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	resnet18_ecoset_full	1.0
2	pnasnet_large_pytorch	1.0
3	ReAlnet09	1.0
4	ReAlnet09_cornet	1.0
5	resnet101_imagenet_full	1.0
6	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	1.0
7	convnext_small_imagenet_10_seed-0	1.0
8	resnet34_ecoset_full	1.0
9	densenet-121	1.0
10	yudixie_resnet50_random_0_240908	1.0
11	resnet-152_v2_pytorch	1.0
12	artResNet18_1	1.0
13	alexnet_training_seed_07	1.0
14	resnet50_imagenet_1_seed-0	1.0
15	resnet50_imagenet_100_seed-0	1.0
16	yudixie_resnet50_distance_translation_rotation_0_240908	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_08	.925
21	alexnet_training_seed_02	.925
22	yudixie_resnet18_object_class_0_240719	.894
23	vgg_19	.839
24	vit_large_patch14_clip_224:openai_ft_in1k	.839
25	yudixie_resnet50_translation_reg_0_240908	.839
26	antialiased-r50	.839
27	convnext_base:clip_laiona_augreg_ft_in1k_384	.839
28	ReAlnet04_cornet	.811
29	resnet_50_v1_spiking_l4	.811
30	ReAlnet04	.811
31	resnet_152_v2	.762
32	resnet_50_v1_spiking_l3	.762
33	ReAlnet10	.762
34	blt_vs	.762
35	ReAlnet10_cornet	.762
36	mobilenet_v2_1-4_224_pytorch	.762
37	mobilenet_v2_1_4_224	.762
38	resnet152_imagenet_full	.736
39	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.736
40	yudixie_resnet50_distance_reg_0_240908	.736
41	resnet50-SIN	.691
42	yudixie_resnet18_distance_translation_0_240719	.691
43	convnext_tiny:in12k_ft_in1k	.691
44	yudixie_resnet18_translation_reg_0_240719	.691
45	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.691
46	resnet50-moclr8deg	.691
47	voneresnet-50	.691
48	mobilenet_v2_0_5_192	.691
49	cvt_cvt-21-384-in1k_4_LucyV4	.691
50	yudixie_resnet18_distance_rotation_0_240719	.667
51	resnet50-barlow	.667
52	convnext_tiny_imagenet_full_seed-0	.667
53	deit_small_imagenet_100_seed-0	.667
54	resnet_101_v1	.627
55	densenet-201	.627
56	ReAlnet08_cornet	.627
57	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.627
58	ReAlnet08	.627
59	vonegrcnn_52e_full	.627
60	ReAlnet03	.627
61	BiT-S-R152x2	.627
62	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.627
63	resnet_50_v2	.627
64	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.627
65	vit_large_patch14_clip_224:laion2b_ft_in1k	.627
66	resnet50_ecoset_full	.627
67	ReAlnet03_cornet	.627
68	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.627
69	resnext101_32x32d_wsl	.627
70	deit_large_imagenet_full_seed-0	.606
71	resnet50-SIN_IN	.606
72	imagenet_l2_3_0	.569
73	AdvProp_efficientnet-b7	.569
74	vit_relpos_base_patch16_clsgap_224:sw_in1k	.569
75	Res2Net50_26w_4s	.569
76	resnet50_robust_l2_eps3	.569
77	AdvProp_efficientnet-b6	.569
78	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.569
79	alexnet_training_seed_01	.569
80	mobilenet_v2_1_3_224	.549
81	resnet50_imagenet_full	.549
82	alexnet_training_seed_03	.549
83	yudixie_resnet50_object_class_0_240908	.516
84	mobilenet_v2_1_0_160	.516
85	convnext_small_imagenet_1_seed-0	.516
86	effnetb1_272x240	.516
87	resnet-50-robust	.516
88	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.516
89	vit_base_patch16_clip_224:openai_ft_in1k	.516
90	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.516
91	nasnet_mobile	.516
92	ReAlnet01	.468
93	vit_large_patch14_clip_336:laion2b_ft_in1k	.468
94	ReAlnet01_cornet	.468
95	grcnn	.468
96	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.468
97	AdvProp_efficientnet-b8	.468
98	deit_base_imagenet_full_seed-0	.452
99	yudixie_resnet18_distance_reg_0_240719	.425
100	grcnn_109	.425
101	resnet50_tutorial	.425
102	alexnet_training_seed_05	.425
103	alexnet_training_seed_06	.425
104	resnet50_primary_visual_cortex	.425
105	mobilenet_v2_0_75_224	.425
106	yudixie_resnet50_imagenet1kpret_0_240908	.425
107	deit_small_imagenet_1_seed-0	.425
108	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.385
109	deit_small_imagenet_10_seed-0	.385
110	convnext_large:fb_in22k_ft_in1k	.385
111	BiT-S-R50x1	.385
112	ReAlnet05	.385
113	ReAlnet07	.385
114	convnext_xlarge:fb_in22k_ft_in1k	.385
115	AdvProp_efficientnet-b4	.385
116	mobilenet_v2_1_0_128	.385
117	ReAlnet05_cornet	.385
118	resnet_101_v2	.385
119	convnext_small_imagenet_full_seed-0	.385
120	ReAlnet07_cornet	.385
121	yudixie_resnet50_category_class_0_240908	.349
122	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.349
123	custom_model_cv_18_dagger_408	.349
124	yudixie_resnet18_translation_rotation_0_240719	.349
125	resnet50_imagenet_10_seed-0	.317
126	vonegrcnn_62e_nobn	.317
127	resnext101_32x8d_wsl	.317
128	mobilenet_v2_0_75_192	.317
129	hmax	.317
130	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.317
131	ReAlnet06_cornet	.317
132	resnet50-vicreg	.317
133	alexnet_training_seed_10	.317
134	resnet50_robust_l2_eps1	.317
135	ReAlnet06	.317
136	yudixie_resnet18_imagenet1kpret_0_240719	.317
137	yudixie_resnet50_translation_rotation_0_240908	.306
138	yudixie_resnet18_random_0_240719	.288
139	omnivore_swinB	.288
140	convnext_large_imagenet_full_seed-0	.288
141	mobilenet_v2_0_5_224	.288
142	convnext_base_imagenet_full_seed-0	.288
143	mobilenet_v2_0_75_160	.288
144	cvt_cvt-13-224-in1k_4_LucyV4	.261
145	ViT_L_32_imagenet1k	.261
146	efficientnet_b2_imagenet_full	.261
147	nasnet_large	.261
148	efficientnet_b0_imagenet_full	.252
149	alexnet_training_seed_04	.237
150	alexnet_training_seed_09	.237
151	yudixie_resnet50_distance_translation_0_240908	.237
152	voneresnet-50-non_stochastic	.237
153	mobilenet_v2_1_0_224	.237
154	vgg_16	.237
155	resnet_50_v1	.215
156	resnet50-sup	.215
157	efficientnet_b1_imagenet_full	.215
158	yudixie_resnet18_category_class_0_240719	.215
159	ReAlnet02	.215
160	ReAlnet02_cornet	.215
161	voneresnet-50-robust	.215
162	AdvProp_efficientnet-b2	.195
163	resnet50-SIN_IN_IN	.195
164	omnivore_swinS	.195
165	convnext_tiny_sup	.195
166	shufflenet_v2_x1_0	.195
167	AT_efficientnet-b2	.195
168	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.195
169	yudixie_resnet50_rotation_reg_0_240908	.195
170	cvt_cvt-21-224-in1k_4_LucyV4	.195
171	VOneCORnet-S	.177
172	efficientnet-b7	.177
173	yudixie_resnet18_distance_translation_rotation_0_240719	.177
174	efficientnet_b0	.177
175	resnet_50_v1_spiking	.177
176	resnet50-vicregl0p9	.177
177	resnet_152_v1	.177
178	resnet18_imagenet_full	.171
179	BiT-S-R101x3	.160
180	resnet34_imagenet_full	.155
181	convnext_small_imagenet_100_seed-0	.145
182	convnext_femto_ols:d1_in1k	.145
183	deit_small_imagenet_full_seed-0	.141
184	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.132
185	resnet50-VITO-8deg-cc	.132
186	resnext101_32x48d_wsl	.132
187	resnet50-vitoimagevidnet8	.132
188	mobilenet_v2_1_0_192	.132
189	cvt_cvt-13-384-in1k_4_LucyV4	.120
190	resnet_50_v1_spiking_l4a	.109
191	focalnet_tiny_lrf_in1k	.109
192	resnet-18_test_m	.109
193	BiT-S-R152x4	.099
194	inception_v3	.089
195	resnet50-vicregl0p75	.081
196	yolos_tiny	.074
197	yudixie_resnet50_distance_rotation_0_240908	.074
198	resnet101_ecoset_full	.071
199	densenet-169	.067
200	briaai_rmbg_1_4	.061
201	xception	.055
202	resnet_SIN_IN_FT_IN	.050
203	vonegrcnn_47e	.050
204	inception_v1	.041
205	BiT-S-R50x3	.037
206	yudixie_resnet18_rotation_reg_0_240719	.037
207	inception_v4	.028
208	BiT-S-R101x1	.013
209	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

0.86.

Note that scores are relative to this ceiling.

Data: Ferguson2024convergence

Metric: value_delta