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

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