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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	ReAlnet10_ALL	1.0
2	ReAlnet10	1.0
3	alexnet_training_seed_08	1.0
4	mobilenet_v2_0_5_224	1.0
5	resnet50_imagenet_1_seed-0	1.0
6	vit_large_patch14_clip_336:openai_ft_in12k_in1k	1.0
7	ReAlnet10_cornet	1.0
8	AdvProp_efficientnet-b8	1.0
9	alexnet_training_seed_03	1.0
10	mobilenet_v2_1_0_160	1.0
11	deit_small_imagenet_10_seed-0	1.0
12	resnext101_32x48d_wsl	1.0
13	resnet50_tutorial	1.0
14	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	1.0
15	resnet50-vicregl0p9	1.0
16	densenet-169	1.0
17	ReAlnet08	1.0
18	focalnet_tiny_lrf_in1k	1.0
19	resnet50_robust_l2_eps1	1.0
20	ReAlnet08_cornet	1.0
21	vgg_16	1.0
22	ReAlnet05_ALL	1.0
23	yudixie_resnet50_random_0_240908	1.0
24	resnet50-vicregl0p75	1.0
25	cvt_cvt-21-384-in1k_4_LucyV4	1.0
26	resnet152_imagenet_full	1.0
27	deit_base_imagenet_full_seed-0	1.0
28	vit_base_patch16_clip_224:openai_ft_in1k	1.0
29	resnet50-SIN_IN	1.0
30	yudixie_resnet50_distance_rotation_0_240908	1.0
31	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	1.0
32	vgg_19	1.0
33	omnivore_swinS	.967
34	shufflenet_v2_x1_0	.967
35	mobilenet_v2_1_3_224	.967
36	ReAlnet07_ALL	.967
37	resnet152_ecoset_full	.911
38	alexnet_training_seed_01	.911
39	Res2Net50_26w_4s	.911
40	efficientnet_b0	.911
41	inception_v1	.911
42	yudixie_resnet50_imagenet1kpret_0_240908	.911
43	BiT-S-R152x2	.911
44	alexnet_training_seed_02	.875
45	resnext101_32x32d_wsl	.875
46	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.875
47	yudixie_resnet50_rotation_reg_0_240908	.875
48	yudixie_resnet50_distance_translation_rotation_0_240908	.875
49	convnext_tiny_imagenet_full_seed-0	.825
50	briaai_rmbg_1_4	.825
51	resnet-50-robust	.825
52	ReAlnet04_cornet	.825
53	artResNet18_1	.825
54	ReAlnet04	.825
55	convnext_xlarge:fb_in22k_ft_in1k	.793
56	AdvProp_efficientnet-b2	.793
57	AT_efficientnet-b2	.793
58	efficientnet_b1_imagenet_full	.747
59	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.747
60	inception_v3	.747
61	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.747
62	resnet34_ecoset_full	.747
63	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.718
64	mobilenet_v2_0_75_224	.718
65	voneresnet-50-non_stochastic	.718
66	yudixie_resnet18_random_0_240719	.718
67	BiT-S-R101x1	.718
68	mobilenet_v2_1_0_192	.676
69	cvt_cvt-21-224-in1k_4_LucyV4	.676
70	cvt_cvt-13-224-in1k_4_LucyV4	.676
71	AdvProp_efficientnet-b6	.650
72	resnet_50_v1	.650
73	resnet50-sup	.650
74	resnet_50_v2	.650
75	ReAlnet08_ALL	.650
76	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.612
77	resnet50_imagenet_full	.612
78	yolos_tiny	.612
79	nasnet_mobile	.612
80	resnet_50_v1_spiking	.588
81	resnet_50_v1_spiking_l4a	.588
82	resnet50-vicreg	.588
83	resnet34_imagenet_full	.554
84	vit_large_patch14_clip_336:laion2b_ft_in1k	.554
85	vit_relpos_base_patch16_clsgap_224:sw_in1k	.554
86	resnet50-SIN_IN_IN	.554
87	yudixie_resnet18_translation_reg_0_240719	.554
88	ReAlnet02_ALL	.554
89	ReAlnet03_ALL	.554
90	ReAlnet06	.532
91	convnext_tiny:in12k_ft_in1k	.532
92	alexnet_training_seed_06	.532
93	resnet-18_test_m	.532
94	ReAlnet06_cornet	.532
95	efficientnet-b7	.532
96	vonegrcnn_62e_nobn	.532
97	BiT-S-R50x1	.532
98	resnet_50_v1_spiking_l4	.502
99	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.502
100	mobilenet_v2_0_75_192	.502
101	blt_vs	.502
102	custom_model_cv_18_dagger_408	.502
103	VOneCORnet-S	.502
104	yudixie_resnet18_distance_translation_0_240719	.502
105	ReAlnet01_ALL	.502
106	ReAlnet04_ALL	.502
107	antialiased-r50	.502
108	alexnet_training_seed_09	.502
109	ReAlnet07	.482
110	ReAlnet07_cornet	.482
111	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.454
112	resnext101_32x8d_wsl	.454
113	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.454
114	resnet50-barlow	.454
115	voneresnet-50	.454
116	ReAlnet09_ALL	.454
117	efficientnet_b2_imagenet_full	.436
118	resnet50_primary_visual_cortex	.436
119	convnext_base:clip_laiona_augreg_ft_in1k_384	.411
120	alexnet_training_seed_05	.411
121	alexnet_training_seed_10	.411
122	ReAlnet01_cornet	.411
123	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.411
124	yudixie_resnet18_category_class_0_240719	.411
125	ReAlnet01	.411
126	ReAlnet06_ALL	.411
127	resnet_SIN_IN_FT_IN	.411
128	voneresnet-50-robust	.395
129	resnet-152_v2_pytorch	.372
130	resnet101_ecoset_full	.372
131	yudixie_resnet18_distance_translation_rotation_0_240719	.372
132	resnet_152_v2	.372
133	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.372
134	ReAlnet03	.358
135	ReAlnet03_cornet	.358
136	inception_v4	.358
137	yudixie_resnet50_translation_rotation_0_240908	.358
138	mobilenet_v2_1_0_128	.337
139	ReAlnet05	.337
140	yudixie_resnet18_translation_rotation_0_240719	.337
141	ReAlnet05_cornet	.337
142	yudixie_resnet18_distance_reg_0_240719	.337
143	deit_small_imagenet_1_seed-0	.337
144	resnet50-moclr8deg	.337
145	BiT-S-R50x3	.337
146	convnext_large:fb_in22k_ft_in1k	.337
147	vonegrcnn_47e	.324
148	BiT-S-R152x4	.324
149	resnet18_ecoset_full	.305
150	pnasnet_large	.305
151	vonegrcnn_52e_full	.305
152	BiT-S-R101x3	.305
153	omnivore_swinB	.293
154	vit_large_patch14_clip_224:openai_ft_in1k	.276
155	alexnet_training_seed_04	.276
156	mobilenet_v2_0_5_192	.276
157	resnet50_imagenet_100_seed-0	.276
158	cvt_cvt-13-384-in1k_4_LucyV4	.276
159	resnet_50_v1_spiking_l3	.276
160	nasnet_large	.276
161	convnext_tiny_sup	.265
162	yudixie_resnet50_category_class_0_240908	.250
163	deit_small_imagenet_100_seed-0	.250
164	yudixie_resnet50_object_class_0_240908	.250
165	vit_large_patch14_clip_224:laion2b_ft_in1k	.226
166	mobilenet_v2_0_75_160	.226
167	convnext_small_imagenet_full_seed-0	.226
168	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.226
169	mobilenet_v2_1-4_224_pytorch	.205
170	yudixie_resnet18_object_class_0_240719	.205
171	yudixie_resnet50_distance_translation_0_240908	.205
172	yudixie_resnet18_imagenet1kpret_0_240719	.205
173	grcnn	.205
174	mobilenet_v2_1_4_224	.205
175	resnet_101_v1	.186
176	resnet_101_v2	.178
177	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.168
178	convnext_small_imagenet_100_seed-0	.168
179	effnetb1_272x240	.168
180	AdvProp_efficientnet-b7	.168
181	resnet_152_v1	.161
182	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.152
183	xception	.152
184	imagenet_l2_3_0	.152
185	resnet50_robust_l2_eps3	.152
186	yudixie_resnet18_rotation_reg_0_240719	.146
187	ReAlnet09	.138
188	yudixie_resnet18_distance_rotation_0_240719	.138
189	ReAlnet09_cornet	.138
190	yudixie_resnet50_distance_reg_0_240908	.125
191	resnet50_imagenet_10_seed-0	.125
192	resnet50-VITO-8deg-cc	.125
193	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.125
194	resnet50-vitoimagevidnet8	.125
195	convnext_large_imagenet_full_seed-0	.113
196	convnext_base_imagenet_full_seed-0	.113
197	resnet18_imagenet_full	.113
198	convnext_femto_ols:d1_in1k	.113
199	densenet-201	.108
200	ViT_L_32_imagenet1k	.108
201	pnasnet_large_pytorch	.092
202	convnext_small_imagenet_10_seed-0	.069
203	efficientnet_b0_imagenet_full	.069
204	ReAlnet02_cornet	.069
205	resnet50_ecoset_full	.069
206	yudixie_resnet50_translation_reg_0_240908	.069
207	ReAlnet02	.069
208	convnext_small_imagenet_1_seed-0	.062
209	resnet50-SIN	.062
210	grcnn_109	.062
211	AdvProp_efficientnet-b4	.056
212	alexnet_training_seed_07	.051
213	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.051
214	deit_small_imagenet_full_seed-0	.046
215	densenet-121	.046
216	deit_large_imagenet_full_seed-0	.025
217	resnet101_imagenet_full	.023
218	hmax
219	mobilenet_v2_1_0_224

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