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