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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024circle_line-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	ijepa_vith14_linhead	1.0
2	mehrer2020_alexnet_training_seed_07	1.0
3	shufflenet_v2_x1_0	1.0
4	yudixie_resnet18_distance_translation_0_240719	1.0
5	resnet101_imagenet_full	1.0
6	effnetb1_272x240	1.0
7	yudixie_resnet50_translation_rotation_0_240908	1.0
8	BiT-S-R101x3	1.0
9	deit_small_imagenet_10_seed-0	1.0
10	resnet50_ewconplus_wzc_seed5_2__323	1.0
11	vit_base_patch14_dinov2-lvd142m	1.0
12	resnet50_st_wzc_2	1.0
13	alexnet_training_seed_09	1.0
14	nasnet_large	1.0
15	efficientnet-b7	1.0
16	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	1.0
17	BiT-S-R50x1	1.0
18	convnext_tiny_imagenet_full_seed-0	1.0
19	deepeisnn_ei_cifar10_vgg19	1.0
20	voneresnet-50-non_stochastic	1.0
21	cvt_cvt-21-224-in1k_4_LucyV4	.935
22	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.935
23	mobilenet_v2_0_75_192	.931
24	vit_base_patch16_clip_224:openai_ft_in1k	.931
25	convnext_tiny_sup	.931
26	convnext_base_fb_in1k	.931
27	resnext101_32x8d_wsl	.931
28	BiT-S-R152x2	.848
29	convnext_small_imagenet_full_seed-0	.848
30	convnext_base_fb_in22k_ft_in1k	.848
31	yudixie_resnet50_random_0_240908	.845
32	grcnn_109	.845
33	vonegrcnn_52e_full	.845
34	yudixie_resnet18_translation_reg_0_240719	.845
35	yudixie_resnet18_distance_translation_rotation_0_240719	.845
36	pnasnet_large_pytorch	.845
37	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.770
38	BiT-S-R50x3	.767
39	convnext_small_imagenet_1_seed-0	.767
40	convnext_large_imagenet_full_seed-0	.767
41	deit3_base_patch16_224_fb_in1k	.767
42	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.699
43	convnext_base_fb_in1k_gram_aligned_dinov3	.699
44	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.699
45	convnext_xxlarge_clip	.696
46	mobilenet_v2_1_4_224	.696
47	AdvProp_efficientnet-b4	.696
48	deit_small_imagenet_100_seed-0	.696
49	deit_base_imagenet_full_seed-0	.696
50	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.696
51	mobilenet_v2_1-4_224_pytorch	.696
52	AdvProp_efficientnet-b8	.634
53	mehrer2020_alexnet_training_seed_01	.632
54	alexnet_training_seed_02	.632
55	resnet_101_v2	.632
56	mobilenet_v2_0_75_160	.632
57	convnext_femto_ols:d1_in1k	.632
58	alexnet_training_seed_01_fov12	.632
59	cvt_cvt-13-224-in1k_4_LucyV4	.632
60	alexnet_fov16	.575
61	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.575
62	resnext101_32x48d_wsl	.573
63	mehrer2020_alexnet_training_seed_01_fov4	.573
64	yudixie_resnet50_distance_reg_0_240908	.573
65	alexnet_training_seed_06	.573
66	mobilenet_v2_0_75_224	.573
67	vit_relpos_base_patch16_clsgap_224:sw_in1k	.573
68	vit_large_patch14_clip_336:laion2b_ft_in1k	.573
69	vit_large_patch14_clip_224:laion2b_ft_in1k	.573
70	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.573
71	convnext_base:clip_laiona_augreg_ft_in1k_384	.573
72	convnext_large_in22k_supervised	.573
73	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.573
74	deit3_base_patch16_224_fb_in22k_ft_in1k	.573
75	resnet18_fair	.573
76	vit_base_patch14_reg4_dinov2-lvd142m	.573
77	AlexNet_SIN_fov12	.573
78	vonegrcnn_62e_nobn	.522
79	omnivore_swinB	.522
80	convnext_base_imagenet_full_seed-0	.522
81	resnet152_ecoset_full	.522
82	resnet50_st_wzc_seed3_2_323	.522
83	cv_18_dagger_408_pretrained	.522
84	resnet50_primary_visual_cortex	.520
85	yolos_tiny	.520
86	vgg_19	.520
87	Res2Net50_26w_4s	.520
88	convnext_large:fb_in22k_ft_in1k	.520
89	densenet-121	.520
90	resnet50-vicregl0p9	.520
91	AdvProp_efficientnet-b6	.520
92	yudixie_resnet18_rotation_reg_0_240719	.520
93	resnet101_ecoset_full	.520
94	cvt_cvt-13-384-in1k_4_LucyV4	.520
95	convnext_large_clip_laion2b_finelayers	.520
96	yudixie_wbh_object_class_scaling_0415_run_1	.520
97	resnext101_32x16d_wsl	.474
98	xception	.472
99	mobilenet_v2_1_0_128	.472
100	antialiased-r50	.472
101	deepeisnn_ei_imagenet_resnet18	.472
102	ViT_L_32_imagenet1k	.472
103	resnet-50-robust	.472
104	resnet_50_v1	.430
105	resnet50-sup	.430
106	mehrer2020_alexnet_training_seed_01_fov12	.428
107	gabor_filter_entropy_4x4_v2	.428
108	dinov2_vitg14_reg_lc	.428
109	ReAlnet10_ALL	.428
110	resnet_50_v2	.428
111	inception_v4	.428
112	mobilenet_v2_0_5_224	.428
113	voneresnet-50	.428
114	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.428
115	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.428
116	convnext_tiny:in12k_ft_in1k	.428
117	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.428
118	BiT-S-R101x1	.428
119	resnext101_32x32d_wsl	.390
120	mehrer2020_alexnet_training_seed_08	.389
121	seresnext50_32x4d	.389
122	ReAlnet02_ALL	.389
123	resnet_50_v1_spiking_l3	.389
124	briaai_rmbg_1_4	.389
125	yudixie_resnet50_imagenet1kpret_0_240908	.389
126	mobilenet_v2_1_0_224	.389
127	yudixie_resnet50_translation_reg_0_240908	.389
128	convnext_xlarge:fb_in22k_ft_in1k	.389
129	yudixie_resnet18_object_class_0_240719	.389
130	yudixie_wbh_object_class_scaling_0415_run_3	.389
131	vit_base_patch16_dinov3-lvd1689m	.389
132	resnet50_ecoset_full	.389
133	predictive_coding_pc	.354
134	resnet50_vicreg_linhead	.353
135	gabor_filter_entropy_4x4_v2_forcedCosine	.353
136	compact_resnet50_robust_V4	.353
137	ReAlnet01_ALL	.353
138	resnet_152_v1	.353
139	voneresnet-50-robust	.353
140	resnet50-moclr8deg	.353
141	resnet50-vicreg	.353
142	resnet50-SIN_IN_IN	.353
143	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.353
144	resnet50-vicregl0p75	.353
145	BiT-S-R152x4	.353
146	convnext_small_imagenet_10_seed-0	.353
147	efficientnet_b2_imagenet_full	.353
148	yudixie_wbh_object_class_scaling_0415_run_0	.353
149	alexnet_training_seed_01_fov16	.353
150	AT_efficientnet-b2	.322
151	AdvProp_efficientnet-b2	.322
152	mehrer2020_alexnet_training_seed_06	.320
153	mehrer2020_alexnet_training_seed_02	.320
154	vit_huge_clip336_laion	.320
155	vit_large_clip336_openai	.320
156	dinov2_vitl14_reg_lc_featread	.320
157	dinov2_vitl14_lc_res336	.320
158	dinov2_vitl14_reg_lc	.320
159	eva02_large_in22k	.320
160	dinov2_vitg14_lc	.320
161	yudixie_resnet50_category_class_0_240908	.320
162	alexnet_training_seed_01	.320
163	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.320
164	vit_large_patch14_clip_224:openai_ft_in1k	.320
165	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.320
166	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.320
167	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.320
168	yudixie_resnet18_imagenet1kpret_0_240719	.320
169	dinov2_vitl14_lc	.320
170	resnet50_st_wzc_seed6_2_323	.320
171	resnet34_ecoset_full	.320
172	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.320
173	deepeisnn_ei_cifar10_resnet18	.292
174	compact_resnet50_robust_IT	.291
175	compact_cornets_V4	.291
176	compact_vgg19_V4	.291
177	ReAlnet06_cornet	.291
178	resnet_101_v1	.291
179	nasnet_mobile	.291
180	inception_v3	.291
181	ReAlnet06	.291
182	resnet50-SIN	.291
183	yudixie_resnet18_distance_rotation_0_240719	.291
184	ReAlnet01_cornet_fov16	.291
185	alexnet_fov12	.291
186	mehrer2020_alexnet_training_seed_03	.264
187	compact_resnet50_V4	.264
188	resnet_50_v1_spiking_l4a	.264
189	resnet_50_v1_spiking	.264
190	yudixie_resnet50_object_class_0_240908	.264
191	grcnn	.264
192	densenet-169	.264
193	yudixie_resnet18_random_0_240719	.264
194	deit_large_imagenet_full_seed-0	.264
195	deit_small_imagenet_full_seed-0	.264
196	efficientnet_b0_imagenet_full	.264
197	resnet50_imagenet_1_seed-0	.264
198	cvt_cvt-21-384-in1k_4_LucyV4	.264
199	yudixie_wbh_object_class_scaling_0415_run_2	.264
200	omnivore_swinS	.240
201	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.240
202	mehrer2020_alexnet_training_seed_09	.239
203	resnet50_ewconplus_wzc	.239
204	mobilenet_v2_1_3_224	.239
205	alexnet_training_seed_05	.239
206	mobilenet_v2_1_0_192	.239
207	resnet50-vitoimagevidnet8	.239
208	resnet50-VITO-8deg-cc	.239
209	efficientnet_b0	.239
210	vonegrcnn_47e	.239
211	resnet50_imagenet_10_seed-0	.239
212	resnet34_imagenet_full	.239
213	resnet50_simsiam_linhead	.217
214	resnet50_barlowtwins_linhead	.217
215	resnet50_hvs_wzc	.217
216	compact_cornets_IT	.217
217	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.217
218	ReAlnet07_cornet	.217
219	mobilenet_v2_0_5_192	.217
220	ReAlnet07	.217
221	resnet_SIN_IN_FT_IN	.217
222	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.217
223	resnet50_st_wzc_seed7_2_323	.217
224	alexnet_fov4	.217
225	pnasnet_large	.198
226	AdvProp_efficientnet-b7	.198
227	pnasnet_large_halfPrecision	.198
228	alexnet_training_seed_08	.197
229	vgg_16	.197
230	resnet50_tutorial	.197
231	resnet50_byol_linhead	.179
232	alexnet_training_seed_03	.179
233	artResNet18_1	.179
234	alexnet_training_seed_01_fov4	.179
235	resnet50_imagenet_full	.179
236	custom_model_cv_18_dagger_408	.179
237	hmax	.179
238	focalnet_tiny_lrf_in1k	.179
239	mehrer2020_alexnet_training_seed_04	.162
240	gabor_filter_mean_abs_4x4_v2	.162
241	alexnet_training_seed_10	.162
242	ReAlnet09_cornet	.162
243	mobilenet_v2_1_0_160	.162
244	ReAlnet09	.162
245	efficientnet_b1_imagenet_full	.162
246	compact_vgg19_IT	.147
247	alexnet_training_seed_04	.147
248	ReAlnet10_cornet	.147
249	yudixie_resnet50_rotation_reg_0_240908	.147
250	ReAlnet10	.147
251	alexnet	.147
252	resnet50_simclr_linhead_01norm	.134
253	mehrer2020_alexnet_training_seed_05	.134
254	convnext_small_imagenet_100_seed-0	.134
255	AlexNet_SIN_fov4	.134
256	ReAlnet08_ALL	.121
257	ReAlnet03_ALL	.121
258	yudixie_resnet50_distance_translation_rotation_0_240908	.121
259	resnet50-SIN_IN	.121
260	deit_small_imagenet_1_seed-0	.121
261	eicircuit_resnet18_bn_20260919	.110
262	compact_resnet50_IT	.110
263	ReAlnet05_ALL	.110
264	yudixie_resnet50_distance_rotation_0_240908	.110
265	densenet-201	.110
266	AlexNet_SIN	.110
267	resnet152_imagenet_full	.110
268	ReAlnet09_ALL	.100
269	ReAlnet04_ALL	.100
270	VOneCORnet-S	.100
271	yudixie_resnet18_category_class_0_240719	.100
272	ReAlnet07_ALL	.091
273	ReAlnet05_cornet	.091
274	ReAlnet05	.091
275	mehrer2020_alexnet_training_seed_10	.082
276	mehrer2020_alexnet_training_seed_01_fov16	.082
277	resnet_50_v1_spiking_l4	.082
278	yudixie_resnet50_distance_translation_0_240908	.082
279	resnet50-barlow	.082
280	yudixie_resnet18_translation_rotation_0_240719	.082
281	gabor_filter_entropy_4x4	.075
282	ReAlnet08_cornet	.075
283	ReAlnet08	.075
284	resnet18_ecoset_full	.075
285	alexnet_training_seed_07	.068
286	ReAlnet03_cornet	.068
287	ReAlnet02_cornet	.068
288	ReAlnet03	.068
289	ReAlnet02	.068
290	resnet50_robust_l2_eps3	.068
291	imagenet_l2_3_0	.068
292	yudixie_resnet18_distance_reg_0_240719	.068
293	resnet-18_test_m	.062
294	resnet50_imagenet_100_seed-0	.062
295	ReAlnet06_ALL	.056
296	yudixie_wbh_object_class_scaling_0415_imagenet	.056
297	AlexNet_SIN_fov16	.056
298	ReAlnet01_cornet_fov12	.056
299	ReAlnet04_cornet	.046
300	ReAlnet04	.046
301	resnet50_robust_l2_eps1	.046
302	blt_vs	.046
303	resnet18_imagenet_full	.046
304	ReAlnet01_cornet	.034
305	ReAlnet01	.034
306	inception_v1	.031
307	ReAlnet01_cornet_fov4	.026
308	regnety_032	.023
309	resnet-152_v2_pytorch	.021
310	resnet_152_v2	.014

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

0.88.

Note that scores are relative to this ceiling.

Data: Ferguson2024circle_line

Metric: value_delta