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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024quarter-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	mobilenet_v2_0_5_224	1.0
2	yudixie_resnet50_translation_reg_0_240908	1.0
3	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
4	omnivore_swinB	1.0
5	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	1.0
6	resnet50_primary_visual_cortex	1.0
7	mobilenet_v2_0_75_160	1.0
8	cv_18_dagger_408_pretrained	1.0
9	alexnet_training_seed_04	1.0
10	resnet_50_v1	1.0
11	resnet50-sup	1.0
12	resnet50_ecoset_full	1.0
13	resnext101_32x8d_wsl	1.0
14	resnet_152_v1	.950
15	inception_v4	.950
16	mobilenet_v2_0_75_192	.950
17	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.950
18	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.950
19	convnext_large:fb_in22k_ft_in1k	.950
20	deit_large_imagenet_full_seed-0	.950
21	resnet34_ecoset_full	.950
22	resnet18_imagenet_full	.950
23	ViT_L_32_imagenet1k	.950
24	vit_base_patch16_dinov3:lvd1689m	.950
25	effnetb1_272x240	.950
26	BiT-S-R152x2	.923
27	resnet18_ecoset_full	.923
28	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.923
29	resnet_50_v1_spiking_l4a	.923
30	resnext101_32x48d_wsl	.923
31	cvt_cvt-21-224-in1k_4_LucyV4	.860
32	mobilenet_v2_1_0_224	.836
33	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.836
34	convnext_tiny:in12k_ft_in1k	.836
35	resnet_SIN_IN_FT_IN	.836
36	antialiased-r50	.836
37	resnet50_tutorial	.836
38	ReAlnet01_cornet_fov16	.836
39	vit_large_patch14_clip_336:laion2b_ft_in1k	.779
40	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.779
41	yudixie_resnet50_category_class_0_240908	.757
42	grcnn	.757
43	efficientnet_b0	.757
44	deit_small_imagenet_1_seed-0	.757
45	ReAlnet01_cornet_fov4	.757
46	alexnet_training_seed_05	.706
47	BiT-S-R152x4	.706
48	yudixie_resnet18_distance_reg_0_240719	.706
49	yudixie_resnet18_imagenet1kpret_0_240719	.706
50	cvt_cvt-21-384-in1k_4_LucyV4	.706
51	resnet_50_v1_spiking	.686
52	yudixie_resnet50_distance_translation_rotation_0_240908	.686
53	resnet_50_v2	.686
54	AT_efficientnet-b2	.686
55	AdvProp_efficientnet-b2	.686
56	convnext_large_imagenet_full_seed-0	.686
57	convnext_base_imagenet_full_seed-0	.686
58	voneresnet-50-non_stochastic	.686
59	convnext_xlarge:fb_in22k_ft_in1k	.639
60	efficientnet-b7	.621
61	convnext_tiny_sup	.621
62	convnext_small_imagenet_full_seed-0	.621
63	artResNet18_1	.579
64	alexnet_training_seed_10	.563
65	alexnet_training_seed_06	.563
66	vonegrcnn_62e_nobn	.563
67	resnet50_robust_l2_eps3	.563
68	imagenet_l2_3_0	.563
69	AdvProp_efficientnet-b7	.563
70	efficientnet_b2_imagenet_full	.563
71	resnet50_imagenet_1_seed-0	.563
72	cvt_cvt-13-224-in1k_4_LucyV4	.524
73	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.524
74	yudixie_resnet50_imagenet1kpret_0_240908	.510
75	mobilenet_v2_1_0_128	.510
76	vgg_16	.510
77	resnet50-SIN_IN_IN	.510
78	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.510
79	densenet-201	.510
80	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.510
81	resnet50_robust_l2_eps1	.510
82	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.510
83	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.510
84	densenet-121	.510
85	resnet50-vicregl0p75	.510
86	BiT-S-R50x3	.510
87	deit_base_imagenet_full_seed-0	.510
88	resnet50_imagenet_100_seed-0	.510
89	resnet101_imagenet_full	.510
90	vit_base_patch14_dinov2:lvd142m	.510
91	ReAlnet01_cornet_fov12	.510
92	resnet50-vicreg	.475
93	BiT-S-R50x1	.475
94	deit_small_imagenet_full_seed-0	.475
95	custom_model_cv_18_dagger_408	.475
96	resnet_50_v1_spiking_l3	.475
97	resnext101_32x32d_wsl	.475
98	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.475
99	inception_v3	.461
100	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.461
101	densenet-169	.461
102	vonegrcnn_47e	.461
103	resnet-152_v2_pytorch	.461
104	AlexNet_SIN	.461
105	mobilenet_v2_1_0_160	.430
106	yudixie_resnet50_rotation_reg_0_240908	.430
107	vit_relpos_base_patch16_clsgap_224:sw_in1k	.430
108	yudixie_resnet18_random_0_240719	.430
109	mobilenet_v2_0_5_192	.418
110	grcnn_109	.418
111	Res2Net50_26w_4s	.418
112	yudixie_resnet18_distance_rotation_0_240719	.418
113	vit_base_patch16_clip_224:openai_ft_in1k	.390
114	yudixie_resnet18_distance_translation_0_240719	.390
115	vit_large_patch14_clip_224:openai_ft_in1k	.379
116	convnext_base:clip_laiona_augreg_ft_in1k_384	.379
117	blt_vs	.379
118	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.379
119	resnet50_imagenet_10_seed-0	.379
120	focalnet_tiny_lrf_in1k	.379
121	alexnet_training_seed_01_fov4	.379
122	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.353
123	xception	.343
124	yudixie_resnet18_distance_translation_rotation_0_240719	.343
125	resnet-18_test_m	.320
126	briaai_rmbg_1_4	.311
127	alexnet_training_seed_02	.311
128	ReAlnet05_cornet	.311
129	yudixie_resnet50_distance_rotation_0_240908	.311
130	ReAlnet05	.311
131	compact_resnet50_IT	.311
132	nasnet_mobile	.289
133	resnet152_ecoset_full	.289
134	yudixie_resnet50_distance_translation_0_240908	.281
135	deit_small_imagenet_100_seed-0	.281
136	deit_small_imagenet_10_seed-0	.281
137	efficientnet_b1_imagenet_full	.281
138	alexnet_fov16	.281
139	alexnet_fov12	.281
140	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.262
141	cvt_cvt-13-384-in1k_4_LucyV4	.262
142	ReAlnet07_cornet	.255
143	vgg_19	.255
144	ReAlnet07	.255
145	resnet50-SIN	.255
146	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.255
147	resnet152_imagenet_full	.255
148	alexnet	.255
149	vit_base_patch14_reg4_dinov2:lvd142m	.255
150	alexnet_training_seed_01_fov12	.255
151	compact_cornets_IT	.255
152	compact_cornets_V4	.255
153	alexnet_workflow_trigger	.255
154	mobilenet_v2_1_4_224	.231
155	resnet50-barlow	.231
156	BiT-S-R101x3	.231
157	yudixie_resnet18_translation_rotation_0_240719	.231
158	resnet34_imagenet_full	.231
159	mobilenet_v2_1-4_224_pytorch	.231
160	resnet-50-robust	.231
161	AlexNet_SIN_fov12	.231
162	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.215
163	yudixie_resnet50_distance_reg_0_240908	.209
164	ReAlnet10_cornet	.209
165	ReAlnet01_cornet	.209
166	ReAlnet10	.209
167	ReAlnet01	.209
168	VOneCORnet-S	.209
169	efficientnet_b0_imagenet_full	.209
170	alexnet_training_seed_01_fov16	.209
171	BiT-S-R101x1	.189
172	convnext_small_imagenet_1_seed-0	.189
173	resnet50_imagenet_full	.189
174	alexnet_fov4	.189
175	alexnet_training_seed_07	.171
176	ReAlnet09_cornet	.171
177	ReAlnet03_cornet	.171
178	resnet_101_v1	.171
179	inception_v1	.171
180	yudixie_resnet50_translation_rotation_0_240908	.171
181	ReAlnet09	.171
182	ReAlnet03	.171
183	shufflenet_v2_x1_0	.171
184	omnivore_swinS	.171
185	resnet101_ecoset_full	.171
186	resnet50_ewconplus_wzc	.171
187	ReAlnet04_cornet	.155
188	ReAlnet02_cornet	.155
189	resnet_152_v2	.155
190	ReAlnet02	.155
191	ReAlnet04	.155
192	voneresnet-50	.155
193	resnet50-vicregl0p9	.155
194	convnext_small_imagenet_10_seed-0	.155
195	yudixie_resnet50_object_class_0_240908	.141
196	alexnet_training_seed_01	.141
197	ReAlnet06_cornet	.141
198	mobilenet_v2_1_0_192	.141
199	ReAlnet06	.141
200	resnet50-vitoimagevidnet8	.141
201	resnet50-VITO-8deg-cc	.141
202	convnext_femto_ols:d1_in1k	.141
203	AdvProp_efficientnet-b8	.141
204	convnext_small_imagenet_100_seed-0	.141
205	AlexNet_SIN_fov16	.141
206	resnet_50_v1_spiking_l4	.127
207	yudixie_resnet50_random_0_240908	.127
208	ReAlnet08_cornet	.127
209	ReAlnet08	.127
210	compact_resnet50_robust_V4	.127
211	ReAlnet09_ALL	.127
212	pnasnet_large_pytorch	.127
213	resnet_101_v2	.119
214	yolos_tiny	.115
215	mobilenet_v2_1_3_224	.115
216	alexnet_training_seed_08	.115
217	compact_vgg19_IT	.115
218	AdvProp_efficientnet-b4	.115
219	yudixie_resnet18_object_class_0_240719	.104
220	ReAlnet07_ALL	.104
221	alexnet_training_seed_03	.095
222	vonegrcnn_52e_full	.095
223	yudixie_resnet18_translation_reg_0_240719	.095
224	compact_resnet50_robust_IT	.095
225	ReAlnet08_ALL	.095
226	yudixie_resnet18_rotation_reg_0_240719	.086
227	alexnet_training_seed_09	.078
228	voneresnet-50-robust	.078
229	resnet50-SIN_IN	.078
230	compact_resnet50_V4	.078
231	ReAlnet03_ALL	.078
232	convnext_tiny_imagenet_full_seed-0	.070
233	ReAlnet04_ALL	.070
234	resnet50-moclr8deg	.064
235	yudixie_resnet18_category_class_0_240719	.064
236	ReAlnet05_ALL	.064
237	ReAlnet01_ALL	.064
238	ReAlnet10_ALL	.058
239	ReAlnet06_ALL	.052
240	nasnet_large	.047
241	AdvProp_efficientnet-b6	.043
242	compact_vgg19_V4	.043
243	AlexNet_SIN_fov4	.021
244	ReAlnet02_ALL	.019
245	pnasnet_large
246	mobilenet_v2_0_75_224
247	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: Ferguson2024quarter

Metric: value_delta