Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024llh-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	alexnet_training_seed_06	1.0
2	resnet50-SIN_IN_IN	1.0
3	alexnet_training_seed_09	1.0
4	efficientnet_b1_imagenet_full	1.0
5	ViT_L_32_imagenet1k	1.0
6	convnext_xxlarge:clip_laion2b_soup_ft_in1k	1.0
7	deit_small_imagenet_10_seed-0	1.0
8	alexnet_training_seed_10	1.0
9	vit_large_patch14_clip_224:openai_ft_in1k	1.0
10	convnext_femto_ols:d1_in1k	1.0
11	resnet50_imagenet_100_seed-0	1.0
12	deit_base_imagenet_full_seed-0	1.0
13	mobilenet_v2_1_0_128	1.0
14	mobilenet_v2_1_0_160	1.0
15	xception	1.0
16	alexnet_training_seed_02	1.0
17	resnet50-moclr8deg	1.0
18	resnet-152_v2_pytorch	1.0
19	resnet34_imagenet_full	1.0
20	efficientnet-b7	1.0
21	resnet152_imagenet_full	1.0
22	alexnet_training_seed_01	1.0
23	yudixie_resnet50_object_class_0_240908	1.0
24	cvt_cvt-w24-384-in22k_finetuned-in1k_4	1.0
25	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
26	resnet50_tutorial	1.0
27	mobilenet_v2_1_0_192	1.0
28	vgg_16	1.0
29	cvt_cvt-13-384-in1k_4_LucyV4	1.0
30	efficientnet_b0_imagenet_full	1.0
31	cvt_cvt-21-224-in1k_4_LucyV4	.979
32	AdvProp_efficientnet-b8	.979
33	vonegrcnn_52e_full	.951
34	antialiased-r50	.951
35	resnet152_ecoset_full	.951
36	AdvProp_efficientnet-b4	.951
37	yudixie_resnet18_category_class_0_240719	.951
38	pnasnet_large_pytorch	.951
39	blt_vs	.951
40	yudixie_resnet18_distance_rotation_0_240719	.888
41	AdvProp_efficientnet-b7	.888
42	BiT-S-R50x3	.888
43	BiT-S-R50x1	.888
44	mobilenet_v2_1_0_224	.888
45	mobilenet_v2_0_5_224	.888
46	yudixie_resnet50_imagenet1kpret_0_240908	.862
47	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.862
48	yudixie_resnet18_distance_translation_0_240719	.862
49	AT_efficientnet-b2	.862
50	focalnet_tiny_lrf_in1k	.862
51	efficientnet_b2_imagenet_full	.862
52	AdvProp_efficientnet-b2	.862
53	deit_small_imagenet_100_seed-0	.805
54	resnet18_ecoset_full	.805
55	resnext101_32x32d_wsl	.805
56	inception_v1	.805
57	yudixie_resnet18_object_class_0_240719	.805
58	mobilenet_v2_0_75_224	.805
59	artResNet18_1	.782
60	resnet50_robust_l2_eps1	.782
61	resnet18_imagenet_full	.782
62	resnet50-vicregl0p9	.782
63	BiT-S-R152x4	.782
64	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.782
65	omnivore_swinB	.730
66	inception_v3	.730
67	mobilenet_v2_0_75_192	.730
68	resnet50_imagenet_1_seed-0	.730
69	yudixie_resnet18_rotation_reg_0_240719	.730
70	yudixie_resnet18_translation_rotation_0_240719	.709
71	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.709
72	resnet50-VITO-8deg-cc	.709
73	omnivore_swinS	.709
74	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.709
75	efficientnet_b0	.709
76	convnext_tiny_sup	.709
77	resnet50-vitoimagevidnet8	.709
78	resnext101_32x48d_wsl	.709
79	BiT-S-R101x1	.709
80	mobilenet_v2_1_3_224	.662
81	resnet_SIN_IN_FT_IN	.662
82	yudixie_resnet18_random_0_240719	.643
83	mobilenet_v2_0_5_192	.643
84	resnet_101_v1	.643
85	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.643
86	convnext_xlarge:fb_in22k_ft_in1k	.643
87	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.643
88	alexnet_training_seed_05	.601
89	BiT-S-R152x2	.601
90	densenet-169	.583
91	alexnet_training_seed_08	.583
92	VOneCORnet-S	.583
93	inception_v4	.583
94	yudixie_resnet50_translation_reg_0_240908	.583
95	resnet_152_v1	.583
96	BiT-S-R101x3	.583
97	shufflenet_v2_x1_0	.583
98	AdvProp_efficientnet-b6	.545
99	yudixie_resnet18_distance_translation_rotation_0_240719	.529
100	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.529
101	yudixie_resnet50_distance_reg_0_240908	.529
102	mobilenet_v2_0_75_160	.529
103	mobilenet_v2_1_4_224	.529
104	mobilenet_v2_1-4_224_pytorch	.529
105	vonegrcnn_62e_nobn	.529
106	vonegrcnn_47e	.529
107	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.529
108	resnet50-vicregl0p75	.529
109	resnet50-vicreg	.529
110	resnet-18_test_m	.529
111	alexnet_training_seed_04	.494
112	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.494
113	resnet_50_v1	.480
114	densenet-201	.480
115	resnet50-sup	.480
116	yudixie_resnet50_translation_rotation_0_240908	.448
117	yudixie_resnet18_distance_reg_0_240719	.448
118	nasnet_mobile	.435
119	yudixie_resnet50_category_class_0_240908	.435
120	resnet50_ecoset_full	.435
121	cvt_cvt-21-384-in1k_4_LucyV4	.435
122	grcnn	.435
123	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.406
124	resnet_152_v2	.406
125	convnext_small_imagenet_10_seed-0	.406
126	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.395
127	yudixie_resnet50_distance_rotation_0_240908	.395
128	custom_model_cv_18_dagger_408	.369
129	alexnet_training_seed_07	.369
130	resnet50_imagenet_10_seed-0	.369
131	resnet50-SIN	.358
132	yudixie_resnet50_rotation_reg_0_240908	.358
133	yudixie_resnet50_random_0_240908	.358
134	effnetb1_272x240	.358
135	deit_small_imagenet_full_seed-0	.325
136	vit_base_patch16_clip_224:openai_ft_in1k	.325
137	imagenet_l2_3_0	.325
138	resnet50_robust_l2_eps3	.325
139	grcnn_109	.325
140	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.303
141	densenet-121	.294
142	convnext_small_imagenet_full_seed-0	.294
143	yudixie_resnet18_translation_reg_0_240719	.294
144	Res2Net50_26w_4s	.294
145	deit_large_imagenet_full_seed-0	.275
146	convnext_base_imagenet_full_seed-0	.267
147	convnext_tiny:in12k_ft_in1k	.267
148	resnet_50_v2	.267
149	ReAlnet06_cornet	.242
150	resnet_101_v2	.242
151	ReAlnet06	.242
152	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.242
153	resnet50-barlow	.242
154	deit_small_imagenet_1_seed-0	.242
155	pnasnet_large	.242
156	resnet50_imagenet_full	.242
157	ReAlnet02	.242
158	ReAlnet02_cornet	.242
159	yudixie_resnet50_distance_translation_0_240908	.242
160	alexnet_training_seed_03	.226
161	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.199
162	resnet101_imagenet_full	.199
163	voneresnet-50-non_stochastic	.199
164	convnext_large_imagenet_full_seed-0	.181
165	yudixie_resnet50_distance_translation_rotation_0_240908	.164
166	nasnet_large	.164
167	resnet50-SIN_IN	.164
168	voneresnet-50	.164
169	ReAlnet09	.164
170	ReAlnet09_cornet	.164
171	cvt_cvt-13-224-in1k_4_LucyV4	.164
172	resnet-50-robust	.164
173	resnet101_ecoset_full	.149
174	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.149
175	vit_large_patch14_clip_336:laion2b_ft_in1k	.135
176	ReAlnet05	.135
177	ReAlnet05_cornet	.135
178	convnext_small_imagenet_1_seed-0	.126
179	vit_large_patch14_clip_224:laion2b_ft_in1k	.122
180	vgg_19	.122
181	voneresnet-50-robust	.111
182	yudixie_resnet18_imagenet1kpret_0_240719	.111
183	convnext_small_imagenet_100_seed-0	.111
184	ReAlnet01_cornet	.083
185	ReAlnet01	.083
186	ReAlnet_incorrect	.083
187	convnext_base:clip_laiona_augreg_ft_in1k_384	.068
188	resnext101_32x8d_wsl	.068
189	convnext_large:fb_in22k_ft_in1k	.068
190	ReAlnet03_cornet	.062
191	ReAlnet03	.062
192	ReAlnet10	.051
193	ReAlnet10_cornet	.051
194	ReAlnet08_cornet	.046
195	resnet34_ecoset_full	.046
196	ReAlnet08	.046
197	ReAlnet07	.042
198	ReAlnet07_cornet	.042
199	convnext_tiny_imagenet_full_seed-0	.034
200	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.031
201	ReAlnet04_cornet	.016
202	ReAlnet04	.016
203	hmax	X

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

0.81.

Note that scores are relative to this ceiling.

Data: Ferguson2024llh

Metric: value_delta