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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	pr1_stl10_disentangled_alpha04	1.0
11	mobilenet_v2_1_0_160	1.0
12	deit_small_imagenet_10_seed-0	1.0
13	resnext101_32x48d_wsl	1.0
14	resnet50_tutorial	1.0
15	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	1.0
16	resnet50-vicregl0p9	1.0
17	densenet-169	1.0
18	ReAlnet08	1.0
19	focalnet_tiny_lrf_in1k	1.0
20	resnet50_robust_l2_eps1	1.0
21	ReAlnet08_cornet	1.0
22	vgg_16	1.0
23	ReAlnet05_ALL	1.0
24	yudixie_resnet50_random_0_240908	1.0
25	resnet50-vicregl0p75	1.0
26	resnet50_ewconplus_wzc_2_323	1.0
27	cvt_cvt-21-384-in1k_4_LucyV4	1.0
28	resnet152_imagenet_full	1.0
29	deit_base_imagenet_full_seed-0	1.0
30	vit_base_patch16_clip_224:openai_ft_in1k	1.0
31	resnet50-SIN_IN	1.0
32	yudixie_resnet50_distance_rotation_0_240908	1.0
33	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	1.0
34	vgg_19	1.0
35	ReAlnet01_cornet_fov12	.967
36	omnivore_swinS	.967
37	shufflenet_v2_x1_0	.967
38	mobilenet_v2_1_3_224	.967
39	ReAlnet07_ALL	.967
40	resnet152_ecoset_full	.911
41	alexnet_training_seed_01	.911
42	Res2Net50_26w_4s	.911
43	efficientnet_b0	.911
44	inception_v1	.911
45	yudixie_resnet50_imagenet1kpret_0_240908	.911
46	BiT-S-R152x2	.911
47	alexnet_training_seed_02	.875
48	AlexNet_SIN	.875
49	resnext101_32x32d_wsl	.875
50	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.875
51	yudixie_resnet50_rotation_reg_0_240908	.875
52	yudixie_resnet50_distance_translation_rotation_0_240908	.875
53	resnet50_st_wzc_2	.825
54	convnext_tiny_imagenet_full_seed-0	.825
55	briaai_rmbg_1_4	.825
56	resnet-50-robust	.825
57	ReAlnet04_cornet	.825
58	artResNet18_1	.825
59	ReAlnet04	.825
60	predictive_coding_pc	.825
61	convnext_base_fb_in1k_gram_aligned_dinov3	.825
62	convnext_xlarge:fb_in22k_ft_in1k	.793
63	AdvProp_efficientnet-b2	.793
64	AT_efficientnet-b2	.793
65	convnext_base_fb_in1k	.793
66	efficientnet_b1_imagenet_full	.747
67	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.747
68	inception_v3	.747
69	resnet50_st_wzc_seed5_2_323	.747
70	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.747
71	resnet34_ecoset_full	.747
72	compact_resnet50_robust_IT	.747
73	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.718
74	mobilenet_v2_0_75_224	.718
75	alexnet_fov4	.718
76	resnet50_st_wzc_seed3_2_323	.718
77	voneresnet-50-non_stochastic	.718
78	yudixie_resnet18_random_0_240719	.718
79	BiT-S-R101x1	.718
80	mobilenet_v2_1_0_192	.676
81	cvt_cvt-21-224-in1k_4_LucyV4	.676
82	cvt_cvt-13-224-in1k_4_LucyV4	.676
83	compact_vgg19_V4	.676
84	deepeisnn_ei_imagenet_resnet18	.676
85	AdvProp_efficientnet-b6	.650
86	resnet_50_v1	.650
87	resnet50-sup	.650
88	resnet_50_v2	.650
89	ReAlnet08_ALL	.650
90	resnet50_st_wzc_noseed_2_323	.650
91	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.612
92	resnet50_imagenet_full	.612
93	yolos_tiny	.612
94	nasnet_mobile	.612
95	resnet_50_v1_spiking	.588
96	resnet_50_v1_spiking_l4a	.588
97	resnet50-vicreg	.588
98	resnet34_imagenet_full	.554
99	vit_large_patch14_clip_336:laion2b_ft_in1k	.554
100	vit_relpos_base_patch16_clsgap_224:sw_in1k	.554
101	vit_base_patch16_dinov3-lvd1689m	.554
102	resnet50-SIN_IN_IN	.554
103	yudixie_resnet18_translation_reg_0_240719	.554
104	ReAlnet02_ALL	.554
105	compact_cornets_IT	.554
106	ReAlnet03_ALL	.554
107	resnet50_st_wzc_seed6_2_323	.554
108	ReAlnet06	.532
109	convnext_tiny:in12k_ft_in1k	.532
110	alexnet_training_seed_06	.532
111	resnet-18_test_m	.532
112	ReAlnet06_cornet	.532
113	efficientnet-b7	.532
114	vonegrcnn_62e_nobn	.532
115	BiT-S-R50x1	.532
116	resnet_50_v1_spiking_l4	.502
117	cv_18_dagger_408_pretrained	.502
118	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.502
119	mobilenet_v2_0_75_192	.502
120	blt_vs	.502
121	custom_model_cv_18_dagger_408	.502
122	VOneCORnet-S	.502
123	yudixie_resnet18_distance_translation_0_240719	.502
124	ReAlnet01_ALL	.502
125	ReAlnet04_ALL	.502
126	antialiased-r50	.502
127	alexnet_training_seed_09	.502
128	ReAlnet07	.482
129	aughost_fair_resnet18	.482
130	ReAlnet07_cornet	.482
131	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.454
132	resnext101_32x8d_wsl	.454
133	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.454
134	compact_resnet50_IT	.454
135	compact_vgg19_IT	.454
136	resnet50-barlow	.454
137	voneresnet-50	.454
138	ReAlnet09_ALL	.454
139	yudixie_wbh_object_class_scaling_0415_run_2	.454
140	efficientnet_b2_imagenet_full	.436
141	compact_resnet50_V4	.436
142	resnet50_primary_visual_cortex	.436
143	convnext_base:clip_laiona_augreg_ft_in1k_384	.411
144	alexnet_training_seed_05	.411
145	alexnet_training_seed_10	.411
146	ReAlnet01_cornet	.411
147	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.411
148	yudixie_resnet18_category_class_0_240719	.411
149	ReAlnet01	.411
150	ReAlnet06_ALL	.411
151	resnet_SIN_IN_FT_IN	.411
152	deit3_base_patch16_224_fb_in1k	.411
153	voneresnet-50-robust	.395
154	resnet-152_v2_pytorch	.372
155	alexnet_fov16	.372
156	resnet101_ecoset_full	.372
157	yudixie_resnet18_distance_translation_rotation_0_240719	.372
158	resnet_152_v2	.372
159	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.372
160	ReAlnet03	.358
161	ReAlnet03_cornet	.358
162	inception_v4	.358
163	yudixie_resnet50_translation_rotation_0_240908	.358
164	mobilenet_v2_1_0_128	.337
165	ReAlnet05	.337
166	vit_base_patch14_dinov2-lvd142m	.337
167	yudixie_resnet18_translation_rotation_0_240719	.337
168	ReAlnet05_cornet	.337
169	yudixie_resnet18_distance_reg_0_240719	.337
170	deit_small_imagenet_1_seed-0	.337
171	resnet50-moclr8deg	.337
172	BiT-S-R50x3	.337
173	convnext_large:fb_in22k_ft_in1k	.337
174	resnet50_st_wzc_seed7_2_323	.337
175	yudixie_wbh_object_class_scaling_0415_run_3	.337
176	vonegrcnn_47e	.324
177	BiT-S-R152x4	.324
178	resnet18_ecoset_full	.305
179	pnasnet_large	.305
180	AlexNet_SIN_fov12	.305
181	vonegrcnn_52e_full	.305
182	BiT-S-R101x3	.305
183	resnet50_ewconplus_wzc_seed5_2__323	.305
184	deit3_base_patch16_224_fb_in22k_ft_in1k	.305
185	yudixie_wbh_object_class_scaling_0415_imagenet	.305
186	omnivore_swinB	.293
187	vit_large_patch14_clip_224:openai_ft_in1k	.276
188	alexnet_training_seed_04	.276
189	mobilenet_v2_0_5_192	.276
190	resnet50_imagenet_100_seed-0	.276
191	cvt_cvt-13-384-in1k_4_LucyV4	.276
192	resnet_50_v1_spiking_l3	.276
193	nasnet_large	.276
194	AlexNet_SIN_fov4	.276
195	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.276
196	compact_cornets_V4	.265
197	convnext_tiny_sup	.265
198	resnet50_ewconplus_wzc	.250
199	yudixie_resnet50_category_class_0_240908	.250
200	deit_small_imagenet_100_seed-0	.250
201	ReAlnet01_cornet_fov16	.250
202	AlexNet_SIN_fov16	.250
203	yudixie_resnet50_object_class_0_240908	.250
204	yudixie_wbh_object_class_scaling_0415_run_0	.250
205	alexnet	.250
206	deepeisnn_ei_cifar10_vgg19	.240
207	vit_large_patch14_clip_224:laion2b_ft_in1k	.226
208	pnasnet_large_halfPrecision	.226
209	mobilenet_v2_0_75_160	.226
210	convnext_small_imagenet_full_seed-0	.226
211	vit_base_patch14_reg4_dinov2-lvd142m	.226
212	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.226
213	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.205
214	mobilenet_v2_1-4_224_pytorch	.205
215	yudixie_resnet18_object_class_0_240719	.205
216	yudixie_resnet50_distance_translation_0_240908	.205
217	yudixie_resnet18_imagenet1kpret_0_240719	.205
218	grcnn	.205
219	mobilenet_v2_1_4_224	.205
220	alexnet_training_seed_01_fov12	.205
221	deepeisnn_ei_cifar10_resnet18	.205
222	compact_resnet50_robust_V4	.197
223	resnet_101_v1	.186
224	resnet_101_v2	.178
225	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.168
226	convnext_small_imagenet_100_seed-0	.168
227	effnetb1_272x240	.168
228	AdvProp_efficientnet-b7	.168
229	resnet_152_v1	.161
230	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.152
231	xception	.152
232	imagenet_l2_3_0	.152
233	resnet50_robust_l2_eps3	.152
234	resnet50_ewconplus_noseed_2_323	.152
235	convnext_base_fb_in22k_ft_in1k	.152
236	yudixie_resnet18_rotation_reg_0_240719	.146
237	ReAlnet09	.138
238	alexnet_fov12	.138
239	yudixie_resnet18_distance_rotation_0_240719	.138
240	ReAlnet09_cornet	.138
241	resnet50_ewconplus_wzc_seed7_2_323	.138
242	yudixie_resnet50_distance_reg_0_240908	.125
243	resnet50_imagenet_10_seed-0	.125
244	resnet50-VITO-8deg-cc	.125
245	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.125
246	resnet50-vitoimagevidnet8	.125
247	resnet50_st_wzc_seed4_2_323	.125
248	convnext_large_imagenet_full_seed-0	.113
249	convnext_base_imagenet_full_seed-0	.113
250	resnet18_imagenet_full	.113
251	convnext_femto_ols:d1_in1k	.113
252	densenet-201	.108
253	ViT_L_32_imagenet1k	.108
254	resnet50_st_wzc_2_323	.102
255	resnet18_fair	.102
256	pnasnet_large_pytorch	.092
257	ReAlnet01_cornet_fov4	.092
258	alexnet_training_seed_01_fov16	.092
259	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.092
260	convnext_small_imagenet_10_seed-0	.069
261	efficientnet_b0_imagenet_full	.069
262	ReAlnet02_cornet	.069
263	resnet50_ecoset_full	.069
264	yudixie_resnet50_translation_reg_0_240908	.069
265	ReAlnet02	.069
266	convnext_small_imagenet_1_seed-0	.062
267	resnet50-SIN	.062
268	grcnn_109	.062
269	AdvProp_efficientnet-b4	.056
270	alexnet_training_seed_07	.051
271	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.051
272	alexnet_training_seed_01_fov4	.049
273	deit_small_imagenet_full_seed-0	.046
274	densenet-121	.046
275	deit_large_imagenet_full_seed-0	.025
276	resnet101_imagenet_full	.023
277	hmax
278	mobilenet_v2_1_0_224
279	yudixie_wbh_object_class_scaling_0415_run_1

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