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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	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
2	mobilenet_v2_0_75_192	1.0
3	ReAlnet01_cornet	1.0
4	resnet50_ecoset_full	1.0
5	grcnn	1.0
6	voneresnet-50-robust	1.0
7	ReAlnet01	1.0
8	vit_large_patch14_clip_224:openai_ft_in12k_in1k	1.0
9	resnet152_ecoset_full	1.0
10	yudixie_resnet18_distance_translation_0_240719	1.0
11	yudixie_resnet18_object_class_0_240719	1.0
12	ReAlnet08	1.0
13	resnet50-SIN	1.0
14	nasnet_large	1.0
15	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	1.0
16	ReAlnet08_cornet	1.0
17	alexnet_training_seed_01_fov4	1.0
18	resnet_152_v2	1.0
19	convnext_femto_ols:d1_in1k	1.0
20	convnext_small_imagenet_10_seed-0	1.000
21	ReAlnet06	1.000
22	yudixie_resnet50_category_class_0_240908	1.000
23	alexnet_training_seed_04	1.000
24	inception_v4	1.000
25	resnet34_ecoset_full	1.000
26	ReAlnet01_cornet_fov4	1.000
27	vit_large_patch14_clip_224:laion2b_ft_in1k	1.000
28	ReAlnet04_cornet	1.000
29	Res2Net50_26w_4s	1.000
30	ReAlnet06_cornet	1.000
31	ReAlnet04	1.000
32	convnext_xlarge:fb_in22k_ft_in1k	.924
33	convnext_base_imagenet_full_seed-0	.924
34	resnet101_imagenet_full	.924
35	mobilenet_v2_0_5_224	.924
36	mobilenet_v2_1-4_224_pytorch	.924
37	ReAlnet01_ALL	.924
38	omnivore_swinB	.924
39	mobilenet_v2_1_4_224	.924
40	vit_large_patch14_clip_336:laion2b_ft_in1k	.905
41	convnext_tiny_imagenet_full_seed-0	.905
42	efficientnet-b7	.905
43	compact_vgg19_IT	.905
44	voneresnet-50	.905
45	deit_small_imagenet_100_seed-0	.836
46	deit_large_imagenet_full_seed-0	.836
47	ReAlnet09	.836
48	resnet101_ecoset_full	.836
49	AlexNet_SIN_fov12	.836
50	nasnet_mobile	.836
51	ReAlnet09_cornet	.836
52	inception_v1	.836
53	vit_large_patch14_clip_224:openai_ft_in1k	.820
54	resnet18_imagenet_full	.820
55	resnext101_32x32d_wsl	.820
56	focalnet_tiny_lrf_in1k	.820
57	resnet152_imagenet_full	.820
58	blt_vs	.820
59	yudixie_resnet18_distance_reg_0_240719	.820
60	resnet50-vicreg	.820
61	resnet34_imagenet_full	.757
62	yudixie_resnet50_distance_reg_0_240908	.757
63	ViT_L_32_imagenet1k	.757
64	alexnet_training_seed_10	.757
65	vgg_16	.757
66	vonegrcnn_62e_nobn	.757
67	ReAlnet05	.742
68	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.742
69	ReAlnet01_cornet_fov12	.742
70	alexnet_training_seed_01	.742
71	ReAlnet05_cornet	.742
72	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.686
73	shufflenet_v2_x1_0	.686
74	ReAlnet02_ALL	.686
75	ReAlnet05_ALL	.686
76	ReAlnet10_ALL	.686
77	alexnet_fov4	.672
78	efficientnet_b2_imagenet_full	.672
79	custom_model_cv_18_dagger_408	.672
80	efficientnet_b0	.672
81	resnet_50_v1_spiking_l3	.672
82	AlexNet_SIN_fov4	.672
83	vgg_19	.672
84	AdvProp_efficientnet-b7	.672
85	ReAlnet07_ALL	.672
86	resnet_50_v2	.672
87	inception_v3	.621
88	convnext_small_imagenet_full_seed-0	.621
89	deit_small_imagenet_10_seed-0	.621
90	deit_base_imagenet_full_seed-0	.621
91	resnet18_ecoset_full	.621
92	ReAlnet03_ALL	.621
93	omnivore_swinS	.621
94	AdvProp_efficientnet-b4	.621
95	ReAlnet08_ALL	.621
96	densenet-169	.621
97	resnext101_32x48d_wsl	.621
98	yudixie_resnet50_distance_translation_rotation_0_240908	.609
99	ReAlnet03	.609
100	ReAlnet07	.609
101	voneresnet-50-non_stochastic	.609
102	ReAlnet03_cornet	.609
103	ReAlnet07_cornet	.609
104	compact_cornets_V4	.609
105	compact_cornets_IT	.609
106	grcnn_109	.609
107	vonegrcnn_47e	.609
108	alexnet_fov12	.562
109	yudixie_resnet18_imagenet1kpret_0_240719	.562
110	AdvProp_efficientnet-b8	.562
111	convnext_tiny_sup	.562
112	alexnet_training_seed_05	.551
113	resnet-50-robust	.551
114	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.551
115	ReAlnet02_cornet	.551
116	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.551
117	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.551
118	yudixie_resnet18_translation_reg_0_240719	.551
119	resnet_SIN_IN_FT_IN	.551
120	ReAlnet02	.551
121	alexnet_training_seed_03	.509
122	cvt_cvt-21-384-in1k_4_LucyV4	.509
123	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.509
124	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.509
125	hmax	.509
126	mobilenet_v2_0_75_224	.499
127	resnet_50_v1_spiking_l4a	.499
128	convnext_small_imagenet_1_seed-0	.499
129	compact_resnet50_robust_IT	.499
130	BiT-S-R50x1	.499
131	convnext_large_imagenet_full_seed-0	.461
132	cv_18_dagger_408_pretrained	.461
133	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.461
134	resnet50_imagenet_100_seed-0	.461
135	vit_base_patch16_clip_224:openai_ft_in1k	.461
136	ReAlnet06_ALL	.461
137	convnext_base:clip_laiona_augreg_ft_in1k_384	.417
138	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.417
139	cvt_cvt-13-224-in1k_4_LucyV4	.417
140	resnet50-SIN_IN	.417
141	resnet-18_test_m	.417
142	resnet50_primary_visual_cortex	.417
143	mobilenet_v2_0_75_160	.409
144	yudixie_resnet18_distance_rotation_0_240719	.409
145	vit_relpos_base_patch16_clsgap_224:sw_in1k	.409
146	resnet50-vitoimagevidnet8	.409
147	mobilenet_v2_1_3_224	.409
148	resnet50-VITO-8deg-cc	.409
149	resnet_50_v1	.409
150	resnet_101_v2	.409
151	resnet50-sup	.409
152	resnet_152_v1	.409
153	effnetb1_272x240	.378
154	VOneCORnet-S	.378
155	imagenet_l2_3_0	.378
156	resnet50_robust_l2_eps3	.378
157	BiT-S-R50x3	.378
158	ReAlnet09_ALL	.378
159	resnet50_imagenet_full	.370
160	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.370
161	ReAlnet10	.370
162	briaai_rmbg_1_4	.370
163	resnet50-SIN_IN_IN	.370
164	xception	.370
165	AlexNet_SIN_fov16	.370
166	ReAlnet10_cornet	.370
167	convnext_large:fb_in22k_ft_in1k	.370
168	cvt_cvt-13-384-in1k_4_LucyV4	.342
169	artResNet18_1	.342
170	resnet50-vicregl0p9	.342
171	yudixie_resnet50_random_0_240908	.335
172	yudixie_resnet50_object_class_0_240908	.335
173	resnet50_ewconplus_wzc	.335
174	AlexNet_SIN	.335
175	yudixie_resnet50_distance_translation_0_240908	.310
176	compact_resnet50_IT	.304
177	yudixie_resnet50_distance_rotation_0_240908	.304
178	resnet50-moclr8deg	.304
179	alexnet_training_seed_01_fov16	.304
180	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.281
181	efficientnet_b0_imagenet_full	.281
182	yudixie_resnet50_imagenet1kpret_0_240908	.275
183	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.275
184	resnet50_imagenet_10_seed-0	.275
185	ReAlnet01_cornet_fov16	.275
186	mobilenet_v2_1_0_224	.275
187	densenet-121	.275
188	deit_small_imagenet_full_seed-0	.254
189	densenet-201	.254
190	AdvProp_efficientnet-b6	.254
191	alexnet	.249
192	deit_small_imagenet_1_seed-0	.249
193	resnet50_robust_l2_eps1	.249
194	alexnet_training_seed_01_fov12	.249
195	BiT-S-R152x2	.249
196	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.230
197	resnet50-barlow	.230
198	ReAlnet04_ALL	.230
199	vonegrcnn_52e_full	.230
200	AdvProp_efficientnet-b2	.230
201	BiT-S-R152x4	.230
202	AT_efficientnet-b2	.230
203	alexnet_training_seed_09	.225
204	resnet_50_v1_spiking_l4	.225
205	alexnet_training_seed_02	.225
206	alexnet_training_seed_08	.225
207	yudixie_resnet18_translation_rotation_0_240719	.225
208	yudixie_resnet18_distance_translation_rotation_0_240719	.225
209	yudixie_resnet50_translation_rotation_0_240908	.225
210	resnet_101_v1	.225
211	mobilenet_v2_1_0_128	.208
212	resnet-152_v2_pytorch	.208
213	mobilenet_v2_1_0_160	.204
214	mobilenet_v2_1_0_192	.204
215	compact_vgg19_V4	.204
216	antialiased-r50	.204
217	mobilenet_v2_0_5_192	.189
218	convnext_tiny:in12k_ft_in1k	.185
219	alexnet_training_seed_06	.185
220	yudixie_resnet50_rotation_reg_0_240908	.185
221	cvt_cvt-21-224-in1k_4_LucyV4	.171
222	alexnet_fov16	.167
223	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.155
224	resnet50-vicregl0p75	.155
225	efficientnet_b1_imagenet_full	.140
226	resnet50_tutorial	.137
227	alexnet_training_seed_07	.137
228	pnasnet_large_pytorch	.124
229	resnext101_32x8d_wsl	.102
230	BiT-S-R101x3	.083
231	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.076
232	convnext_small_imagenet_100_seed-0	.070
233	resnet50_imagenet_1_seed-0	.068
234	yudixie_resnet18_category_class_0_240719	.068
235	yudixie_resnet18_rotation_reg_0_240719	.068
236	pnasnet_large	.062
237	compact_resnet50_V4	.051
238	compact_resnet50_robust_V4	.051
239	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.035
240	yudixie_resnet50_translation_reg_0_240908	.034
241	resnet_50_v1_spiking	.025
242	yolos_tiny	.025
243	BiT-S-R101x1	.025
244	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