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