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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024tilted_line-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	gabor_filter_mean_abs_4x4_v2	1.0
2	convnext_large_in22k_supervised	1.0
3	yudixie_wbh_object_class_scaling_0415_run_1	1.0
4	yudixie_wbh_object_class_scaling_0415_imagenet	1.0
5	ReAlnet01_cornet_fov16	1.0
6	compact_resnet50_robust_IT	1.0
7	alexnet_training_seed_01	1.0
8	alexnet_training_seed_02	1.0
9	resnet50-vitoimagevidnet8	1.0
10	resnet-18_test_m	1.0
11	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
12	resnet50-VITO-8deg-cc	1.0
13	omnivore_swinS	1.0
14	convnext_tiny_imagenet_full_seed-0	1.0
15	efficientnet_b1_imagenet_full	1.0
16	resnet50_ecoset_full	1.0
17	pnasnet_large_halfPrecision	1.0
18	resnet50_st_wzc_2	1.0
19	compact_cornets_V4	1.0
20	resnet_50_v1_spiking_l3	1.0
21	yudixie_resnet50_random_0_240908	1.0
22	pnasnet_large	1.0
23	ReAlnet08_cornet	1.0
24	mobilenet_v2_0_75_224	1.0
25	ReAlnet08	1.0
26	swin_small_patch4_window7_224:ms_in22k_ft_in1k	1.0
27	vonegrcnn_52e_full	1.0
28	resnet50_tutorial	1.0
29	resnet-50-robust	1.0
30	yudixie_wbh_object_class_scaling_0415_run_3	.975
31	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.975
32	resnet_50_v1_spiking	.975
33	resnet_152_v1	.975
34	resnet_101_v2	.975
35	mobilenet_v2_0_5_224	.975
36	resnet152_ecoset_full	.975
37	resnet152_imagenet_full	.975
38	resnet50_ewconplus_wzc_seed5_2__323	.917
39	alexnet_fov4	.917
40	compact_vgg19_IT	.917
41	ReAlnet06_ALL	.917
42	alexnet_training_seed_05	.917
43	ReAlnet05_cornet	.917
44	ReAlnet05	.917
45	resnet50-SIN_IN_IN	.917
46	convnext_small_imagenet_10_seed-0	.917
47	yudixie_resnet18_category_class_0_240719	.917
48	resnet50_imagenet_10_seed-0	.917
49	convnext_xxlarge_clip	.883
50	alexnet_training_seed_01_fov16	.883
51	AlexNet_SIN_fov4	.883
52	alexnet_fov16	.883
53	compact_resnet50_V4	.883
54	yudixie_resnet50_object_class_0_240908	.883
55	mobilenet_v2_0_75_160	.883
56	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.883
57	densenet-169	.883
58	resnet50-vicregl0p75	.883
59	cvt_cvt-21-224-in1k_4_LucyV4	.883
60	deepeisnn_ei_imagenet_resnet18	.831
61	resnet50_ewconplus_wzc_seed6_2_323	.831
62	alexnet_fov12	.831
63	AlexNet_SIN	.831
64	resnet50_hvs_wzc	.831
65	briaai_rmbg_1_4	.831
66	mobilenet_v2_1_4_224	.831
67	mobilenet_v2_1_0_128	.831
68	grcnn_109	.831
69	voneresnet-50-robust	.831
70	resnet50-barlow	.831
71	BiT-S-R101x3	.831
72	convnext_tiny_sup	.831
73	cvt_cvt-13-224-in1k_4_LucyV4	.831
74	mobilenet_v2_1-4_224_pytorch	.831
75	resnext101_32x32d_wsl	.831
76	convnext_base_fb_in1k_gram_aligned_dinov3	.800
77	convnext_base_fb_in1k	.800
78	resnet50_st_wzc_seed3_2_323	.800
79	ReAlnet01_cornet	.800
80	mobilenet_v2_1_0_192	.800
81	vgg_16	.800
82	ReAlnet01	.800
83	convnext_xlarge:fb_in22k_ft_in1k	.800
84	convnext_large:fb_in22k_ft_in1k	.800
85	resnet_SIN_IN_FT_IN	.800
86	convnext_base_imagenet_full_seed-0	.800
87	yudixie_resnet18_distance_reg_0_240719	.800
88	deit_large_imagenet_full_seed-0	.800
89	gabor_filter_entropy_4x4_v2_forcedCosine	.753
90	deepeisnn_ei_cifar10_vgg19	.753
91	ReAlnet01_ALL	.753
92	ReAlnet10_cornet	.753
93	vgg_19	.753
94	vonegrcnn_62e_nobn	.753
95	ReAlnet10	.753
96	efficientnet_b0	.753
97	BiT-S-R152x2	.753
98	convnext_small_imagenet_100_seed-0	.753
99	convnext_large_clip_laion2b_finelayers	.725
100	alexnet_training_seed_04	.725
101	compact_resnet50_IT	.682
102	ReAlnet08_ALL	.682
103	ReAlnet09_cornet	.682
104	resnet_101_v1	.682
105	inception_v3	.682
106	yudixie_resnet50_translation_reg_0_240908	.682
107	ReAlnet09	.682
108	omnivore_swinB	.682
109	antialiased-r50	.682
110	convnext_small_imagenet_full_seed-0	.682
111	deit_small_imagenet_full_seed-0	.682
112	resnet101_ecoset_full	.682
113	seresnext50_32x4d	.657
114	regnety_032	.657
115	vit_huge_clip336_laion	.657
116	vit_large_clip336_openai	.657
117	dinov2_vitl14_reg_lc_featread	.657
118	dinov2_vitl14_lc_res336	.657
119	dinov2_vitl14_reg_lc	.657
120	eva02_large_in22k	.657
121	dinov2_vitg14_reg_lc	.657
122	dinov2_vitg14_lc	.657
123	dinov2_vitl14_lc	.657
124	deit3_base_patch16_224_fb_in1k	.657
125	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.657
126	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.657
127	convnext_base_fb_in22k_ft_in1k	.657
128	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.657
129	vit_base_patch16_dinov3-lvd1689m	.657
130	vit_base_patch14_reg4_dinov2-lvd142m	.657
131	vit_base_patch14_dinov2-lvd142m	.657
132	AlexNet_SIN_fov12	.657
133	cv_18_dagger_408_pretrained	.657
134	resnet_50_v1_spiking_l4a	.657
135	mobilenet_v2_0_75_192	.657
136	efficientnet-b7	.657
137	resnet50-SIN_IN	.657
138	vit_relpos_base_patch16_clsgap_224:sw_in1k	.657
139	vit_large_patch14_clip_336:laion2b_ft_in1k	.657
140	vit_large_patch14_clip_224:openai_ft_in1k	.657
141	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.657
142	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.657
143	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.657
144	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.657
145	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.657
146	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.657
147	vit_base_patch16_clip_224:openai_ft_in1k	.657
148	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.657
149	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.657
150	convnext_base:clip_laiona_augreg_ft_in1k_384	.657
151	AT_efficientnet-b2	.657
152	resnet50-vicregl0p9	.657
153	BiT-S-R152x4	.657
154	AdvProp_efficientnet-b8	.657
155	AdvProp_efficientnet-b2	.657
156	AdvProp_efficientnet-b4	.657
157	AdvProp_efficientnet-b7	.657
158	AdvProp_efficientnet-b6	.657
159	convnext_large_imagenet_full_seed-0	.657
160	yudixie_resnet18_distance_translation_rotation_0_240719	.657
161	deit_base_imagenet_full_seed-0	.657
162	cvt_cvt-21-384-in1k_4_LucyV4	.657
163	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.657
164	cvt_cvt-13-384-in1k_4_LucyV4	.657
165	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.657
166	ViT_L_32_imagenet1k	.657
167	custom_model_cv_18_dagger_408	.657
168	resnet50_st_wzc_seed6_2_323	.618
169	ReAlnet04_ALL	.618
170	yolos_tiny	.618
171	ReAlnet04_cornet	.618
172	ReAlnet04	.618
173	BiT-S-R101x1	.618
174	deit_small_imagenet_100_seed-0	.618
175	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.618
176	yudixie_resnet50_distance_reg_0_240908	.595
177	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.595
178	yudixie_resnet18_rotation_reg_0_240719	.595
179	resnet50_imagenet_100_seed-0	.595
180	resnet50_ewconplus_wzc_seed2_2__323	.560
181	ReAlnet02_ALL	.560
182	resnet34_imagenet_full	.560
183	resnext101_32x48d_wsl	.560
184	compact_vgg19_V4	.539
185	mobilenet_v2_1_0_224	.539
186	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.539
187	VOneCORnet-S	.539
188	deit_small_imagenet_1_seed-0	.539
189	deit_small_imagenet_10_seed-0	.539
190	ReAlnet03_ALL	.507
191	BiT-S-R50x3	.507
192	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.507
193	resnet50_imagenet_1_seed-0	.507
194	hmax	.507
195	ReAlnet06_cornet	.489
196	resnet_152_v2	.489
197	yudixie_resnet50_rotation_reg_0_240908	.489
198	ReAlnet06	.489
199	alexnet	.489
200	gabor_filter_entropy_4x4_v2	.460
201	deepeisnn_ei_cifar10_resnet18	.460
202	ReAlnet01_cornet_fov4	.460
203	ReAlnet07_ALL	.460
204	yudixie_resnet50_distance_translation_rotation_0_240908	.460
205	artResNet18_1	.460
206	efficientnet_b2_imagenet_full	.460
207	resnext101_32x8d_wsl	.460
208	yudixie_wbh_object_class_scaling_0415_run_2	.443
209	resnet18_fair	.443
210	resnet50_ewconplus_wzc	.443
211	mobilenet_v2_0_5_192	.443
212	yudixie_resnet50_distance_rotation_0_240908	.443
213	resnet50-moclr8deg	.443
214	resnet50-vicreg	.443
215	Res2Net50_26w_4s	.443
216	yudixie_resnet18_translation_reg_0_240719	.443
217	resnet101_imagenet_full	.443
218	voneresnet-50-non_stochastic	.443
219	nasnet_large	.417
220	BiT-S-R50x1	.417
221	resnet50_imagenet_full	.417
222	alexnet_training_seed_03	.401
223	ReAlnet03_cornet	.401
224	ReAlnet03	.401
225	convnext_tiny:in12k_ft_in1k	.401
226	convnext_small_imagenet_1_seed-0	.401
227	ReAlnet10_ALL	.377
228	ReAlnet05_ALL	.377
229	alexnet_training_seed_08	.377
230	yudixie_resnet50_distance_translation_0_240908	.377
231	resnet50_robust_l2_eps1	.377
232	densenet-121	.377
233	resnet50_robust_l2_eps3	.377
234	imagenet_l2_3_0	.377
235	efficientnet_b0_imagenet_full	.377
236	AlexNet_SIN_fov16	.363
237	yudixie_resnet50_category_class_0_240908	.363
238	yudixie_resnet50_imagenet1kpret_0_240908	.363
239	ReAlnet02_cornet	.363
240	ReAlnet02	.363
241	compact_resnet50_robust_V4	.342
242	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.342
243	predictive_coding_pc	.329
244	pr1_stl10_disentangled_alpha04	.329
245	resnet_50_v1_spiking_l4	.329
246	resnet_50_v1	.329
247	resnet50-sup	.329
248	yudixie_resnet18_random_0_240719	.329
249	compact_cornets_IT	.310
250	ReAlnet09_ALL	.310
251	mobilenet_v2_1_3_224	.310
252	inception_v4	.310
253	shufflenet_v2_x1_0	.310
254	resnet34_ecoset_full	.310
255	resnet18_imagenet_full	.310
256	effnetb1_272x240	.310
257	alexnet_training_seed_01_fov12	.298
258	densenet-201	.298
259	nasnet_mobile	.281
260	vonegrcnn_47e	.281
261	resnet-152_v2_pytorch	.281
262	gabor_filter_entropy_4x4	.270
263	yudixie_wbh_object_class_scaling_0415_run_0	.270
264	alexnet_training_seed_01_fov4	.270
265	yudixie_resnet18_translation_rotation_0_240719	.270
266	alexnet_training_seed_10	.254
267	mobilenet_v2_1_0_160	.254
268	ReAlnet07_cornet	.245
269	ReAlnet07	.245
270	blt_vs	.245
271	yudixie_resnet18_imagenet1kpret_0_240719	.230
272	resnet18_ecoset_full	.230
273	ReAlnet01_cornet_fov12	.222
274	yudixie_resnet50_translation_rotation_0_240908	.222
275	voneresnet-50	.222
276	resnet50-SIN	.222
277	yudixie_resnet18_distance_translation_0_240719	.222
278	yudixie_resnet18_distance_rotation_0_240719	.209
279	grcnn	.189
280	alexnet_training_seed_06	.182
281	pnasnet_large_pytorch	.171
282	alexnet_training_seed_09	.165
283	convnext_femto_ols:d1_in1k	.155
284	inception_v1	.141
285	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.141
286	xception	.135
287	alexnet_training_seed_07	.135
288	yudixie_resnet18_object_class_0_240719	.135
289	resnet_50_v2	.116
290	resnet50_primary_visual_cortex	.111
291	eicircuit_resnet18_bn_20260919	.058
292	focalnet_tiny_lrf_in1k

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

0.91.

Note that scores are relative to this ceiling.

Data: Ferguson2024tilted_line

Metric: value_delta