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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	ReAlnet01_cornet_fov12	.967
34	omnivore_swinS	.967
35	shufflenet_v2_x1_0	.967
36	mobilenet_v2_1_3_224	.967
37	ReAlnet07_ALL	.967
38	resnet152_ecoset_full	.911
39	alexnet_training_seed_01	.911
40	Res2Net50_26w_4s	.911
41	efficientnet_b0	.911
42	inception_v1	.911
43	yudixie_resnet50_imagenet1kpret_0_240908	.911
44	BiT-S-R152x2	.911
45	alexnet_training_seed_02	.875
46	AlexNet_SIN	.875
47	resnext101_32x32d_wsl	.875
48	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.875
49	yudixie_resnet50_rotation_reg_0_240908	.875
50	yudixie_resnet50_distance_translation_rotation_0_240908	.875
51	convnext_tiny_imagenet_full_seed-0	.825
52	briaai_rmbg_1_4	.825
53	resnet-50-robust	.825
54	ReAlnet04_cornet	.825
55	artResNet18_1	.825
56	ReAlnet04	.825
57	convnext_xlarge:fb_in22k_ft_in1k	.793
58	AdvProp_efficientnet-b2	.793
59	AT_efficientnet-b2	.793
60	efficientnet_b1_imagenet_full	.747
61	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.747
62	inception_v3	.747
63	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.747
64	resnet34_ecoset_full	.747
65	compact_resnet50_robust_IT	.747
66	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.718
67	mobilenet_v2_0_75_224	.718
68	alexnet_fov4	.718
69	voneresnet-50-non_stochastic	.718
70	yudixie_resnet18_random_0_240719	.718
71	BiT-S-R101x1	.718
72	mobilenet_v2_1_0_192	.676
73	cvt_cvt-21-224-in1k_4_LucyV4	.676
74	cvt_cvt-13-224-in1k_4_LucyV4	.676
75	compact_vgg19_V4	.676
76	AdvProp_efficientnet-b6	.650
77	resnet_50_v1	.650
78	resnet50-sup	.650
79	resnet_50_v2	.650
80	ReAlnet08_ALL	.650
81	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.612
82	resnet50_imagenet_full	.612
83	yolos_tiny	.612
84	nasnet_mobile	.612
85	resnet_50_v1_spiking	.588
86	resnet_50_v1_spiking_l4a	.588
87	resnet50-vicreg	.588
88	resnet34_imagenet_full	.554
89	vit_large_patch14_clip_336:laion2b_ft_in1k	.554
90	vit_relpos_base_patch16_clsgap_224:sw_in1k	.554
91	vit_base_patch16_dinov3-lvd1689m	.554
92	resnet50-SIN_IN_IN	.554
93	yudixie_resnet18_translation_reg_0_240719	.554
94	ReAlnet02_ALL	.554
95	compact_cornets_IT	.554
96	ReAlnet03_ALL	.554
97	ReAlnet06	.532
98	convnext_tiny:in12k_ft_in1k	.532
99	alexnet_training_seed_06	.532
100	resnet-18_test_m	.532
101	ReAlnet06_cornet	.532
102	efficientnet-b7	.532
103	vonegrcnn_62e_nobn	.532
104	BiT-S-R50x1	.532
105	resnet_50_v1_spiking_l4	.502
106	cv_18_dagger_408_pretrained	.502
107	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.502
108	mobilenet_v2_0_75_192	.502
109	blt_vs	.502
110	custom_model_cv_18_dagger_408	.502
111	VOneCORnet-S	.502
112	yudixie_resnet18_distance_translation_0_240719	.502
113	ReAlnet01_ALL	.502
114	ReAlnet04_ALL	.502
115	antialiased-r50	.502
116	alexnet_training_seed_09	.502
117	ReAlnet07	.482
118	aughost_fair_resnet18	.482
119	ReAlnet07_cornet	.482
120	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.454
121	resnext101_32x8d_wsl	.454
122	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.454
123	compact_resnet50_IT	.454
124	compact_vgg19_IT	.454
125	resnet50-barlow	.454
126	voneresnet-50	.454
127	ReAlnet09_ALL	.454
128	efficientnet_b2_imagenet_full	.436
129	compact_resnet50_V4	.436
130	resnet50_primary_visual_cortex	.436
131	convnext_base:clip_laiona_augreg_ft_in1k_384	.411
132	alexnet_training_seed_05	.411
133	alexnet_training_seed_10	.411
134	ReAlnet01_cornet	.411
135	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.411
136	yudixie_resnet18_category_class_0_240719	.411
137	ReAlnet01	.411
138	ReAlnet06_ALL	.411
139	resnet_SIN_IN_FT_IN	.411
140	voneresnet-50-robust	.395
141	resnet-152_v2_pytorch	.372
142	alexnet_fov16	.372
143	resnet101_ecoset_full	.372
144	yudixie_resnet18_distance_translation_rotation_0_240719	.372
145	resnet_152_v2	.372
146	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.372
147	ReAlnet03	.358
148	ReAlnet03_cornet	.358
149	inception_v4	.358
150	yudixie_resnet50_translation_rotation_0_240908	.358
151	mobilenet_v2_1_0_128	.337
152	ReAlnet05	.337
153	vit_base_patch14_dinov2-lvd142m	.337
154	yudixie_resnet18_translation_rotation_0_240719	.337
155	ReAlnet05_cornet	.337
156	yudixie_resnet18_distance_reg_0_240719	.337
157	deit_small_imagenet_1_seed-0	.337
158	resnet50-moclr8deg	.337
159	BiT-S-R50x3	.337
160	convnext_large:fb_in22k_ft_in1k	.337
161	vonegrcnn_47e	.324
162	BiT-S-R152x4	.324
163	resnet18_ecoset_full	.305
164	pnasnet_large	.305
165	AlexNet_SIN_fov12	.305
166	vonegrcnn_52e_full	.305
167	BiT-S-R101x3	.305
168	omnivore_swinB	.293
169	vit_large_patch14_clip_224:openai_ft_in1k	.276
170	alexnet_training_seed_04	.276
171	mobilenet_v2_0_5_192	.276
172	resnet50_imagenet_100_seed-0	.276
173	cvt_cvt-13-384-in1k_4_LucyV4	.276
174	resnet_50_v1_spiking_l3	.276
175	nasnet_large	.276
176	AlexNet_SIN_fov4	.276
177	compact_cornets_V4	.265
178	convnext_tiny_sup	.265
179	resnet50_ewconplus_wzc	.250
180	yudixie_resnet50_category_class_0_240908	.250
181	deit_small_imagenet_100_seed-0	.250
182	ReAlnet01_cornet_fov16	.250
183	AlexNet_SIN_fov16	.250
184	yudixie_resnet50_object_class_0_240908	.250
185	alexnet	.250
186	vit_large_patch14_clip_224:laion2b_ft_in1k	.226
187	pnasnet_large_halfPrecision	.226
188	mobilenet_v2_0_75_160	.226
189	convnext_small_imagenet_full_seed-0	.226
190	vit_base_patch14_reg4_dinov2-lvd142m	.226
191	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.226
192	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.205
193	mobilenet_v2_1-4_224_pytorch	.205
194	yudixie_resnet18_object_class_0_240719	.205
195	yudixie_resnet50_distance_translation_0_240908	.205
196	yudixie_resnet18_imagenet1kpret_0_240719	.205
197	grcnn	.205
198	mobilenet_v2_1_4_224	.205
199	alexnet_training_seed_01_fov12	.205
200	compact_resnet50_robust_V4	.197
201	resnet_101_v1	.186
202	resnet_101_v2	.178
203	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.168
204	convnext_small_imagenet_100_seed-0	.168
205	effnetb1_272x240	.168
206	AdvProp_efficientnet-b7	.168
207	resnet_152_v1	.161
208	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.152
209	xception	.152
210	imagenet_l2_3_0	.152
211	resnet50_robust_l2_eps3	.152
212	yudixie_resnet18_rotation_reg_0_240719	.146
213	ReAlnet09	.138
214	alexnet_fov12	.138
215	yudixie_resnet18_distance_rotation_0_240719	.138
216	ReAlnet09_cornet	.138
217	yudixie_resnet50_distance_reg_0_240908	.125
218	resnet50_imagenet_10_seed-0	.125
219	resnet50-VITO-8deg-cc	.125
220	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.125
221	resnet50-vitoimagevidnet8	.125
222	convnext_large_imagenet_full_seed-0	.113
223	convnext_base_imagenet_full_seed-0	.113
224	resnet18_imagenet_full	.113
225	convnext_femto_ols:d1_in1k	.113
226	densenet-201	.108
227	ViT_L_32_imagenet1k	.108
228	pnasnet_large_pytorch	.092
229	ReAlnet01_cornet_fov4	.092
230	alexnet_training_seed_01_fov16	.092
231	convnext_small_imagenet_10_seed-0	.069
232	efficientnet_b0_imagenet_full	.069
233	ReAlnet02_cornet	.069
234	resnet50_ecoset_full	.069
235	yudixie_resnet50_translation_reg_0_240908	.069
236	ReAlnet02	.069
237	convnext_small_imagenet_1_seed-0	.062
238	resnet50-SIN	.062
239	grcnn_109	.062
240	AdvProp_efficientnet-b4	.056
241	alexnet_training_seed_07	.051
242	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.051
243	alexnet_training_seed_01_fov4	.049
244	deit_small_imagenet_full_seed-0	.046
245	densenet-121	.046
246	deit_large_imagenet_full_seed-0	.025
247	resnet101_imagenet_full	.023
248	hmax
249	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