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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024juncture-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	alexnet_fov16	1.0
2	resnet50-vitoimagevidnet8	1.0
3	resnet50-VITO-8deg-cc	1.0
4	resnet18_ecoset_full	1.0
5	resnet50_ewconplus_wzc	1.0
6	ReAlnet04	1.0
7	efficientnet_b1_imagenet_full	1.0
8	ReAlnet04_cornet	1.0
9	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.984
10	resnet34_ecoset_full	.984
11	resnet50-vicreg	.984
12	AdvProp_efficientnet-b6	.951
13	convnext_small_imagenet_full_seed-0	.951
14	vonegrcnn_47e	.951
15	ReAlnet08	.848
16	cv_18_dagger_408_pretrained	.848
17	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.848
18	ReAlnet08_cornet	.848
19	convnext_base:clip_laiona_augreg_ft_in1k_384	.848
20	yudixie_resnet18_distance_translation_rotation_0_240719	.819
21	yudixie_resnet50_rotation_reg_0_240908	.819
22	shufflenet_v2_x1_0	.819
23	ReAlnet01_cornet_fov16	.819
24	vgg_19	.819
25	cvt_cvt-13-224-in1k_4_LucyV4	.730
26	resnext101_32x32d_wsl	.730
27	ReAlnet01_cornet_fov4	.730
28	nasnet_large	.730
29	efficientnet_b0	.730
30	ReAlnet10	.706
31	ReAlnet01_cornet	.706
32	convnext_small_imagenet_10_seed-0	.706
33	resnet50_imagenet_100_seed-0	.706
34	ReAlnet01_ALL	.706
35	ReAlnet02_ALL	.706
36	ReAlnet01	.706
37	ReAlnet10_cornet	.706
38	vonegrcnn_52e_full	.706
39	deit_small_imagenet_full_seed-0	.629
40	convnext_large_imagenet_full_seed-0	.608
41	resnet152_imagenet_full	.608
42	pnasnet_large	.608
43	mobilenet_v2_1_3_224	.608
44	antialiased-r50	.608
45	convnext_tiny_sup	.608
46	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.542
47	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.524
48	resnet50_imagenet_10_seed-0	.524
49	vit_relpos_base_patch16_clsgap_224:sw_in1k	.524
50	resnet_50_v1_spiking_l4a	.524
51	yudixie_resnet50_distance_reg_0_240908	.524
52	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.524
53	resnet101_ecoset_full	.524
54	ReAlnet06	.467
55	ReAlnet06_cornet	.467
56	convnext_tiny_imagenet_full_seed-0	.451
57	resnet50-SIN_IN_IN	.451
58	AlexNet_SIN_fov4	.451
59	ReAlnet09_ALL	.451
60	yudixie_resnet50_category_class_0_240908	.389
61	resnext101_32x48d_wsl	.389
62	focalnet_tiny_lrf_in1k	.389
63	inception_v1	.389
64	ReAlnet04_ALL	.389
65	AdvProp_efficientnet-b2	.389
66	AT_efficientnet-b2	.389
67	mobilenet_v2_1-4_224_pytorch	.346
68	mobilenet_v2_1_4_224	.346
69	resnet50-vicregl0p75	.346
70	ReAlnet05	.335
71	yudixie_resnet18_distance_rotation_0_240719	.335
72	ReAlnet05_cornet	.335
73	resnet50_robust_l2_eps1	.335
74	resnet_50_v1	.335
75	resnet50-sup	.335
76	AdvProp_efficientnet-b4	.335
77	convnext_femto_ols:d1_in1k	.335
78	mobilenet_v2_0_5_192	.298
79	resnet50-SIN_IN	.298
80	mobilenet_v2_1_0_192	.288
81	convnext_small_imagenet_1_seed-0	.288
82	resnext101_32x8d_wsl	.288
83	resnet50-vicregl0p9	.288
84	resnet_152_v1	.288
85	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.257
86	resnet50_imagenet_full	.248
87	mobilenet_v2_0_75_224	.248
88	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.248
89	alexnet_fov12	.248
90	resnet50_tutorial	.248
91	ReAlnet03_ALL	.248
92	ReAlnet06_ALL	.248
93	alexnet	.248
94	convnext_small_imagenet_100_seed-0	.214
95	ReAlnet01_cornet_fov12	.214
96	Res2Net50_26w_4s	.214
97	ReAlnet03	.191
98	ReAlnet03_cornet	.191
99	yudixie_resnet50_distance_rotation_0_240908	.191
100	convnext_xlarge:fb_in22k_ft_in1k	.184
101	cvt_cvt-21-224-in1k_4_LucyV4	.184
102	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.184
103	voneresnet-50-non_stochastic	.184
104	resnet-152_v2_pytorch	.184
105	resnet50_ecoset_full	.184
106	xception	.184
107	deit_small_imagenet_1_seed-0	.184
108	VOneCORnet-S	.184
109	resnet50-moclr8deg	.184
110	BiT-S-R50x1	.184
111	resnet50-SIN	.164
112	resnet_50_v2	.159
113	alexnet_fov4	.159
114	efficientnet_b2_imagenet_full	.159
115	custom_model_cv_18_dagger_408	.159
116	yudixie_resnet18_distance_translation_0_240719	.159
117	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.159
118	compact_vgg19_IT	.159
119	ReAlnet08_ALL	.159
120	alexnet_training_seed_03	.159
121	mobilenet_v2_1_0_160	.137
122	resnet152_ecoset_full	.137
123	resnet-50-robust	.137
124	yudixie_resnet18_object_class_0_240719	.137
125	nasnet_mobile	.137
126	omnivore_swinS	.137
127	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.118
128	yudixie_resnet50_object_class_0_240908	.118
129	deit_base_imagenet_full_seed-0	.118
130	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.118
131	AlexNet_SIN	.118
132	yolos_tiny	.118
133	ViT_L_32_imagenet1k	.118
134	briaai_rmbg_1_4	.118
135	yudixie_resnet50_random_0_240908	.118
136	yudixie_resnet50_imagenet1kpret_0_240908	.118
137	resnet50_primary_visual_cortex	.118
138	convnext_tiny:in12k_ft_in1k	.105
139	resnet_50_v1_spiking	.102
140	resnet34_imagenet_full	.102
141	efficientnet_b0_imagenet_full	.102
142	convnext_base_imagenet_full_seed-0	.102
143	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.102
144	vit_base_patch16_clip_224:openai_ft_in1k	.102
145	yudixie_resnet18_distance_reg_0_240719	.102
146	voneresnet-50-robust	.102
147	imagenet_l2_3_0	.102
148	resnet50_robust_l2_eps3	.102
149	BiT-S-R152x2	.102
150	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.087
151	AdvProp_efficientnet-b8	.087
152	convnext_large:fb_in22k_ft_in1k	.087
153	resnet18_imagenet_full	.078
154	ReAlnet07	.075
155	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.075
156	ReAlnet09	.075
157	deit_small_imagenet_10_seed-0	.075
158	yudixie_resnet18_imagenet1kpret_0_240719	.075
159	ReAlnet07_cornet	.075
160	AlexNet_SIN_fov12	.075
161	ReAlnet09_cornet	.075
162	grcnn_109	.075
163	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.065
164	vit_large_patch14_clip_224:laion2b_ft_in1k	.065
165	inception_v4	.065
166	resnet_50_v1_spiking_l3	.065
167	resnet50-barlow	.065
168	vonegrcnn_62e_nobn	.065
169	ReAlnet07_ALL	.065
170	yudixie_resnet50_distance_translation_rotation_0_240908	.065
171	BiT-S-R50x3	.065
172	pnasnet_large_pytorch	.056
173	mobilenet_v2_0_75_192	.056
174	resnet101_imagenet_full	.056
175	resnet_101_v2	.056
176	AdvProp_efficientnet-b7	.056
177	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.048
178	mobilenet_v2_1_0_128	.048
179	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.048
180	AlexNet_SIN_fov16	.048
181	ReAlnet05_ALL	.048
182	resnet_SIN_IN_FT_IN	.048
183	densenet-169	.048
184	resnet_50_v1_spiking_l4	.041
185	alexnet_training_seed_07	.041
186	efficientnet-b7	.041
187	yudixie_resnet50_distance_translation_0_240908	.041
188	BiT-S-R101x1	.041
189	yudixie_resnet18_translation_rotation_0_240719	.036
190	yudixie_resnet50_translation_rotation_0_240908	.036
191	alexnet_training_seed_01_fov4	.036
192	voneresnet-50	.036
193	resnet-18_test_m	.031
194	grcnn	.031
195	resnet_152_v2	.031
196	BiT-S-R101x3	.031
197	densenet-201	.027
198	mobilenet_v2_0_75_160	.027
199	yudixie_resnet18_category_class_0_240719	.027
200	yudixie_resnet18_random_0_240719	.027
201	ReAlnet02_cornet	.023
202	ReAlnet02	.023
203	alexnet_training_seed_09	.020
204	vit_large_patch14_clip_224:openai_ft_in1k	.020
205	alexnet_training_seed_04	.020
206	resnet_101_v1	.020
207	compact_resnet50_robust_IT	.020
208	alexnet_training_seed_10	.017
209	yudixie_resnet18_translation_reg_0_240719	.017
210	inception_v3	.015
211	deit_large_imagenet_full_seed-0	.015
212	resnet50_imagenet_1_seed-0	.015
213	alexnet_training_seed_01	.015
214	compact_vgg19_V4	.015
215	alexnet_training_seed_02	.013
216	mobilenet_v2_0_5_224	.013
217	yudixie_resnet18_rotation_reg_0_240719	.013
218	omnivore_swinB	.013
219	densenet-121	.013
220	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.011
221	vit_large_patch14_clip_336:laion2b_ft_in1k	.011
222	compact_cornets_V4	.011
223	alexnet_training_seed_06	.009
224	yudixie_resnet50_translation_reg_0_240908	.009
225	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.009
226	alexnet_training_seed_01_fov12	.009
227	ReAlnet10_ALL	.008
228	compact_resnet50_IT	.008
229	vgg_16	.008
230	deit_small_imagenet_100_seed-0	.006
231	cvt_cvt-13-384-in1k_4_LucyV4	.006
232	artResNet18_1	.005
233	effnetb1_272x240	.004
234	compact_resnet50_V4	.004
235	alexnet_training_seed_05	.004
236	BiT-S-R152x4	.004
237	blt_vs	.003
238	alexnet_training_seed_08	.003
239	alexnet_training_seed_01_fov16	.003
240	compact_cornets_IT	.003
241	cvt_cvt-21-384-in1k_4_LucyV4	.002
242	compact_resnet50_robust_V4	.001
243	hmax
244	mobilenet_v2_1_0_224

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

0.77.

Note that scores are relative to this ceiling.

Data: Ferguson2024juncture

Metric: value_delta