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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	yudixie_resnet50_translation_rotation_0_240908	1.0
7	BiT-S-R101x3	1.0
8	alexnet_training_seed_09	1.0
9	convnext_tiny_imagenet_full_seed-0	1.0
10	voneresnet-50-non_stochastic	1.0
11	nasnet_large	1.0
12	efficientnet-b7	1.0
13	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	1.0
14	BiT-S-R50x1	1.0
15	cvt_cvt-21-224-in1k_4_LucyV4	.935
16	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.935
17	mobilenet_v2_0_75_192	.931
18	resnext101_32x8d_wsl	.931
19	vit_base_patch16_clip_224:openai_ft_in1k	.931
20	convnext_tiny_sup	.931
21	convnext_small_imagenet_full_seed-0	.848
22	BiT-S-R152x2	.848
23	pnasnet_large_pytorch	.845
24	yudixie_resnet18_distance_translation_rotation_0_240719	.845
25	yudixie_resnet18_translation_reg_0_240719	.845
26	grcnn_109	.845
27	yudixie_resnet50_random_0_240908	.845
28	vonegrcnn_52e_full	.845
29	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.770
30	convnext_small_imagenet_1_seed-0	.767
31	convnext_large_imagenet_full_seed-0	.767
32	BiT-S-R50x3	.767
33	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.699
34	deit_base_imagenet_full_seed-0	.696
35	deit_small_imagenet_100_seed-0	.696
36	mobilenet_v2_1-4_224_pytorch	.696
37	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.696
38	mobilenet_v2_1_4_224	.696
39	AdvProp_efficientnet-b4	.696
40	AdvProp_efficientnet-b8	.634
41	alexnet_training_seed_02	.632
42	mobilenet_v2_0_75_160	.632
43	cvt_cvt-13-224-in1k_4_LucyV4	.632
44	resnet_101_v2	.632
45	convnext_femto_ols:d1_in1k	.632
46	resnext101_32x48d_wsl	.573
47	convnext_base:clip_laiona_augreg_ft_in1k_384	.573
48	alexnet_training_seed_06	.573
49	mobilenet_v2_0_75_224	.573
50	vit_large_patch14_clip_336:laion2b_ft_in1k	.573
51	vit_relpos_base_patch16_clsgap_224:sw_in1k	.573
52	yudixie_resnet50_distance_reg_0_240908	.573
53	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.573
54	vit_large_patch14_clip_224:laion2b_ft_in1k	.573
55	convnext_base_imagenet_full_seed-0	.522
56	resnet152_ecoset_full	.522
57	omnivore_swinB	.522
58	vonegrcnn_62e_nobn	.522
59	yolos_tiny	.520
60	AdvProp_efficientnet-b6	.520
61	cvt_cvt-13-384-in1k_4_LucyV4	.520
62	resnet101_ecoset_full	.520
63	yudixie_resnet18_rotation_reg_0_240719	.520
64	Res2Net50_26w_4s	.520
65	vgg_19	.520
66	resnet50-vicregl0p9	.520
67	densenet-121	.520
68	resnet50_primary_visual_cortex	.520
69	convnext_large:fb_in22k_ft_in1k	.520
70	mobilenet_v2_1_0_128	.472
71	resnet-50-robust	.472
72	ViT_L_32_imagenet1k	.472
73	xception	.472
74	antialiased-r50	.472
75	resnet_50_v1	.430
76	resnet50-sup	.430
77	ReAlnet10_ALL	.428
78	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.428
79	convnext_tiny:in12k_ft_in1k	.428
80	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.428
81	mobilenet_v2_0_5_224	.428
82	inception_v4	.428
83	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.428
84	voneresnet-50	.428
85	BiT-S-R101x1	.428
86	resnet_50_v2	.428
87	resnext101_32x32d_wsl	.390
88	convnext_xlarge:fb_in22k_ft_in1k	.389
89	briaai_rmbg_1_4	.389
90	resnet50_ecoset_full	.389
91	yudixie_resnet50_translation_reg_0_240908	.389
92	yudixie_resnet18_object_class_0_240719	.389
93	resnet_50_v1_spiking_l3	.389
94	ReAlnet02_ALL	.389
95	mobilenet_v2_1_0_224	.389
96	yudixie_resnet50_imagenet1kpret_0_240908	.389
97	convnext_small_imagenet_10_seed-0	.353
98	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.353
99	efficientnet_b2_imagenet_full	.353
100	resnet50-SIN_IN_IN	.353
101	compact_resnet50_robust_V4	.353
102	ReAlnet01_ALL	.353
103	resnet50-vicreg	.353
104	resnet50-moclr8deg	.353
105	voneresnet-50-robust	.353
106	BiT-S-R152x4	.353
107	resnet50-vicregl0p75	.353
108	resnet_152_v1	.353
109	AdvProp_efficientnet-b2	.322
110	AT_efficientnet-b2	.322
111	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.320
112	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.320
113	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.320
114	vit_large_patch14_clip_224:openai_ft_in1k	.320
115	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.320
116	yudixie_resnet50_category_class_0_240908	.320
117	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.320
118	resnet34_ecoset_full	.320
119	alexnet_training_seed_01	.320
120	yudixie_resnet18_imagenet1kpret_0_240719	.320
121	ReAlnet06	.291
122	inception_v3	.291
123	resnet50-SIN	.291
124	yudixie_resnet18_distance_rotation_0_240719	.291
125	compact_vgg19_V4	.291
126	ReAlnet06_cornet	.291
127	nasnet_mobile	.291
128	compact_cornets_V4	.291
129	resnet_101_v1	.291
130	compact_resnet50_robust_IT	.291
131	cvt_cvt-21-384-in1k_4_LucyV4	.264
132	deit_small_imagenet_full_seed-0	.264
133	resnet_50_v1_spiking	.264
134	deit_large_imagenet_full_seed-0	.264
135	resnet_50_v1_spiking_l4a	.264
136	compact_resnet50_V4	.264
137	resnet50_imagenet_1_seed-0	.264
138	efficientnet_b0_imagenet_full	.264
139	yudixie_resnet18_random_0_240719	.264
140	grcnn	.264
141	yudixie_resnet50_object_class_0_240908	.264
142	densenet-169	.264
143	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.240
144	omnivore_swinS	.240
145	resnet50_imagenet_10_seed-0	.239
146	alexnet_training_seed_05	.239
147	resnet34_imagenet_full	.239
148	resnet50_ewconplus_wzc	.239
149	mobilenet_v2_1_0_192	.239
150	efficientnet_b0	.239
151	resnet50-VITO-8deg-cc	.239
152	resnet50-vitoimagevidnet8	.239
153	mobilenet_v2_1_3_224	.239
154	vonegrcnn_47e	.239
155	ReAlnet07	.217
156	mobilenet_v2_0_5_192	.217
157	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.217
158	ReAlnet07_cornet	.217
159	compact_cornets_IT	.217
160	resnet_SIN_IN_FT_IN	.217
161	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.217
162	pnasnet_large	.198
163	AdvProp_efficientnet-b7	.198
164	alexnet_training_seed_08	.197
165	resnet50_tutorial	.197
166	vgg_16	.197
167	resnet50_imagenet_full	.179
168	focalnet_tiny_lrf_in1k	.179
169	custom_model_cv_18_dagger_408	.179
170	artResNet18_1	.179
171	alexnet_training_seed_03	.179
172	mobilenet_v2_1_0_160	.162
173	ReAlnet09	.162
174	alexnet_training_seed_10	.162
175	efficientnet_b1_imagenet_full	.162
176	ReAlnet09_cornet	.162
177	alexnet_training_seed_04	.147
178	ReAlnet10	.147
179	yudixie_resnet50_rotation_reg_0_240908	.147
180	compact_vgg19_IT	.147
181	ReAlnet10_cornet	.147
182	convnext_small_imagenet_100_seed-0	.134
183	resnet50-SIN_IN	.121
184	deit_small_imagenet_1_seed-0	.121
185	ReAlnet03_ALL	.121
186	yudixie_resnet50_distance_translation_rotation_0_240908	.121
187	ReAlnet08_ALL	.121
188	densenet-201	.110
189	resnet152_imagenet_full	.110
190	compact_resnet50_IT	.110
191	yudixie_resnet50_distance_rotation_0_240908	.110
192	ReAlnet05_ALL	.110
193	VOneCORnet-S	.100
194	yudixie_resnet18_category_class_0_240719	.100
195	ReAlnet04_ALL	.100
196	ReAlnet09_ALL	.100
197	ReAlnet05	.091
198	ReAlnet05_cornet	.091
199	ReAlnet07_ALL	.091
200	resnet_50_v1_spiking_l4	.082
201	yudixie_resnet18_translation_rotation_0_240719	.082
202	yudixie_resnet50_distance_translation_0_240908	.082
203	resnet50-barlow	.082
204	resnet18_ecoset_full	.075
205	ReAlnet08	.075
206	ReAlnet08_cornet	.075
207	ReAlnet03	.068
208	ReAlnet02_cornet	.068
209	alexnet_training_seed_07	.068
210	ReAlnet03_cornet	.068
211	yudixie_resnet18_distance_reg_0_240719	.068
212	imagenet_l2_3_0	.068
213	resnet50_robust_l2_eps3	.068
214	ReAlnet02	.068
215	resnet50_imagenet_100_seed-0	.062
216	resnet-18_test_m	.062
217	ReAlnet06_ALL	.056
218	blt_vs	.046
219	resnet18_imagenet_full	.046
220	ReAlnet04_cornet	.046
221	resnet50_robust_l2_eps1	.046
222	ReAlnet04	.046
223	ReAlnet01_cornet	.034
224	ReAlnet01	.034
225	inception_v1	.031
226	resnet-152_v2_pytorch	.021
227	resnet_152_v2	.014
228	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