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

0.88.

Note that scores are relative to this ceiling.

Data: Ferguson2024circle_line

Metric: value_delta