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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024round_v-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	ReAlnet01_cornet	1.0
2	mobilenet_v2_0_75_192	1.0
3	grcnn	1.0
4	ReAlnet01	1.0
5	voneresnet-50-robust	1.0
6	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
7	resnet50_ecoset_full	1.0
8	vit_large_patch14_clip_224:openai_ft_in12k_in1k	1.0
9	yudixie_resnet18_object_class_0_240719	1.0
10	yudixie_resnet18_distance_translation_0_240719	1.0
11	resnet152_ecoset_full	1.0
12	alexnet_training_seed_01_fov4	1.0
13	ReAlnet08_cornet	1.0
14	resnet_152_v2	1.0
15	nasnet_large	1.0
16	ReAlnet08	1.0
17	resnet50-SIN	1.0
18	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	1.0
19	convnext_femto_ols:d1_in1k	1.0
20	ReAlnet01_cornet_fov4	1.0
21	yudixie_resnet50_category_class_0_240908	1.0
22	alexnet_training_seed_04	1.0
23	ReAlnet06_cornet	1.0
24	ReAlnet04_cornet	1.0
25	inception_v4	1.0
26	ReAlnet06	1.0
27	ReAlnet04	1.0
28	Res2Net50_26w_4s	1.0
29	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
30	convnext_small_imagenet_10_seed-0	1.0
31	resnet34_ecoset_full	1.0
32	ReAlnet01_ALL	.924
33	mobilenet_v2_1_4_224	.924
34	mobilenet_v2_0_5_224	.924
35	omnivore_swinB	.924
36	convnext_xlarge:fb_in22k_ft_in1k	.924
37	convnext_base_imagenet_full_seed-0	.924
38	resnet101_imagenet_full	.924
39	mobilenet_v2_1-4_224_pytorch	.924
40	vit_base_patch16_dinov3-lvd1689m	.905
41	compact_vgg19_IT	.905
42	voneresnet-50	.905
43	efficientnet-b7	.905
44	vit_large_patch14_clip_336:laion2b_ft_in1k	.905
45	convnext_tiny_imagenet_full_seed-0	.905
46	AlexNet_SIN_fov12	.836
47	ReAlnet09_cornet	.836
48	nasnet_mobile	.836
49	inception_v1	.836
50	ReAlnet09	.836
51	deit_small_imagenet_100_seed-0	.836
52	deit_large_imagenet_full_seed-0	.836
53	resnet101_ecoset_full	.836
54	resnet50-vicreg	.820
55	vit_large_patch14_clip_224:openai_ft_in1k	.820
56	blt_vs	.820
57	yudixie_resnet18_distance_reg_0_240719	.820
58	resnet152_imagenet_full	.820
59	resnet18_imagenet_full	.820
60	focalnet_tiny_lrf_in1k	.820
61	resnext101_32x32d_wsl	.820
62	yudixie_resnet50_distance_reg_0_240908	.757
63	alexnet_training_seed_10	.757
64	vgg_16	.757
65	vonegrcnn_62e_nobn	.757
66	resnet34_imagenet_full	.757
67	ViT_L_32_imagenet1k	.757
68	ReAlnet01_cornet_fov12	.742
69	alexnet_training_seed_01	.742
70	ReAlnet05_cornet	.742
71	ReAlnet05	.742
72	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.742
73	ReAlnet10_ALL	.686
74	ReAlnet05_ALL	.686
75	ReAlnet02_ALL	.686
76	shufflenet_v2_x1_0	.686
77	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.686
78	AlexNet_SIN_fov4	.672
79	alexnet_fov4	.672
80	ReAlnet07_ALL	.672
81	resnet_50_v1_spiking_l3	.672
82	resnet_50_v2	.672
83	vgg_19	.672
84	efficientnet_b0	.672
85	AdvProp_efficientnet-b7	.672
86	efficientnet_b2_imagenet_full	.672
87	custom_model_cv_18_dagger_408	.672
88	ReAlnet08_ALL	.621
89	ReAlnet03_ALL	.621
90	inception_v3	.621
91	omnivore_swinS	.621
92	densenet-169	.621
93	AdvProp_efficientnet-b4	.621
94	convnext_small_imagenet_full_seed-0	.621
95	deit_small_imagenet_10_seed-0	.621
96	deit_base_imagenet_full_seed-0	.621
97	resnet18_ecoset_full	.621
98	resnext101_32x48d_wsl	.621
99	compact_cornets_IT	.609
100	compact_cornets_V4	.609
101	yudixie_resnet50_distance_translation_rotation_0_240908	.609
102	ReAlnet07_cornet	.609
103	ReAlnet03_cornet	.609
104	grcnn_109	.609
105	ReAlnet07	.609
106	ReAlnet03	.609
107	vonegrcnn_47e	.609
108	voneresnet-50-non_stochastic	.609
109	alexnet_fov12	.562
110	convnext_tiny_sup	.562
111	AdvProp_efficientnet-b8	.562
112	yudixie_resnet18_imagenet1kpret_0_240719	.562
113	alexnet_training_seed_05	.551
114	ReAlnet02_cornet	.551
115	ReAlnet02	.551
116	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.551
117	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.551
118	resnet_SIN_IN_FT_IN	.551
119	yudixie_resnet18_translation_reg_0_240719	.551
120	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.551
121	resnet-50-robust	.551
122	aughost_fair_resnet18	.509
123	alexnet_training_seed_03	.509
124	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.509
125	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.509
126	cvt_cvt-21-384-in1k_4_LucyV4	.509
127	hmax	.509
128	compact_resnet50_robust_IT	.499
129	resnet_50_v1_spiking_l4a	.499
130	mobilenet_v2_0_75_224	.499
131	BiT-S-R50x1	.499
132	convnext_small_imagenet_1_seed-0	.499
133	cv_18_dagger_408_pretrained	.461
134	ReAlnet06_ALL	.461
135	vit_base_patch16_clip_224:openai_ft_in1k	.461
136	convnext_large_imagenet_full_seed-0	.461
137	resnet50_imagenet_100_seed-0	.461
138	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.461
139	resnet50_primary_visual_cortex	.417
140	resnet-18_test_m	.417
141	resnet50-SIN_IN	.417
142	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.417
143	convnext_base:clip_laiona_augreg_ft_in1k_384	.417
144	cvt_cvt-13-224-in1k_4_LucyV4	.417
145	vit_base_patch14_dinov2-lvd142m	.409
146	mobilenet_v2_1_3_224	.409
147	resnet_152_v1	.409
148	resnet_101_v2	.409
149	resnet_50_v1	.409
150	mobilenet_v2_0_75_160	.409
151	resnet50-vitoimagevidnet8	.409
152	resnet50-VITO-8deg-cc	.409
153	vit_relpos_base_patch16_clsgap_224:sw_in1k	.409
154	resnet50-sup	.409
155	yudixie_resnet18_distance_rotation_0_240719	.409
156	ReAlnet09_ALL	.378
157	VOneCORnet-S	.378
158	BiT-S-R50x3	.378
159	resnet50_robust_l2_eps3	.378
160	imagenet_l2_3_0	.378
161	effnetb1_272x240	.378
162	AlexNet_SIN_fov16	.370
163	briaai_rmbg_1_4	.370
164	xception	.370
165	ReAlnet10_cornet	.370
166	ReAlnet10	.370
167	resnet50-SIN_IN_IN	.370
168	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.370
169	convnext_large:fb_in22k_ft_in1k	.370
170	resnet50_imagenet_full	.370
171	resnet50-vicregl0p9	.342
172	artResNet18_1	.342
173	cvt_cvt-13-384-in1k_4_LucyV4	.342
174	AlexNet_SIN	.335
175	resnet50_ewconplus_wzc	.335
176	yudixie_resnet50_object_class_0_240908	.335
177	yudixie_resnet50_random_0_240908	.335
178	yudixie_resnet50_distance_translation_0_240908	.310
179	alexnet_training_seed_01_fov16	.304
180	compact_resnet50_IT	.304
181	yudixie_resnet50_distance_rotation_0_240908	.304
182	resnet50-moclr8deg	.304
183	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.281
184	efficientnet_b0_imagenet_full	.281
185	ReAlnet01_cornet_fov16	.275
186	yudixie_resnet50_imagenet1kpret_0_240908	.275
187	mobilenet_v2_1_0_224	.275
188	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.275
189	densenet-121	.275
190	resnet50_imagenet_10_seed-0	.275
191	vit_base_patch14_reg4_dinov2-lvd142m	.254
192	densenet-201	.254
193	AdvProp_efficientnet-b6	.254
194	deit_small_imagenet_full_seed-0	.254
195	alexnet_training_seed_01_fov12	.249
196	resnet50_robust_l2_eps1	.249
197	BiT-S-R152x2	.249
198	deit_small_imagenet_1_seed-0	.249
199	alexnet	.249
200	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.230
201	ReAlnet04_ALL	.230
202	resnet50-barlow	.230
203	AT_efficientnet-b2	.230
204	BiT-S-R152x4	.230
205	AdvProp_efficientnet-b2	.230
206	vonegrcnn_52e_full	.230
207	resnet_50_v1_spiking_l4	.225
208	alexnet_training_seed_08	.225
209	alexnet_training_seed_02	.225
210	alexnet_training_seed_09	.225
211	resnet_101_v1	.225
212	yudixie_resnet50_translation_rotation_0_240908	.225
213	yudixie_resnet18_distance_translation_rotation_0_240719	.225
214	yudixie_resnet18_translation_rotation_0_240719	.225
215	mobilenet_v2_1_0_128	.208
216	resnet-152_v2_pytorch	.208
217	compact_vgg19_V4	.204
218	mobilenet_v2_1_0_192	.204
219	mobilenet_v2_1_0_160	.204
220	antialiased-r50	.204
221	mobilenet_v2_0_5_192	.189
222	alexnet_training_seed_06	.185
223	yudixie_resnet50_rotation_reg_0_240908	.185
224	convnext_tiny:in12k_ft_in1k	.185
225	cvt_cvt-21-224-in1k_4_LucyV4	.171
226	alexnet_fov16	.167
227	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.155
228	resnet50-vicregl0p75	.155
229	efficientnet_b1_imagenet_full	.140
230	alexnet_training_seed_07	.137
231	resnet50_tutorial	.137
232	pnasnet_large_pytorch	.124
233	resnext101_32x8d_wsl	.102
234	pnasnet_large_halfPrecision	.083
235	BiT-S-R101x3	.083
236	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.076
237	convnext_small_imagenet_100_seed-0	.070
238	yudixie_resnet18_rotation_reg_0_240719	.068
239	yudixie_resnet18_category_class_0_240719	.068
240	resnet50_imagenet_1_seed-0	.068
241	pnasnet_large	.062
242	compact_resnet50_robust_V4	.051
243	compact_resnet50_V4	.051
244	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.035
245	yudixie_resnet50_translation_reg_0_240908	.034
246	resnet_50_v1_spiking	.025
247	yolos_tiny	.025
248	BiT-S-R101x1	.025
249	yudixie_resnet18_random_0_240719	.023

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

0.85.

Note that scores are relative to this ceiling.

Data: Ferguson2024round_v

Metric: value_delta