Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024convergence-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	1.0
2	resnet18_ecoset_full	1.0
3	ReAlnet09	1.0
4	ReAlnet09_cornet	1.0
5	resnet101_imagenet_full	1.0
6	pnasnet_large_pytorch	1.0
7	resnet34_ecoset_full	1.0
8	convnext_small_imagenet_10_seed-0	1.0
9	yudixie_resnet50_random_0_240908	1.0
10	densenet-121	1.0
11	resnet-152_v2_pytorch	1.0
12	yudixie_resnet50_distance_translation_rotation_0_240908	1.0
13	resnet50_imagenet_1_seed-0	1.0
14	alexnet_training_seed_07	1.0
15	resnet50_imagenet_100_seed-0	1.0
16	artResNet18_1	1.0
17	resnet152_ecoset_full	.985
18	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.985
19	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.985
20	alexnet_training_seed_02	.925
21	alexnet_training_seed_08	.925
22	yudixie_resnet18_object_class_0_240719	.894
23	yudixie_resnet50_translation_reg_0_240908	.839
24	vit_large_patch14_clip_224:openai_ft_in1k	.839
25	vgg_19	.839
26	antialiased-r50	.839
27	convnext_base:clip_laiona_augreg_ft_in1k_384	.839
28	ReAlnet04	.811
29	ReAlnet04_cornet	.811
30	mobilenet_v2_1-4_224_pytorch	.762
31	blt_vs	.762
32	resnet_152_v2	.762
33	ReAlnet10	.762
34	mobilenet_v2_1_4_224	.762
35	ReAlnet10_cornet	.762
36	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.736
37	yudixie_resnet50_distance_reg_0_240908	.736
38	resnet152_imagenet_full	.736
39	convnext_tiny:in12k_ft_in1k	.691
40	yudixie_resnet18_distance_translation_0_240719	.691
41	mobilenet_v2_0_5_192	.691
42	resnet50-SIN	.691
43	cvt_cvt-21-384-in1k_4_LucyV4	.691
44	voneresnet-50	.691
45	yudixie_resnet18_translation_reg_0_240719	.691
46	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.691
47	resnet50-moclr8deg	.691
48	convnext_tiny_imagenet_full_seed-0	.667
49	deit_small_imagenet_100_seed-0	.667
50	resnet50-barlow	.667
51	yudixie_resnet18_distance_rotation_0_240719	.667
52	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.627
53	ReAlnet03	.627
54	resnet_50_v2	.627
55	BiT-S-R152x2	.627
56	ReAlnet03_cornet	.627
57	vonegrcnn_52e_full	.627
58	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.627
59	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.627
60	resnet50_ecoset_full	.627
61	ReAlnet08	.627
62	vit_large_patch14_clip_224:laion2b_ft_in1k	.627
63	ReAlnet08_cornet	.627
64	densenet-201	.627
65	resnet_101_v1	.627
66	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.627
67	resnext101_32x32d_wsl	.627
68	resnet50-SIN_IN	.606
69	deit_large_imagenet_full_seed-0	.606
70	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.569
71	AdvProp_efficientnet-b7	.569
72	AdvProp_efficientnet-b6	.569
73	Res2Net50_26w_4s	.569
74	vit_relpos_base_patch16_clsgap_224:sw_in1k	.569
75	alexnet_training_seed_01	.569
76	resnet50_robust_l2_eps3	.569
77	imagenet_l2_3_0	.569
78	alexnet_training_seed_03	.549
79	mobilenet_v2_1_3_224	.549
80	resnet50_imagenet_full	.549
81	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.516
82	convnext_small_imagenet_1_seed-0	.516
83	yudixie_resnet50_object_class_0_240908	.516
84	effnetb1_272x240	.516
85	mobilenet_v2_1_0_160	.516
86	vit_base_patch16_clip_224:openai_ft_in1k	.516
87	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.516
88	resnet-50-robust	.516
89	nasnet_mobile	.516
90	ReAlnet01_cornet	.468
91	ReAlnet_incorrect	.468
92	vit_large_patch14_clip_336:laion2b_ft_in1k	.468
93	ReAlnet01	.468
94	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.468
95	grcnn	.468
96	AdvProp_efficientnet-b8	.468
97	deit_base_imagenet_full_seed-0	.452
98	yudixie_resnet18_distance_reg_0_240719	.425
99	alexnet_training_seed_05	.425
100	alexnet_training_seed_06	.425
101	mobilenet_v2_0_75_224	.425
102	resnet50_tutorial	.425
103	deit_small_imagenet_1_seed-0	.425
104	grcnn_109	.425
105	yudixie_resnet50_imagenet1kpret_0_240908	.425
106	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.385
107	resnet_101_v2	.385
108	convnext_large:fb_in22k_ft_in1k	.385
109	convnext_xlarge:fb_in22k_ft_in1k	.385
110	AdvProp_efficientnet-b4	.385
111	BiT-S-R50x1	.385
112	ReAlnet07	.385
113	mobilenet_v2_1_0_128	.385
114	convnext_small_imagenet_full_seed-0	.385
115	ReAlnet07_cornet	.385
116	deit_small_imagenet_10_seed-0	.385
117	ReAlnet05	.385
118	ReAlnet05_cornet	.385
119	yudixie_resnet50_category_class_0_240908	.349
120	custom_model_cv_18_dagger_408	.349
121	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.349
122	yudixie_resnet18_translation_rotation_0_240719	.349
123	ReAlnet06_cornet	.317
124	mobilenet_v2_0_75_192	.317
125	vonegrcnn_62e_nobn	.317
126	ReAlnet06	.317
127	yudixie_resnet18_imagenet1kpret_0_240719	.317
128	resnet50_imagenet_10_seed-0	.317
129	resnet50_robust_l2_eps1	.317
130	hmax	.317
131	resnet50-vicreg	.317
132	alexnet_training_seed_10	.317
133	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.317
134	resnext101_32x8d_wsl	.317
135	yudixie_resnet50_translation_rotation_0_240908	.306
136	yudixie_resnet18_random_0_240719	.288
137	mobilenet_v2_0_75_160	.288
138	mobilenet_v2_0_5_224	.288
139	omnivore_swinB	.288
140	convnext_base_imagenet_full_seed-0	.288
141	convnext_large_imagenet_full_seed-0	.288
142	ViT_L_32_imagenet1k	.261
143	nasnet_large	.261
144	efficientnet_b2_imagenet_full	.261
145	cvt_cvt-13-224-in1k_4_LucyV4	.261
146	efficientnet_b0_imagenet_full	.252
147	vgg_16	.237
148	alexnet_training_seed_04	.237
149	voneresnet-50-non_stochastic	.237
150	alexnet_training_seed_09	.237
151	yudixie_resnet50_distance_translation_0_240908	.237
152	mobilenet_v2_1_0_224	.237
153	resnet50-sup	.215
154	yudixie_resnet18_category_class_0_240719	.215
155	ReAlnet02_cornet	.215
156	efficientnet_b1_imagenet_full	.215
157	resnet_50_v1	.215
158	voneresnet-50-robust	.215
159	ReAlnet02	.215
160	resnet50-SIN_IN_IN	.195
161	yudixie_resnet50_rotation_reg_0_240908	.195
162	shufflenet_v2_x1_0	.195
163	omnivore_swinS	.195
164	cvt_cvt-21-224-in1k_4_LucyV4	.195
165	AdvProp_efficientnet-b2	.195
166	convnext_tiny_sup	.195
167	AT_efficientnet-b2	.195
168	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.195
169	resnet50-vicregl0p9	.177
170	yudixie_resnet18_distance_translation_rotation_0_240719	.177
171	VOneCORnet-S	.177
172	efficientnet-b7	.177
173	resnet_152_v1	.177
174	efficientnet_b0	.177
175	resnet18_imagenet_full	.171
176	BiT-S-R101x3	.160
177	resnet34_imagenet_full	.155
178	convnext_femto_ols:d1_in1k	.145
179	convnext_small_imagenet_100_seed-0	.145
180	deit_small_imagenet_full_seed-0	.141
181	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.132
182	resnet50-vitoimagevidnet8	.132
183	resnext101_32x48d_wsl	.132
184	mobilenet_v2_1_0_192	.132
185	resnet50-VITO-8deg-cc	.132
186	cvt_cvt-13-384-in1k_4_LucyV4	.120
187	focalnet_tiny_lrf_in1k	.109
188	resnet-18_test_m	.109
189	BiT-S-R152x4	.099
190	inception_v3	.089
191	resnet50-vicregl0p75	.081
192	yudixie_resnet50_distance_rotation_0_240908	.074
193	resnet101_ecoset_full	.071
194	densenet-169	.067
195	xception	.055
196	vonegrcnn_47e	.050
197	resnet_SIN_IN_FT_IN	.050
198	inception_v1	.041
199	yudixie_resnet18_rotation_reg_0_240719	.037
200	BiT-S-R50x3	.037
201	inception_v4	.028
202	BiT-S-R101x1	.013
203	pnasnet_large	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

0.86.

Note that scores are relative to this ceiling.

Data: Ferguson2024convergence

Metric: value_delta