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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	pr1_stl10_disentangled_alpha04	1.0
2	ReAlnet10_ALL	1.0
3	alexnet_training_seed_08	1.0
4	alexnet_training_seed_03	1.0
5	ReAlnet10_cornet	1.0
6	mobilenet_v2_0_5_224	1.0
7	ReAlnet10	1.0
8	vit_large_patch14_clip_336:openai_ft_in12k_in1k	1.0
9	AdvProp_efficientnet-b8	1.0
10	resnet50_imagenet_1_seed-0	1.0
11	mobilenet_v2_1_0_160	1.0
12	densenet-169	1.0
13	resnet50-vicregl0p9	1.0
14	deit_small_imagenet_10_seed-0	1.0
15	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	1.0
16	resnet50_tutorial	1.0
17	resnext101_32x48d_wsl	1.0
18	gabor_filter_entropy_4x4_v2	1.0
19	ReAlnet05_ALL	1.0
20	yudixie_resnet50_random_0_240908	1.0
21	ReAlnet08_cornet	1.0
22	vgg_16	1.0
23	ReAlnet08	1.0
24	resnet50_robust_l2_eps1	1.0
25	resnet50-vicregl0p75	1.0
26	focalnet_tiny_lrf_in1k	1.0
27	gabor_filter_entropy_4x4_v2_forcedCosine	1.0
28	eva02_large_in22k	1.0
29	resnet50_hvs_wzc	1.0
30	vgg_19	1.0
31	yudixie_resnet50_distance_rotation_0_240908	1.0
32	resnet50-SIN_IN	1.0
33	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	1.0
34	vit_base_patch16_clip_224:openai_ft_in1k	1.0
35	deit_base_imagenet_full_seed-0	1.0
36	cvt_cvt-21-384-in1k_4_LucyV4	1.0
37	resnet152_imagenet_full	1.0
38	ReAlnet01_cornet_fov12	.967
39	ReAlnet07_ALL	.967
40	mobilenet_v2_1_3_224	.967
41	shufflenet_v2_x1_0	.967
42	omnivore_swinS	.967
43	yudixie_resnet50_imagenet1kpret_0_240908	.911
44	alexnet_training_seed_01	.911
45	inception_v1	.911
46	Res2Net50_26w_4s	.911
47	efficientnet_b0	.911
48	BiT-S-R152x2	.911
49	resnet152_ecoset_full	.911
50	AlexNet_SIN	.875
51	yudixie_resnet50_distance_translation_rotation_0_240908	.875
52	alexnet_training_seed_02	.875
53	yudixie_resnet50_rotation_reg_0_240908	.875
54	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.875
55	resnext101_32x32d_wsl	.875
56	convnext_base_fb_in1k_gram_aligned_dinov3	.825
57	predictive_coding_pc	.825
58	resnet50_st_wzc_2	.825
59	briaai_rmbg_1_4	.825
60	ReAlnet04_cornet	.825
61	ReAlnet04	.825
62	artResNet18_1	.825
63	convnext_tiny_imagenet_full_seed-0	.825
64	resnet-50-robust	.825
65	convnext_base_fb_in1k	.793
66	convnext_xlarge:fb_in22k_ft_in1k	.793
67	AT_efficientnet-b2	.793
68	AdvProp_efficientnet-b2	.793
69	compact_resnet50_robust_IT	.747
70	inception_v3	.747
71	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.747
72	efficientnet_b1_imagenet_full	.747
73	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.747
74	resnet34_ecoset_full	.747
75	resnet50_st_wzc_seed3_2_323	.718
76	alexnet_fov4	.718
77	mobilenet_v2_0_75_224	.718
78	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.718
79	BiT-S-R101x1	.718
80	yudixie_resnet18_random_0_240719	.718
81	voneresnet-50-non_stochastic	.718
82	deepeisnn_ei_imagenet_resnet18	.676
83	compact_vgg19_V4	.676
84	mobilenet_v2_1_0_192	.676
85	cvt_cvt-21-224-in1k_4_LucyV4	.676
86	cvt_cvt-13-224-in1k_4_LucyV4	.676
87	ReAlnet08_ALL	.650
88	resnet_50_v2	.650
89	resnet_50_v1	.650
90	resnet50-sup	.650
91	AdvProp_efficientnet-b6	.650
92	vit_large_clip336_openai	.612
93	yolos_tiny	.612
94	nasnet_mobile	.612
95	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.612
96	resnet50_imagenet_full	.612
97	convnext_large_clip_laion2b_finelayers	.588
98	resnet_50_v1_spiking_l4a	.588
99	resnet_50_v1_spiking	.588
100	resnet50-vicreg	.588
101	seresnext50_32x4d	.554
102	resnet50_st_wzc_seed6_2_323	.554
103	vit_base_patch16_dinov3-lvd1689m	.554
104	compact_cornets_IT	.554
105	ReAlnet03_ALL	.554
106	ReAlnet02_ALL	.554
107	resnet50-SIN_IN_IN	.554
108	vit_relpos_base_patch16_clsgap_224:sw_in1k	.554
109	vit_large_patch14_clip_336:laion2b_ft_in1k	.554
110	yudixie_resnet18_translation_reg_0_240719	.554
111	resnet34_imagenet_full	.554
112	convnext_xxlarge_clip	.532
113	alexnet_training_seed_06	.532
114	ReAlnet06_cornet	.532
115	vonegrcnn_62e_nobn	.532
116	ReAlnet06	.532
117	efficientnet-b7	.532
118	resnet-18_test_m	.532
119	convnext_tiny:in12k_ft_in1k	.532
120	BiT-S-R50x1	.532
121	cv_18_dagger_408_pretrained	.502
122	ReAlnet04_ALL	.502
123	ReAlnet01_ALL	.502
124	resnet_50_v1_spiking_l4	.502
125	alexnet_training_seed_09	.502
126	mobilenet_v2_0_75_192	.502
127	VOneCORnet-S	.502
128	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.502
129	antialiased-r50	.502
130	blt_vs	.502
131	yudixie_resnet18_distance_translation_0_240719	.502
132	custom_model_cv_18_dagger_408	.502
133	ReAlnet07_cornet	.482
134	ReAlnet07	.482
135	yudixie_wbh_object_class_scaling_0415_run_2	.454
136	compact_vgg19_IT	.454
137	compact_resnet50_IT	.454
138	ReAlnet09_ALL	.454
139	voneresnet-50	.454
140	resnet50-barlow	.454
141	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.454
142	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.454
143	resnext101_32x8d_wsl	.454
144	compact_resnet50_V4	.436
145	resnet50_primary_visual_cortex	.436
146	efficientnet_b2_imagenet_full	.436
147	vit_huge_clip336_laion	.411
148	deit3_base_patch16_224_fb_in1k	.411
149	ReAlnet06_ALL	.411
150	alexnet_training_seed_10	.411
151	alexnet_training_seed_05	.411
152	ReAlnet01_cornet	.411
153	ReAlnet01	.411
154	convnext_base:clip_laiona_augreg_ft_in1k_384	.411
155	resnet_SIN_IN_FT_IN	.411
156	yudixie_resnet18_category_class_0_240719	.411
157	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.411
158	voneresnet-50-robust	.395
159	dinov2_vitl14_reg_lc_featread	.372
160	alexnet_fov16	.372
161	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.372
162	resnet_152_v2	.372
163	yudixie_resnet18_distance_translation_rotation_0_240719	.372
164	resnet101_ecoset_full	.372
165	resnet-152_v2_pytorch	.372
166	ReAlnet03_cornet	.358
167	inception_v4	.358
168	yudixie_resnet50_translation_rotation_0_240908	.358
169	ReAlnet03	.358
170	yudixie_wbh_object_class_scaling_0415_run_3	.337
171	resnet50_st_wzc_seed7_2_323	.337
172	vit_base_patch14_dinov2-lvd142m	.337
173	ReAlnet05_cornet	.337
174	mobilenet_v2_1_0_128	.337
175	ReAlnet05	.337
176	resnet50-moclr8deg	.337
177	convnext_large:fb_in22k_ft_in1k	.337
178	BiT-S-R50x3	.337
179	deit_small_imagenet_1_seed-0	.337
180	yudixie_resnet18_distance_reg_0_240719	.337
181	yudixie_resnet18_translation_rotation_0_240719	.337
182	BiT-S-R152x4	.324
183	vonegrcnn_47e	.324
184	dinov2_vitg14_reg_lc	.305
185	yudixie_wbh_object_class_scaling_0415_imagenet	.305
186	deit3_base_patch16_224_fb_in22k_ft_in1k	.305
187	resnet50_ewconplus_wzc_seed5_2__323	.305
188	AlexNet_SIN_fov12	.305
189	pnasnet_large	.305
190	BiT-S-R101x3	.305
191	vonegrcnn_52e_full	.305
192	resnet18_ecoset_full	.305
193	omnivore_swinB	.293
194	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.276
195	AlexNet_SIN_fov4	.276
196	resnet_50_v1_spiking_l3	.276
197	alexnet_training_seed_04	.276
198	nasnet_large	.276
199	mobilenet_v2_0_5_192	.276
200	vit_large_patch14_clip_224:openai_ft_in1k	.276
201	resnet50_imagenet_100_seed-0	.276
202	cvt_cvt-13-384-in1k_4_LucyV4	.276
203	compact_cornets_V4	.265
204	convnext_tiny_sup	.265
205	dinov2_vitg14_lc	.250
206	yudixie_wbh_object_class_scaling_0415_run_0	.250
207	AlexNet_SIN_fov16	.250
208	ReAlnet01_cornet_fov16	.250
209	resnet50_ewconplus_wzc	.250
210	yudixie_resnet50_category_class_0_240908	.250
211	yudixie_resnet50_object_class_0_240908	.250
212	deit_small_imagenet_100_seed-0	.250
213	alexnet	.250
214	deepeisnn_ei_cifar10_vgg19	.240
215	vit_base_patch14_reg4_dinov2-lvd142m	.226
216	pnasnet_large_halfPrecision	.226
217	mobilenet_v2_0_75_160	.226
218	vit_large_patch14_clip_224:laion2b_ft_in1k	.226
219	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.226
220	convnext_small_imagenet_full_seed-0	.226
221	convnext_large_in22k_supervised	.205
222	deepeisnn_ei_cifar10_resnet18	.205
223	alexnet_training_seed_01_fov12	.205
224	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.205
225	mobilenet_v2_1_4_224	.205
226	grcnn	.205
227	yudixie_resnet50_distance_translation_0_240908	.205
228	yudixie_resnet18_imagenet1kpret_0_240719	.205
229	yudixie_resnet18_object_class_0_240719	.205
230	mobilenet_v2_1-4_224_pytorch	.205
231	compact_resnet50_robust_V4	.197
232	eicircuit_resnet18_bn_20260919	.186
233	resnet_101_v1	.186
234	resnet_101_v2	.178
235	gabor_filter_mean_abs_4x4_v2	.168
236	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.168
237	AdvProp_efficientnet-b7	.168
238	convnext_small_imagenet_100_seed-0	.168
239	effnetb1_272x240	.168
240	resnet_152_v1	.161
241	convnext_base_fb_in22k_ft_in1k	.152
242	xception	.152
243	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.152
244	resnet50_robust_l2_eps3	.152
245	imagenet_l2_3_0	.152
246	yudixie_resnet18_rotation_reg_0_240719	.146
247	dinov2_vitl14_reg_lc	.138
248	alexnet_fov12	.138
249	ReAlnet09_cornet	.138
250	ReAlnet09	.138
251	yudixie_resnet18_distance_rotation_0_240719	.138
252	gabor_filter_entropy_4x4	.125
253	regnety_032	.125
254	dinov2_vitl14_lc_res336	.125
255	yudixie_resnet50_distance_reg_0_240908	.125
256	resnet50-vitoimagevidnet8	.125
257	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.125
258	resnet50-VITO-8deg-cc	.125
259	resnet50_imagenet_10_seed-0	.125
260	convnext_femto_ols:d1_in1k	.113
261	convnext_large_imagenet_full_seed-0	.113
262	convnext_base_imagenet_full_seed-0	.113
263	resnet18_imagenet_full	.113
264	densenet-201	.108
265	ViT_L_32_imagenet1k	.108
266	resnet18_fair	.102
267	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.092
268	alexnet_training_seed_01_fov16	.092
269	ReAlnet01_cornet_fov4	.092
270	pnasnet_large_pytorch	.092
271	dinov2_vitl14_lc	.076
272	ReAlnet02_cornet	.069
273	yudixie_resnet50_translation_reg_0_240908	.069
274	ReAlnet02	.069
275	convnext_small_imagenet_10_seed-0	.069
276	efficientnet_b0_imagenet_full	.069
277	resnet50_ecoset_full	.069
278	grcnn_109	.062
279	resnet50-SIN	.062
280	convnext_small_imagenet_1_seed-0	.062
281	AdvProp_efficientnet-b4	.056
282	alexnet_training_seed_07	.051
283	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.051
284	alexnet_training_seed_01_fov4	.049
285	densenet-121	.046
286	deit_small_imagenet_full_seed-0	.046
287	deit_large_imagenet_full_seed-0	.025
288	resnet101_imagenet_full	.023
289	yudixie_wbh_object_class_scaling_0415_run_1
290	mobilenet_v2_1_0_224
291	hmax

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