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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	resnet101_imagenet_full	1.0
2	effnetb1_272x240	1.0
3	yudixie_resnet18_distance_translation_0_240719	1.0
4	shufflenet_v2_x1_0	1.0
5	deit_small_imagenet_10_seed-0	1.0
6	vit_base_patch14_dinov2-lvd142m	1.0
7	yudixie_resnet50_translation_rotation_0_240908	1.0
8	BiT-S-R101x3	1.0
9	resnet50_ewconplus_wzc_seed5_2__323	1.0
10	alexnet_training_seed_09	1.0
11	convnext_tiny_imagenet_full_seed-0	1.0
12	resnet50_st_wzc_2	1.0
13	voneresnet-50-non_stochastic	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	deepeisnn_ei_cifar10_vgg19	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	resnext101_32x8d_wsl	.931
23	vit_base_patch16_clip_224:openai_ft_in1k	.931
24	convnext_tiny_sup	.931
25	convnext_base_fb_in1k	.931
26	convnext_small_imagenet_full_seed-0	.848
27	BiT-S-R152x2	.848
28	convnext_base_fb_in22k_ft_in1k	.848
29	pnasnet_large_pytorch	.845
30	yudixie_resnet18_distance_translation_rotation_0_240719	.845
31	yudixie_resnet18_translation_reg_0_240719	.845
32	grcnn_109	.845
33	yudixie_resnet50_random_0_240908	.845
34	vonegrcnn_52e_full	.845
35	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.770
36	resnet50_ewconplus_wzc_seed4_2_323	.770
37	convnext_small_imagenet_1_seed-0	.767
38	convnext_large_imagenet_full_seed-0	.767
39	BiT-S-R50x3	.767
40	deit3_base_patch16_224_fb_in1k	.767
41	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.699
42	convnext_base_fb_in1k_gram_aligned_dinov3	.699
43	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.699
44	deit_base_imagenet_full_seed-0	.696
45	deit_small_imagenet_100_seed-0	.696
46	mobilenet_v2_1-4_224_pytorch	.696
47	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.696
48	mobilenet_v2_1_4_224	.696
49	AdvProp_efficientnet-b4	.696
50	AdvProp_efficientnet-b8	.634
51	alexnet_training_seed_02	.632
52	mobilenet_v2_0_75_160	.632
53	cvt_cvt-13-224-in1k_4_LucyV4	.632
54	resnet_101_v2	.632
55	alexnet_training_seed_01_fov12	.632
56	convnext_femto_ols:d1_in1k	.632
57	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.575
58	alexnet_fov16	.575
59	resnext101_32x48d_wsl	.573
60	convnext_base:clip_laiona_augreg_ft_in1k_384	.573
61	alexnet_training_seed_06	.573
62	mobilenet_v2_0_75_224	.573
63	vit_large_patch14_clip_336:laion2b_ft_in1k	.573
64	vit_relpos_base_patch16_clsgap_224:sw_in1k	.573
65	yudixie_resnet50_distance_reg_0_240908	.573
66	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.573
67	vit_base_patch14_reg4_dinov2-lvd142m	.573
68	vit_large_patch14_clip_224:laion2b_ft_in1k	.573
69	AlexNet_SIN_fov12	.573
70	resnet18_fair	.573
71	deit3_base_patch16_224_fb_in22k_ft_in1k	.573
72	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.573
73	cv_18_dagger_408_pretrained	.522
74	convnext_base_imagenet_full_seed-0	.522
75	resnet152_ecoset_full	.522
76	omnivore_swinB	.522
77	vonegrcnn_62e_nobn	.522
78	resnet50_st_wzc_seed3_2_323	.522
79	yolos_tiny	.520
80	AdvProp_efficientnet-b6	.520
81	cvt_cvt-13-384-in1k_4_LucyV4	.520
82	resnet101_ecoset_full	.520
83	yudixie_resnet18_rotation_reg_0_240719	.520
84	Res2Net50_26w_4s	.520
85	vgg_19	.520
86	resnet50-vicregl0p9	.520
87	densenet-121	.520
88	resnet50_primary_visual_cortex	.520
89	convnext_large:fb_in22k_ft_in1k	.520
90	yudixie_wbh_object_class_scaling_0415_run_1	.520
91	mobilenet_v2_1_0_128	.472
92	resnet-50-robust	.472
93	ViT_L_32_imagenet1k	.472
94	xception	.472
95	antialiased-r50	.472
96	deepeisnn_ei_imagenet_resnet18	.472
97	resnet_50_v1	.430
98	resnet50-sup	.430
99	ReAlnet10_ALL	.428
100	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.428
101	convnext_tiny:in12k_ft_in1k	.428
102	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.428
103	mobilenet_v2_0_5_224	.428
104	inception_v4	.428
105	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.428
106	voneresnet-50	.428
107	BiT-S-R101x1	.428
108	resnet_50_v2	.428
109	resnext101_32x32d_wsl	.390
110	convnext_xlarge:fb_in22k_ft_in1k	.389
111	resnet50_st_wzc_noseed_2_323	.389
112	briaai_rmbg_1_4	.389
113	resnet50_ecoset_full	.389
114	yudixie_resnet50_translation_reg_0_240908	.389
115	vit_base_patch16_dinov3-lvd1689m	.389
116	yudixie_resnet18_object_class_0_240719	.389
117	resnet_50_v1_spiking_l3	.389
118	ReAlnet02_ALL	.389
119	mobilenet_v2_1_0_224	.389
120	yudixie_resnet50_imagenet1kpret_0_240908	.389
121	resnet50_ewconplus_wzc_2_323	.389
122	yudixie_wbh_object_class_scaling_0415_run_3	.389
123	predictive_coding_pc	.354
124	convnext_small_imagenet_10_seed-0	.353
125	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.353
126	efficientnet_b2_imagenet_full	.353
127	resnet50-SIN_IN_IN	.353
128	compact_resnet50_robust_V4	.353
129	ReAlnet01_ALL	.353
130	resnet50-vicreg	.353
131	resnet50-moclr8deg	.353
132	voneresnet-50-robust	.353
133	alexnet_training_seed_01_fov16	.353
134	BiT-S-R152x4	.353
135	resnet50-vicregl0p75	.353
136	resnet_152_v1	.353
137	yudixie_wbh_object_class_scaling_0415_run_0	.353
138	AdvProp_efficientnet-b2	.322
139	AT_efficientnet-b2	.322
140	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.320
141	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.320
142	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.320
143	vit_large_patch14_clip_224:openai_ft_in1k	.320
144	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.320
145	yudixie_resnet50_category_class_0_240908	.320
146	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.320
147	resnet34_ecoset_full	.320
148	alexnet_training_seed_01	.320
149	yudixie_resnet18_imagenet1kpret_0_240719	.320
150	resnet50_st_wzc_seed6_2_323	.320
151	deepeisnn_ei_cifar10_resnet18	.292
152	ReAlnet06	.291
153	inception_v3	.291
154	alexnet_fov12	.291
155	resnet50-SIN	.291
156	yudixie_resnet18_distance_rotation_0_240719	.291
157	compact_vgg19_V4	.291
158	ReAlnet06_cornet	.291
159	ReAlnet01_cornet_fov16	.291
160	nasnet_mobile	.291
161	compact_cornets_V4	.291
162	resnet_101_v1	.291
163	compact_resnet50_robust_IT	.291
164	cvt_cvt-21-384-in1k_4_LucyV4	.264
165	deit_small_imagenet_full_seed-0	.264
166	resnet_50_v1_spiking	.264
167	deit_large_imagenet_full_seed-0	.264
168	resnet_50_v1_spiking_l4a	.264
169	compact_resnet50_V4	.264
170	resnet50_imagenet_1_seed-0	.264
171	efficientnet_b0_imagenet_full	.264
172	yudixie_resnet18_random_0_240719	.264
173	grcnn	.264
174	yudixie_resnet50_object_class_0_240908	.264
175	densenet-169	.264
176	yudixie_wbh_object_class_scaling_0415_run_2	.264
177	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.240
178	omnivore_swinS	.240
179	resnet50_imagenet_10_seed-0	.239
180	alexnet_training_seed_05	.239
181	resnet34_imagenet_full	.239
182	resnet50_ewconplus_wzc	.239
183	mobilenet_v2_1_0_192	.239
184	resnet50_ewconplus_wzc_seed7_2_323	.239
185	resnet50_st_wzc_seed5_2_323	.239
186	efficientnet_b0	.239
187	resnet50-VITO-8deg-cc	.239
188	resnet50-vitoimagevidnet8	.239
189	mobilenet_v2_1_3_224	.239
190	vonegrcnn_47e	.239
191	ReAlnet07	.217
192	mobilenet_v2_0_5_192	.217
193	alexnet_fov4	.217
194	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.217
195	ReAlnet07_cornet	.217
196	compact_cornets_IT	.217
197	resnet_SIN_IN_FT_IN	.217
198	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.217
199	resnet50_ewconplus_wzc_seed3_2_323	.217
200	resnet50_st_wzc_seed7_2_323	.217
201	pnasnet_large_halfPrecision	.198
202	pnasnet_large	.198
203	AdvProp_efficientnet-b7	.198
204	alexnet_training_seed_08	.197
205	resnet50_tutorial	.197
206	vgg_16	.197
207	aughost_fair_resnet18	.197
208	resnet50_ewconplus_noseed_2_323	.197
209	resnet50_imagenet_full	.179
210	focalnet_tiny_lrf_in1k	.179
211	custom_model_cv_18_dagger_408	.179
212	artResNet18_1	.179
213	alexnet_training_seed_01_fov4	.179
214	alexnet_training_seed_03	.179
215	mobilenet_v2_1_0_160	.162
216	ReAlnet09	.162
217	alexnet_training_seed_10	.162
218	efficientnet_b1_imagenet_full	.162
219	ReAlnet09_cornet	.162
220	alexnet_training_seed_04	.147
221	ReAlnet10	.147
222	alexnet	.147
223	yudixie_resnet50_rotation_reg_0_240908	.147
224	compact_vgg19_IT	.147
225	ReAlnet10_cornet	.147
226	resnet50_st_wzc_seed4_2_323	.134
227	convnext_small_imagenet_100_seed-0	.134
228	AlexNet_SIN_fov4	.134
229	resnet50-SIN_IN	.121
230	deit_small_imagenet_1_seed-0	.121
231	ReAlnet03_ALL	.121
232	yudixie_resnet50_distance_translation_rotation_0_240908	.121
233	ReAlnet08_ALL	.121
234	AlexNet_SIN	.110
235	densenet-201	.110
236	resnet152_imagenet_full	.110
237	compact_resnet50_IT	.110
238	yudixie_resnet50_distance_rotation_0_240908	.110
239	ReAlnet05_ALL	.110
240	VOneCORnet-S	.100
241	yudixie_resnet18_category_class_0_240719	.100
242	ReAlnet04_ALL	.100
243	ReAlnet09_ALL	.100
244	ReAlnet05	.091
245	ReAlnet05_cornet	.091
246	ReAlnet07_ALL	.091
247	resnet_50_v1_spiking_l4	.082
248	yudixie_resnet18_translation_rotation_0_240719	.082
249	yudixie_resnet50_distance_translation_0_240908	.082
250	resnet50-barlow	.082
251	resnet18_ecoset_full	.075
252	ReAlnet08	.075
253	ReAlnet08_cornet	.075
254	ReAlnet03	.068
255	ReAlnet02_cornet	.068
256	alexnet_training_seed_07	.068
257	ReAlnet03_cornet	.068
258	yudixie_resnet18_distance_reg_0_240719	.068
259	imagenet_l2_3_0	.068
260	resnet50_robust_l2_eps3	.068
261	ReAlnet02	.068
262	resnet50_imagenet_100_seed-0	.062
263	resnet-18_test_m	.062
264	ReAlnet01_cornet_fov12	.056
265	AlexNet_SIN_fov16	.056
266	ReAlnet06_ALL	.056
267	yudixie_wbh_object_class_scaling_0415_imagenet	.056
268	blt_vs	.046
269	resnet18_imagenet_full	.046
270	ReAlnet04_cornet	.046
271	resnet50_robust_l2_eps1	.046
272	ReAlnet04	.046
273	ReAlnet01_cornet	.034
274	ReAlnet01	.034
275	inception_v1	.031
276	ReAlnet01_cornet_fov4	.026
277	resnet-152_v2_pytorch	.021
278	resnet_152_v2	.014
279	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