Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024lle-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet50_imagenet_1_seed-0	1.0
2	AdvProp_efficientnet-b8	1.0
3	ReAlnet10	1.0
4	alexnet_training_seed_03	1.0
5	vit_large_patch14_clip_336:openai_ft_in12k_in1k	1.0
6	ReAlnet10_cornet	1.0
7	mobilenet_v2_0_5_224	1.0
8	alexnet_training_seed_08	1.0
9	resnet50-vicregl0p9	1.0
10	resnext101_32x48d_wsl	1.0
11	mobilenet_v2_1_0_160	1.0
12	deit_small_imagenet_10_seed-0	1.0
13	resnet50_tutorial	1.0
14	densenet-169	1.0
15	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	1.0
16	resnet50_robust_l2_eps1	1.0
17	focalnet_tiny_lrf_in1k	1.0
18	resnet50-vicregl0p75	1.0
19	ReAlnet08	1.0
20	ReAlnet08_cornet	1.0
21	vgg_16	1.0
22	yudixie_resnet50_random_0_240908	1.0
23	resnet152_imagenet_full	1.0
24	cvt_cvt-21-384-in1k_4_LucyV4	1.0
25	vgg_19	1.0
26	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	1.0
27	yudixie_resnet50_distance_rotation_0_240908	1.0
28	resnet50-SIN_IN	1.0
29	vit_base_patch16_clip_224:openai_ft_in1k	1.0
30	deit_base_imagenet_full_seed-0	1.0
31	shufflenet_v2_x1_0	.967
32	mobilenet_v2_1_3_224	.967
33	omnivore_swinS	.967
34	inception_v1	.911
35	alexnet_training_seed_01	.911
36	BiT-S-R152x2	.911
37	efficientnet_b0	.911
38	yudixie_resnet50_imagenet1kpret_0_240908	.911
39	resnet152_ecoset_full	.911
40	Res2Net50_26w_4s	.911
41	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.875
42	resnext101_32x32d_wsl	.875
43	yudixie_resnet50_distance_translation_rotation_0_240908	.875
44	alexnet_training_seed_02	.875
45	yudixie_resnet50_rotation_reg_0_240908	.875
46	artResNet18_1	.825
47	ReAlnet04	.825
48	convnext_tiny_imagenet_full_seed-0	.825
49	resnet-50-robust	.825
50	ReAlnet04_cornet	.825
51	AT_efficientnet-b2	.793
52	convnext_xlarge:fb_in22k_ft_in1k	.793
53	AdvProp_efficientnet-b2	.793
54	inception_v3	.747
55	efficientnet_b1_imagenet_full	.747
56	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.747
57	resnet34_ecoset_full	.747
58	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.747
59	mobilenet_v2_0_75_224	.718
60	yudixie_resnet18_random_0_240719	.718
61	BiT-S-R101x1	.718
62	voneresnet-50-non_stochastic	.718
63	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.718
64	mobilenet_v2_1_0_192	.676
65	cvt_cvt-21-224-in1k_4_LucyV4	.676
66	cvt_cvt-13-224-in1k_4_LucyV4	.676
67	resnet_50_v1	.650
68	AdvProp_efficientnet-b6	.650
69	resnet_50_v2	.650
70	resnet50-sup	.650
71	resnet50_imagenet_full	.612
72	nasnet_mobile	.612
73	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.612
74	resnet50-vicreg	.588
75	yudixie_resnet18_translation_reg_0_240719	.554
76	resnet34_imagenet_full	.554
77	vit_large_patch14_clip_336:laion2b_ft_in1k	.554
78	vit_relpos_base_patch16_clsgap_224:sw_in1k	.554
79	resnet50-SIN_IN_IN	.554
80	resnet-18_test_m	.532
81	vonegrcnn_62e_nobn	.532
82	ReAlnet06_cornet	.532
83	BiT-S-R50x1	.532
84	efficientnet-b7	.532
85	ReAlnet06	.532
86	convnext_tiny:in12k_ft_in1k	.532
87	alexnet_training_seed_06	.532
88	yudixie_resnet18_distance_translation_0_240719	.502
89	blt_vs	.502
90	VOneCORnet-S	.502
91	alexnet_training_seed_09	.502
92	antialiased-r50	.502
93	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.502
94	mobilenet_v2_0_75_192	.502
95	custom_model_cv_18_dagger_408	.502
96	ReAlnet07	.482
97	ReAlnet07_cornet	.482
98	resnext101_32x8d_wsl	.454
99	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.454
100	voneresnet-50	.454
101	resnet50-barlow	.454
102	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.454
103	efficientnet_b2_imagenet_full	.436
104	ReAlnet01_cornet	.411
105	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.411
106	convnext_base:clip_laiona_augreg_ft_in1k_384	.411
107	alexnet_training_seed_05	.411
108	ReAlnet_incorrect	.411
109	resnet_SIN_IN_FT_IN	.411
110	ReAlnet01	.411
111	yudixie_resnet18_category_class_0_240719	.411
112	alexnet_training_seed_10	.411
113	voneresnet-50-robust	.395
114	resnet_152_v2	.372
115	resnet101_ecoset_full	.372
116	yudixie_resnet18_distance_translation_rotation_0_240719	.372
117	resnet-152_v2_pytorch	.372
118	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.372
119	inception_v4	.358
120	ReAlnet03_cornet	.358
121	yudixie_resnet50_translation_rotation_0_240908	.358
122	ReAlnet03	.358
123	mobilenet_v2_1_0_128	.337
124	resnet50-moclr8deg	.337
125	convnext_large:fb_in22k_ft_in1k	.337
126	yudixie_resnet18_translation_rotation_0_240719	.337
127	BiT-S-R50x3	.337
128	yudixie_resnet18_distance_reg_0_240719	.337
129	ReAlnet05_cornet	.337
130	ReAlnet05	.337
131	deit_small_imagenet_1_seed-0	.337
132	vonegrcnn_47e	.324
133	BiT-S-R152x4	.324
134	resnet18_ecoset_full	.305
135	pnasnet_large	.305
136	vonegrcnn_52e_full	.305
137	BiT-S-R101x3	.305
138	omnivore_swinB	.293
139	vit_large_patch14_clip_224:openai_ft_in1k	.276
140	alexnet_training_seed_04	.276
141	nasnet_large	.276
142	cvt_cvt-13-384-in1k_4_LucyV4	.276
143	resnet50_imagenet_100_seed-0	.276
144	mobilenet_v2_0_5_192	.276
145	convnext_tiny_sup	.265
146	yudixie_resnet50_object_class_0_240908	.250
147	yudixie_resnet50_category_class_0_240908	.250
148	deit_small_imagenet_100_seed-0	.250
149	convnext_small_imagenet_full_seed-0	.226
150	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.226
151	vit_large_patch14_clip_224:laion2b_ft_in1k	.226
152	mobilenet_v2_0_75_160	.226
153	yudixie_resnet18_object_class_0_240719	.205
154	mobilenet_v2_1-4_224_pytorch	.205
155	yudixie_resnet50_distance_translation_0_240908	.205
156	yudixie_resnet18_imagenet1kpret_0_240719	.205
157	grcnn	.205
158	mobilenet_v2_1_4_224	.205
159	resnet_101_v1	.186
160	resnet_101_v2	.178
161	effnetb1_272x240	.168
162	AdvProp_efficientnet-b7	.168
163	convnext_small_imagenet_100_seed-0	.168
164	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.168
165	resnet_152_v1	.161
166	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.152
167	xception	.152
168	resnet50_robust_l2_eps3	.152
169	imagenet_l2_3_0	.152
170	yudixie_resnet18_rotation_reg_0_240719	.146
171	ReAlnet09_cornet	.138
172	ReAlnet09	.138
173	yudixie_resnet18_distance_rotation_0_240719	.138
174	resnet50-vitoimagevidnet8	.125
175	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.125
176	resnet50_imagenet_10_seed-0	.125
177	yudixie_resnet50_distance_reg_0_240908	.125
178	resnet50-VITO-8deg-cc	.125
179	resnet18_imagenet_full	.113
180	convnext_femto_ols:d1_in1k	.113
181	convnext_large_imagenet_full_seed-0	.113
182	convnext_base_imagenet_full_seed-0	.113
183	densenet-201	.108
184	ViT_L_32_imagenet1k	.108
185	pnasnet_large_pytorch	.092
186	ReAlnet02_cornet	.069
187	resnet50_ecoset_full	.069
188	ReAlnet02	.069
189	yudixie_resnet50_translation_reg_0_240908	.069
190	convnext_small_imagenet_10_seed-0	.069
191	efficientnet_b0_imagenet_full	.069
192	convnext_small_imagenet_1_seed-0	.062
193	resnet50-SIN	.062
194	grcnn_109	.062
195	AdvProp_efficientnet-b4	.056
196	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.051
197	alexnet_training_seed_07	.051
198	densenet-121	.046
199	deit_small_imagenet_full_seed-0	.046
200	deit_large_imagenet_full_seed-0	.025
201	resnet101_imagenet_full	.023
202	mobilenet_v2_1_0_224	X
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

0.83.

Note that scores are relative to this ceiling.

Data: Ferguson2024lle

Metric: value_delta