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

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