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