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