_resnet18	.771
71	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.771
72	convnext_base_fb_in22k_ft_in1k	.771
73	resnet18_fair	.771
74	grcnn	.771
75	voneresnet-50-robust	.771
76	vit_large_patch14_clip_336:laion2b_ft_in1k	.771
77	vit_large_patch14_clip_224:openai_ft_in1k	.771
78	vit_base_patch16_clip_224:openai_ft_in1k	.771
79	efficientnet_b0	.771
80	convnext_xlarge:fb_in22k_ft_in1k	.771
81	AT_efficientnet-b2	.771
82	AdvProp_efficientnet-b2	.771
83	AdvProp_efficientnet-b4	.771
84	yudixie_resnet18_category_class_0_240719	.771
85	ViT_L_32_imagenet1k	.771
86	resnext101_32x48d_wsl	.771
87	AlexNet_SIN_fov16	.722
88	yudixie_resnet50_category_class_0_240908	.722
89	mobilenet_v2_0_5_192	.722
90	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.722
91	AdvProp_efficientnet-b7	.722
92	yudixie_resnet18_translation_reg_0_240719	.722
93	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.722
94	vit_huge_clip336_laion	.698
95	ReAlnet07_cornet	.698
96	vgg_16	.698
97	ReAlnet07	.698
98	Res2Net50_26w_4s	.698
99	omnivore_swinB	.698
100	vit_large_patch14_clip_224:laion2b_ft_in1k	.698
101	densenet-121	.698
102	BiT-S-R101x3	.698
103	deit_small_imagenet_1_seed-0	.698
104	dinov2_vitl14_lc_res336	.653
105	compact_cornets_V4	.653
106	compact_resnet50_IT	.653
107	mobilenet_v2_1_0_160	.653
108	convnext_base_fb_in1k_gram_aligned_dinov3	.631
109	ReAlnet01_cornet_fov16	.631
110	alexnet_fov16	.631
111	compact_vgg19_IT	.631
112	yudixie_resnet50_object_class_0_240908	.631
113	convnext_large:fb_in22k_ft_in1k	.631
114	yudixie_resnet18_imagenet1kpret_0_240719	.631
115	yudixie_resnet18_distance_translation_rotation_0_240719	.631
116	yudixie_resnet18_translation_rotation_0_240719	.631
117	convnext_tiny_imagenet_full_seed-0	.631
118	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.631
119	compact_resnet50_robust_IT	.591
120	briaai_rmbg_1_4	.591
121	alexnet_training_seed_10	.591
122	alexnet_training_seed_09	.591
123	mobilenet_v2_0_75_160	.591
124	inception_v1	.591
125	resnet50-SIN_IN	.591
126	ReAlnet01_cornet_fov4	.571
127	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.571
128	resnet_50_v1_spiking	.571
129	ReAlnet02_cornet	.571
130	mobilenet_v2_1_0_192	.571
131	resnet_152_v1	.571
132	vonegrcnn_62e_nobn	.571
133	ReAlnet02	.571
134	resnet50_robust_l2_eps1	.571
135	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.571
136	convnext_tiny:in12k_ft_in1k	.571
137	BiT-S-R50x1	.571
138	BiT-S-R50x3	.571
139	yudixie_resnet18_distance_rotation_0_240719	.571
140	pnasnet_large_pytorch	.571
141	yudixie_wbh_object_class_scaling_0415_run_1	.535
142	yudixie_wbh_object_class_scaling_0415_imagenet	.535
143	resnet50_hvs_wzc	.535
144	mobilenet_v2_1_3_224	.535
145	VOneCORnet-S	.535
146	resnet50-vicregl0p9	.535
147	alexnet_fov12	.517
148	alexnet_training_seed_06	.517
149	vgg_19	.517
150	voneresnet-50	.517
151	resnet50-vicreg	.517
152	shufflenet_v2_x1_0	.517
153	vit_relpos_base_patch16_clsgap_224:sw_in1k	.517
154	cvt_cvt-21-384-in1k_4_LucyV4	.517
155	resnet50_ecoset_full	.517
156	hmax	.517
157	vit_base_patch14_reg4_dinov2-lvd142m	.484
158	alexnet_training_seed_01_fov16	.484
159	compact_cornets_IT	.484
160	nasnet_large	.484
161	yudixie_resnet18_distance_translation_0_240719	.484
162	resnet50_imagenet_full	.484
163	yudixie_wbh_object_class_scaling_0415_run_0	.467
164	ReAlnet01_cornet_fov12	.467
165	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.467
166	alexnet_training_seed_03	.467
167	deit_small_imagenet_10_seed-0	.467
168	deit_large_imagenet_full_seed-0	.467
169	resnet50_imagenet_100_seed-0	.467
170	yudixie_wbh_object_class_scaling_0415_run_2	.438
171	compact_resnet50_robust_V4	.438
172	resnet_101_v2	.438
173	deepeisnn_ei_cifar10_resnet18	.423
174	yolos_tiny	.423
175	mobilenet_v2_1_4_224	.423
176	grcnn_109	.423
177	densenet-201	.423
178	resnet_SIN_IN_FT_IN	.423
179	antialiased-r50	.423
180	deit_base_imagenet_full_seed-0	.423
181	mobilenet_v2_1-4_224_pytorch	.423
182	alexnet	.423
183	alexnet_training_seed_01	.396
184	mobilenet_v2_1_0_224	.383
185	ReAlnet08_cornet	.383
186	inception_v3	.383
187	ReAlnet08	.383
188	omnivore_swinS	.383
189	convnext_base:clip_laiona_augreg_ft_in1k_384	.383
190	convnext_large_imagenet_full_seed-0	.383
191	cvt_cvt-13-224-in1k_4_LucyV4	.383
192	resnet-50-robust	.383
193	mobilenet_v2_0_75_224	.358
194	resnet50-vicregl0p75	.358
195	convnext_femto_ols:d1_in1k	.346
196	efficientnet_b2_imagenet_full	.346
197	cvt_cvt-21-224-in1k_4_LucyV4	.346
198	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.346
199	predictive_coding_pc	.324
200	vit_base_patch14_dinov2-lvd142m	.324
201	resnet_50_v1	.324
202	resnet50-SIN	.324
203	densenet-169	.324
204	resnet50-sup	.324
205	custom_model_cv_18_dagger_408	.324
206	ReAlnet07_ALL	.313
207	yudixie_resnet50_distance_translation_rotation_0_240908	.313
208	resnet50-SIN_IN_IN	.313
209	BiT-S-R101x1	.313
210	convnext_small_imagenet_100_seed-0	.313
211	convnext_small_imagenet_full_seed-0	.313
212	resnet50_st_wzc_seed7_2_323	.293
213	alexnet_training_seed_01_fov12	.293
214	compact_vgg19_V4	.293
215	resnet50_ewconplus_wzc_seed2_2__323	.283
216	ReAlnet09_cornet	.283
217	mobilenet_v2_1_0_128	.283
218	yudixie_resnet50_rotation_reg_0_240908	.283
219	ReAlnet09	.283
220	resnet50-vitoimagevidnet8	.283
221	resnet-18_test_m	.283
222	resnet50-VITO-8deg-cc	.283
223	blt_vs	.283
224	yudixie_resnet18_object_class_0_240719	.283
225	resnet101_ecoset_full	.283
226	ReAlnet01_cornet	.256
227	yudixie_resnet50_distance_translation_0_240908	.256
228	ReAlnet01	.256
229	alexnet_fov4	.240
230	BiT-S-R152x2	.240
231	aughost_fair_resnet18	.232
232	AlexNet_SIN	.232
233	ReAlnet08_ALL	.232
234	ReAlnet05_ALL	.232
235	yudixie_resnet50_distance_rotation_0_240908	.232
236	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.232
237	deit3_base_patch16_224_fb_in1k	.217
238	resnet_50_v1_spiking_l4	.217
239	resnet101_imagenet_full	.217
240	AdvProp_efficientnet-b6	.210
241	alexnet_training_seed_01_fov4	.196
242	resnet152_imagenet_full	.196
243	ReAlnet10_ALL	.190
244	deepeisnn_ei_cifar10_vgg19	.172
245	ReAlnet10_cornet	.172
246	ReAlnet06_cornet	.172
247	resnet_50_v2	.172
248	yudixie_resnet50_translation_reg_0_240908	.172
249	ReAlnet10	.172
250	ReAlnet06	.172
251	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.172
252	resnet34_ecoset_full	.172
253	ReAlnet09_ALL	.155
254	ReAlnet02_ALL	.155
255	ReAlnet03_cornet	.155
256	resnet_101_v1	.155
257	ReAlnet03	.155
258	yudixie_resnet18_distance_reg_0_240719	.155
259	resnet50_imagenet_10_seed-0	.155
260	alexnet_training_seed_08	.145
261	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.141
262	deit_small_imagenet_100_seed-0	.141
263	resnet50_ewconplus_wzc	.132
264	ReAlnet04_cornet	.127
265	ReAlnet04	.127
266	pr1_stl10_disentangled_alpha04	.115
267	ReAlnet04_ALL	.115
268	xception	.115
269	imagenet_l2_3_0	.115
270	resnet50_robust_l2_eps3	.115
271	efficientnet_b1_imagenet_full	.115
272	yudixie_resnet50_translation_rotation_0_240908	.108
273	alexnet_training_seed_02	.104
274	resnet50-moclr8deg	.104
275	ReAlnet06_ALL	.094
276	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.094
277	AlexNet_SIN_fov12	.085
278	ReAlnet03_ALL	.077
279	ReAlnet01_ALL	.077
280	deit_small_imagenet_full_seed-0	.077
281	resnet50_primary_visual_cortex	.072
282	dinov2_vitl14_lc
283	deit3_base_patch16_224_fb_in22k_ft_in1k
284	convnext_base_fb_in1k
285	resnet50_ewconplus_wzc_seed5_2__323
286	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