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	resnet50-vitoimagevidnet8	1.0
2	resnet50_ecoset_full	1.0
3	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
4	alexnet_training_seed_02	1.0
5	resnet50-VITO-8deg-cc	1.0
6	efficientnet_b1_imagenet_full	1.0
7	convnext_tiny_imagenet_full_seed-0	1.0
8	alexnet_training_seed_01	1.0
9	omnivore_swinS	1.0
10	resnet-18_test_m	1.0
11	ReAlnet08	1.0
12	resnet50_tutorial	1.0
13	swin_small_patch4_window7_224:ms_in22k_ft_in1k	1.0
14	pnasnet_large	1.0
15	ReAlnet08_cornet	1.0
16	yudixie_resnet50_random_0_240908	1.0
17	vonegrcnn_52e_full	1.0
18	resnet-50-robust	1.0
19	mobilenet_v2_0_75_224	1.0
20	resnet152_ecoset_full	.975
21	resnet_152_v1	.975
22	resnet_101_v2	.975
23	mobilenet_v2_0_5_224	.975
24	resnet152_imagenet_full	.975
25	yudixie_resnet18_category_class_0_240719	.917
26	alexnet_training_seed_05	.917
27	resnet50_imagenet_10_seed-0	.917
28	ReAlnet05	.917
29	convnext_small_imagenet_10_seed-0	.917
30	ReAlnet05_cornet	.917
31	resnet50-SIN_IN_IN	.917
32	resnet50-vicregl0p75	.883
33	mobilenet_v2_0_75_160	.883
34	densenet-169	.883
35	yudixie_resnet50_object_class_0_240908	.883
36	cvt_cvt-21-224-in1k_4_LucyV4	.883
37	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.883
38	grcnn_109	.831
39	resnet50-barlow	.831
40	mobilenet_v2_1_0_128	.831
41	mobilenet_v2_1-4_224_pytorch	.831
42	convnext_tiny_sup	.831
43	voneresnet-50-robust	.831
44	mobilenet_v2_1_4_224	.831
45	BiT-S-R101x3	.831
46	resnext101_32x32d_wsl	.831
47	cvt_cvt-13-224-in1k_4_LucyV4	.831
48	convnext_xlarge:fb_in22k_ft_in1k	.800
49	mobilenet_v2_1_0_192	.800
50	ReAlnet01	.800
51	resnet_SIN_IN_FT_IN	.800
52	vgg_16	.800
53	deit_large_imagenet_full_seed-0	.800
54	yudixie_resnet18_distance_reg_0_240719	.800
55	convnext_large:fb_in22k_ft_in1k	.800
56	ReAlnet01_cornet	.800
57	ReAlnet_incorrect	.800
58	convnext_base_imagenet_full_seed-0	.800
59	ReAlnet10_cornet	.753
60	vonegrcnn_62e_nobn	.753
61	convnext_small_imagenet_100_seed-0	.753
62	ReAlnet10	.753
63	vgg_19	.753
64	efficientnet_b0	.753
65	BiT-S-R152x2	.753
66	alexnet_training_seed_04	.725
67	inception_v3	.682
68	deit_small_imagenet_full_seed-0	.682
69	ReAlnet09	.682
70	convnext_small_imagenet_full_seed-0	.682
71	resnet101_ecoset_full	.682
72	yudixie_resnet50_translation_reg_0_240908	.682
73	omnivore_swinB	.682
74	ReAlnet09_cornet	.682
75	resnet_101_v1	.682
76	antialiased-r50	.682
77	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.657
78	custom_model_cv_18_dagger_408	.657
79	ViT_L_32_imagenet1k	.657
80	cvt_cvt-21-384-in1k_4_LucyV4	.657
81	AT_efficientnet-b2	.657
82	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.657
83	resnet50-SIN_IN	.657
84	AdvProp_efficientnet-b2	.657
85	AdvProp_efficientnet-b6	.657
86	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.657
87	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.657
88	AdvProp_efficientnet-b4	.657
89	convnext_large_imagenet_full_seed-0	.657
90	AdvProp_efficientnet-b7	.657
91	mobilenet_v2_0_75_192	.657
92	deit_base_imagenet_full_seed-0	.657
93	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.657
94	vit_large_patch14_clip_224:openai_ft_in1k	.657
95	vit_large_patch14_clip_336:laion2b_ft_in1k	.657
96	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.657
97	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.657
98	vit_relpos_base_patch16_clsgap_224:sw_in1k	.657
99	yudixie_resnet18_distance_translation_rotation_0_240719	.657
100	convnext_base:clip_laiona_augreg_ft_in1k_384	.657
101	efficientnet-b7	.657
102	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.657
103	resnet50-vicregl0p9	.657
104	cvt_cvt-13-384-in1k_4_LucyV4	.657
105	vit_base_patch16_clip_224:openai_ft_in1k	.657
106	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.657
107	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.657
108	BiT-S-R152x4	.657
109	AdvProp_efficientnet-b8	.657
110	ReAlnet04_cornet	.618
111	BiT-S-R101x1	.618
112	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.618
113	deit_small_imagenet_100_seed-0	.618
114	ReAlnet04	.618
115	yudixie_resnet50_distance_reg_0_240908	.595
116	yudixie_resnet18_rotation_reg_0_240719	.595
117	resnet50_imagenet_100_seed-0	.595
118	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.595
119	resnext101_32x48d_wsl	.560
120	resnet34_imagenet_full	.560
121	VOneCORnet-S	.539
122	deit_small_imagenet_10_seed-0	.539
123	deit_small_imagenet_1_seed-0	.539
124	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.539
125	mobilenet_v2_1_0_224	.539
126	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.507
127	resnet50_imagenet_1_seed-0	.507
128	BiT-S-R50x3	.507
129	hmax	.507
130	resnet_152_v2	.489
131	ReAlnet06_cornet	.489
132	yudixie_resnet50_rotation_reg_0_240908	.489
133	ReAlnet06	.489
134	efficientnet_b2_imagenet_full	.460
135	yudixie_resnet50_distance_translation_rotation_0_240908	.460
136	artResNet18_1	.460
137	resnext101_32x8d_wsl	.460
138	voneresnet-50-non_stochastic	.443
139	yudixie_resnet50_distance_rotation_0_240908	.443
140	yudixie_resnet18_translation_reg_0_240719	.443
141	resnet101_imagenet_full	.443
142	mobilenet_v2_0_5_192	.443
143	Res2Net50_26w_4s	.443
144	resnet50-vicreg	.443
145	resnet50-moclr8deg	.443
146	nasnet_large	.417
147	BiT-S-R50x1	.417
148	resnet50_imagenet_full	.417
149	alexnet_training_seed_03	.401
150	ReAlnet03_cornet	.401
151	convnext_small_imagenet_1_seed-0	.401
152	ReAlnet03	.401
153	convnext_tiny:in12k_ft_in1k	.401
154	imagenet_l2_3_0	.377
155	resnet50_robust_l2_eps3	.377
156	resnet50_robust_l2_eps1	.377
157	yudixie_resnet50_distance_translation_0_240908	.377
158	densenet-121	.377
159	alexnet_training_seed_08	.377
160	efficientnet_b0_imagenet_full	.377
161	yudixie_resnet50_imagenet1kpret_0_240908	.363
162	yudixie_resnet50_category_class_0_240908	.363
163	ReAlnet02	.363
164	ReAlnet02_cornet	.363
165	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.342
166	yudixie_resnet18_random_0_240719	.329
167	resnet50-sup	.329
168	resnet_50_v1	.329
169	resnet18_imagenet_full	.310
170	shufflenet_v2_x1_0	.310
171	mobilenet_v2_1_3_224	.310
172	inception_v4	.310
173	effnetb1_272x240	.310
174	resnet34_ecoset_full	.310
175	densenet-201	.298
176	vonegrcnn_47e	.281
177	nasnet_mobile	.281
178	resnet-152_v2_pytorch	.281
179	yudixie_resnet18_translation_rotation_0_240719	.270
180	alexnet_training_seed_10	.254
181	mobilenet_v2_1_0_160	.254
182	blt_vs	.245
183	ReAlnet07	.245
184	ReAlnet07_cornet	.245
185	resnet18_ecoset_full	.230
186	yudixie_resnet18_imagenet1kpret_0_240719	.230
187	yudixie_resnet18_distance_translation_0_240719	.222
188	resnet50-SIN	.222
189	voneresnet-50	.222
190	yudixie_resnet50_translation_rotation_0_240908	.222
191	yudixie_resnet18_distance_rotation_0_240719	.209
192	grcnn	.189
193	alexnet_training_seed_06	.182
194	pnasnet_large_pytorch	.171
195	alexnet_training_seed_09	.165
196	convnext_femto_ols:d1_in1k	.155
197	inception_v1	.141
198	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.141
199	alexnet_training_seed_07	.135
200	xception	.135
201	yudixie_resnet18_object_class_0_240719	.135
202	resnet_50_v2	.116
203	focalnet_tiny_lrf_in1k	X

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