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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	ReAlnet09	1.0
3	pnasnet_large_pytorch	1.0
4	resnet101_imagenet_full	1.0
5	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	1.0
6	ReAlnet09_cornet	1.0
7	convnext_small_imagenet_10_seed-0	1.0
8	compact_resnet50_robust_V4	1.0
9	resnet34_ecoset_full	1.0
10	yudixie_resnet50_random_0_240908	1.0
11	densenet-121	1.0
12	resnet-152_v2_pytorch	1.0
13	resnet50_imagenet_100_seed-0	1.0
14	alexnet_training_seed_07	1.0
15	resnet50_imagenet_1_seed-0	1.0
16	artResNet18_1	1.0
17	yudixie_resnet50_distance_translation_rotation_0_240908	1.0
18	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.985
19	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.985
20	resnet152_ecoset_full	.985
21	alexnet_training_seed_02	.925
22	alexnet_training_seed_08	.925
23	yudixie_resnet18_object_class_0_240719	.894
24	vit_large_patch14_clip_224:openai_ft_in1k	.839
25	yudixie_resnet50_translation_reg_0_240908	.839
26	vgg_19	.839
27	compact_cornets_IT	.839
28	antialiased-r50	.839
29	convnext_base:clip_laiona_augreg_ft_in1k_384	.839
30	resnet_50_v1_spiking_l4	.811
31	ReAlnet04_cornet	.811
32	ReAlnet04	.811
33	ReAlnet10	.762
34	blt_vs	.762
35	mobilenet_v2_1-4_224_pytorch	.762
36	resnet_50_v1_spiking_l3	.762
37	mobilenet_v2_1_4_224	.762
38	ReAlnet10_cornet	.762
39	ReAlnet07_ALL	.762
40	resnet_152_v2	.762
41	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.736
42	yudixie_resnet50_distance_reg_0_240908	.736
43	resnet152_imagenet_full	.736
44	cvt_cvt-21-384-in1k_4_LucyV4	.691
45	convnext_tiny:in12k_ft_in1k	.691
46	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.691
47	mobilenet_v2_0_5_192	.691
48	resnet50-SIN	.691
49	yudixie_resnet18_distance_translation_0_240719	.691
50	yudixie_resnet18_translation_reg_0_240719	.691
51	resnet50-moclr8deg	.691
52	voneresnet-50	.691
53	deit_small_imagenet_100_seed-0	.667
54	convnext_tiny_imagenet_full_seed-0	.667
55	yudixie_resnet18_distance_rotation_0_240719	.667
56	resnet50-barlow	.667
57	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.627
58	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.627
59	ReAlnet03	.627
60	ReAlnet08	.627
61	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.627
62	densenet-201	.627
63	ReAlnet03_cornet	.627
64	resnet50_ecoset_full	.627
65	vit_large_patch14_clip_224:laion2b_ft_in1k	.627
66	ReAlnet08_cornet	.627
67	vonegrcnn_52e_full	.627
68	BiT-S-R152x2	.627
69	resnet_101_v1	.627
70	resnet_50_v2	.627
71	resnext101_32x32d_wsl	.627
72	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.627
73	deit_large_imagenet_full_seed-0	.606
74	resnet50-SIN_IN	.606
75	AdvProp_efficientnet-b6	.569
76	alexnet_training_seed_01	.569
77	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.569
78	vit_relpos_base_patch16_clsgap_224:sw_in1k	.569
79	Res2Net50_26w_4s	.569
80	AdvProp_efficientnet-b7	.569
81	imagenet_l2_3_0	.569
82	resnet50_robust_l2_eps3	.569
83	resnet50_imagenet_full	.549
84	mobilenet_v2_1_3_224	.549
85	alexnet_training_seed_03	.549
86	yudixie_resnet50_object_class_0_240908	.516
87	mobilenet_v2_1_0_160	.516
88	convnext_small_imagenet_1_seed-0	.516
89	effnetb1_272x240	.516
90	resnet-50-robust	.516
91	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.516
92	vit_base_patch16_clip_224:openai_ft_in1k	.516
93	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.516
94	nasnet_mobile	.516
95	vit_large_patch14_clip_336:laion2b_ft_in1k	.468
96	ReAlnet01_cornet	.468
97	compact_resnet50_IT	.468
98	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.468
99	grcnn	.468
100	ReAlnet01	.468
101	AdvProp_efficientnet-b8	.468
102	deit_base_imagenet_full_seed-0	.452
103	alexnet_training_seed_05	.425
104	alexnet_training_seed_06	.425
105	mobilenet_v2_0_75_224	.425
106	resnet50_tutorial	.425
107	yudixie_resnet18_distance_reg_0_240719	.425
108	deit_small_imagenet_1_seed-0	.425
109	grcnn_109	.425
110	yudixie_resnet50_imagenet1kpret_0_240908	.425
111	resnet50_primary_visual_cortex	.425
112	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.385
113	ReAlnet05	.385
114	ReAlnet07	.385
115	convnext_xlarge:fb_in22k_ft_in1k	.385
116	mobilenet_v2_1_0_128	.385
117	convnext_small_imagenet_full_seed-0	.385
118	deit_small_imagenet_10_seed-0	.385
119	ReAlnet05_cornet	.385
120	ReAlnet07_cornet	.385
121	resnet_101_v2	.385
122	AdvProp_efficientnet-b4	.385
123	BiT-S-R50x1	.385
124	convnext_large:fb_in22k_ft_in1k	.385
125	yudixie_resnet50_category_class_0_240908	.349
126	custom_model_cv_18_dagger_408	.349
127	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.349
128	yudixie_resnet18_translation_rotation_0_240719	.349
129	ReAlnet02_ALL	.349
130	resnet50_imagenet_10_seed-0	.317
131	ReAlnet06	.317
132	alexnet_training_seed_10	.317
133	mobilenet_v2_0_75_192	.317
134	resnext101_32x8d_wsl	.317
135	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.317
136	hmax	.317
137	yudixie_resnet18_imagenet1kpret_0_240719	.317
138	ReAlnet06_cornet	.317
139	resnet50_robust_l2_eps1	.317
140	resnet50-vicreg	.317
141	vonegrcnn_62e_nobn	.317
142	yudixie_resnet50_translation_rotation_0_240908	.306
143	mobilenet_v2_0_75_160	.288
144	convnext_base_imagenet_full_seed-0	.288
145	mobilenet_v2_0_5_224	.288
146	yudixie_resnet18_random_0_240719	.288
147	convnext_large_imagenet_full_seed-0	.288
148	omnivore_swinB	.288
149	cvt_cvt-13-224-in1k_4_LucyV4	.261
150	efficientnet_b2_imagenet_full	.261
151	ViT_L_32_imagenet1k	.261
152	nasnet_large	.261
153	compact_cornets_V4	.261
154	efficientnet_b0_imagenet_full	.252
155	alexnet_training_seed_09	.237
156	alexnet_training_seed_04	.237
157	voneresnet-50-non_stochastic	.237
158	yudixie_resnet50_distance_translation_0_240908	.237
159	ReAlnet01_ALL	.237
160	vgg_16	.237
161	mobilenet_v2_1_0_224	.237
162	efficientnet_b1_imagenet_full	.215
163	ReAlnet02_cornet	.215
164	yudixie_resnet18_category_class_0_240719	.215
165	resnet_50_v1	.215
166	voneresnet-50-robust	.215
167	resnet50-sup	.215
168	ReAlnet02	.215
169	resnet50_ewconplus_wzc	.195
170	cvt_cvt-21-224-in1k_4_LucyV4	.195
171	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.195
172	resnet50-SIN_IN_IN	.195
173	yudixie_resnet50_rotation_reg_0_240908	.195
174	shufflenet_v2_x1_0	.195
175	compact_vgg19_IT	.195
176	omnivore_swinS	.195
177	AdvProp_efficientnet-b2	.195
178	convnext_tiny_sup	.195
179	AT_efficientnet-b2	.195
180	compact_resnet50_V4	.188
181	resnet_50_v1_spiking	.177
182	efficientnet_b0	.177
183	yudixie_resnet18_distance_translation_rotation_0_240719	.177
184	efficientnet-b7	.177
185	VOneCORnet-S	.177
186	ReAlnet03_ALL	.177
187	ReAlnet06_ALL	.177
188	resnet50-vicregl0p9	.177
189	resnet_152_v1	.177
190	resnet18_imagenet_full	.171
191	compact_vgg19_V4	.160
192	BiT-S-R101x3	.160
193	ReAlnet09_ALL	.160
194	resnet34_imagenet_full	.155
195	convnext_small_imagenet_100_seed-0	.145
196	ReAlnet05_ALL	.145
197	convnext_femto_ols:d1_in1k	.145
198	deit_small_imagenet_full_seed-0	.141
199	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.132
200	mobilenet_v2_1_0_192	.132
201	resnext101_32x48d_wsl	.132
202	resnet50-vitoimagevidnet8	.132
203	resnet50-VITO-8deg-cc	.132
204	cvt_cvt-13-384-in1k_4_LucyV4	.120
205	ReAlnet04_ALL	.120
206	resnet_50_v1_spiking_l4a	.109
207	focalnet_tiny_lrf_in1k	.109
208	resnet-18_test_m	.109
209	BiT-S-R152x4	.099
210	inception_v3	.089
211	ReAlnet10_ALL	.081
212	resnet50-vicregl0p75	.081
213	yolos_tiny	.074
214	yudixie_resnet50_distance_rotation_0_240908	.074
215	resnet101_ecoset_full	.071
216	compact_resnet50_robust_IT	.067
217	densenet-169	.067
218	briaai_rmbg_1_4	.061
219	xception	.055
220	vonegrcnn_47e	.050
221	resnet_SIN_IN_FT_IN	.050
222	inception_v1	.041
223	yudixie_resnet18_rotation_reg_0_240719	.037
224	BiT-S-R50x3	.037
225	ReAlnet08_ALL	.034
226	inception_v4	.028
227	BiT-S-R101x1	.013
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

0.86.

Note that scores are relative to this ceiling.

Data: Ferguson2024convergence

Metric: value_delta