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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	AlexNet_SIN_fov4	1.0
11	alexnet_training_seed_01_fov4	1.0
12	yudixie_resnet50_random_0_240908	1.0
13	densenet-121	1.0
14	resnet-152_v2_pytorch	1.0
15	resnet50_imagenet_100_seed-0	1.0
16	alexnet_training_seed_07	1.0
17	resnet50_imagenet_1_seed-0	1.0
18	AlexNet_SIN_fov16	1.0
19	artResNet18_1	1.0
20	yudixie_resnet50_distance_translation_rotation_0_240908	1.0
21	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.985
22	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.985
23	resnet152_ecoset_full	.985
24	alexnet_training_seed_02	.925
25	alexnet_training_seed_08	.925
26	yudixie_resnet18_object_class_0_240719	.894
27	vit_large_patch14_clip_224:openai_ft_in1k	.839
28	yudixie_resnet50_translation_reg_0_240908	.839
29	vgg_19	.839
30	compact_cornets_IT	.839
31	antialiased-r50	.839
32	convnext_base:clip_laiona_augreg_ft_in1k_384	.839
33	resnet_50_v1_spiking_l4	.811
34	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.811
35	ReAlnet04_cornet	.811
36	ReAlnet04	.811
37	ReAlnet10	.762
38	blt_vs	.762
39	mobilenet_v2_1-4_224_pytorch	.762
40	resnet_50_v1_spiking_l3	.762
41	mobilenet_v2_1_4_224	.762
42	ReAlnet10_cornet	.762
43	ReAlnet07_ALL	.762
44	resnet_152_v2	.762
45	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.736
46	yudixie_resnet50_distance_reg_0_240908	.736
47	resnet152_imagenet_full	.736
48	cvt_cvt-21-384-in1k_4_LucyV4	.691
49	convnext_tiny:in12k_ft_in1k	.691
50	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.691
51	mobilenet_v2_0_5_192	.691
52	resnet50-SIN	.691
53	yudixie_resnet18_distance_translation_0_240719	.691
54	yudixie_resnet18_translation_reg_0_240719	.691
55	resnet50-moclr8deg	.691
56	voneresnet-50	.691
57	deit_small_imagenet_100_seed-0	.667
58	convnext_tiny_imagenet_full_seed-0	.667
59	yudixie_resnet18_distance_rotation_0_240719	.667
60	resnet50-barlow	.667
61	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.627
62	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.627
63	ReAlnet03	.627
64	ReAlnet08	.627
65	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.627
66	densenet-201	.627
67	ReAlnet03_cornet	.627
68	alexnet_fov16	.627
69	resnet50_ecoset_full	.627
70	vit_large_patch14_clip_224:laion2b_ft_in1k	.627
71	ReAlnet08_cornet	.627
72	vonegrcnn_52e_full	.627
73	BiT-S-R152x2	.627
74	resnet_101_v1	.627
75	resnet_50_v2	.627
76	resnext101_32x32d_wsl	.627
77	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.627
78	deit_large_imagenet_full_seed-0	.606
79	resnet50-SIN_IN	.606
80	AdvProp_efficientnet-b6	.569
81	alexnet_training_seed_01	.569
82	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.569
83	vit_relpos_base_patch16_clsgap_224:sw_in1k	.569
84	Res2Net50_26w_4s	.569
85	AdvProp_efficientnet-b7	.569
86	imagenet_l2_3_0	.569
87	resnet50_robust_l2_eps3	.569
88	resnet50_imagenet_full	.549
89	mobilenet_v2_1_3_224	.549
90	alexnet_training_seed_03	.549
91	yudixie_resnet50_object_class_0_240908	.516
92	mobilenet_v2_1_0_160	.516
93	convnext_small_imagenet_1_seed-0	.516
94	effnetb1_272x240	.516
95	resnet-50-robust	.516
96	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.516
97	vit_base_patch16_clip_224:openai_ft_in1k	.516
98	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.516
99	nasnet_mobile	.516
100	vit_large_patch14_clip_336:laion2b_ft_in1k	.468
101	ReAlnet01_cornet	.468
102	compact_resnet50_IT	.468
103	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.468
104	grcnn	.468
105	ReAlnet01	.468
106	AdvProp_efficientnet-b8	.468
107	deit_base_imagenet_full_seed-0	.452
108	alexnet_training_seed_05	.425
109	alexnet_training_seed_06	.425
110	mobilenet_v2_0_75_224	.425
111	alexnet_fov12	.425
112	pnasnet_large_halfPrecision	.425
113	resnet50_tutorial	.425
114	yudixie_resnet18_distance_reg_0_240719	.425
115	deit_small_imagenet_1_seed-0	.425
116	grcnn_109	.425
117	yudixie_resnet50_imagenet1kpret_0_240908	.425
118	resnet50_primary_visual_cortex	.425
119	ReAlnet01_cornet_fov12	.410
120	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.385
121	ReAlnet05	.385
122	ReAlnet07	.385
123	convnext_xlarge:fb_in22k_ft_in1k	.385
124	mobilenet_v2_1_0_128	.385
125	convnext_small_imagenet_full_seed-0	.385
126	deit_small_imagenet_10_seed-0	.385
127	vit_base_patch14_reg4_dinov2-lvd142m	.385
128	ReAlnet05_cornet	.385
129	ReAlnet07_cornet	.385
130	resnet_101_v2	.385
131	AdvProp_efficientnet-b4	.385
132	BiT-S-R50x1	.385
133	convnext_large:fb_in22k_ft_in1k	.385
134	yudixie_resnet50_category_class_0_240908	.349
135	vit_base_patch16_dinov3-lvd1689m	.349
136	custom_model_cv_18_dagger_408	.349
137	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.349
138	ReAlnet01_cornet_fov4	.349
139	yudixie_resnet18_translation_rotation_0_240719	.349
140	aughost_fair_resnet18	.349
141	ReAlnet02_ALL	.349
142	resnet50_imagenet_10_seed-0	.317
143	ReAlnet06	.317
144	alexnet_training_seed_10	.317
145	mobilenet_v2_0_75_192	.317
146	resnext101_32x8d_wsl	.317
147	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.317
148	hmax	.317
149	vit_base_patch14_dinov2-lvd142m	.317
150	yudixie_resnet18_imagenet1kpret_0_240719	.317
151	ReAlnet06_cornet	.317
152	resnet50_robust_l2_eps1	.317
153	resnet50-vicreg	.317
154	vonegrcnn_62e_nobn	.317
155	yudixie_resnet50_translation_rotation_0_240908	.306
156	mobilenet_v2_0_75_160	.288
157	convnext_base_imagenet_full_seed-0	.288
158	mobilenet_v2_0_5_224	.288
159	yudixie_resnet18_random_0_240719	.288
160	convnext_large_imagenet_full_seed-0	.288
161	omnivore_swinB	.288
162	alexnet_training_seed_01_fov16	.288
163	cvt_cvt-13-224-in1k_4_LucyV4	.261
164	efficientnet_b2_imagenet_full	.261
165	ViT_L_32_imagenet1k	.261
166	nasnet_large	.261
167	compact_cornets_V4	.261
168	efficientnet_b0_imagenet_full	.252
169	alexnet_training_seed_09	.237
170	alexnet_training_seed_04	.237
171	voneresnet-50-non_stochastic	.237
172	yudixie_resnet50_distance_translation_0_240908	.237
173	ReAlnet01_cornet_fov16	.237
174	ReAlnet01_ALL	.237
175	vgg_16	.237
176	mobilenet_v2_1_0_224	.237
177	efficientnet_b1_imagenet_full	.215
178	ReAlnet02_cornet	.215
179	yudixie_resnet18_category_class_0_240719	.215
180	resnet_50_v1	.215
181	voneresnet-50-robust	.215
182	resnet50-sup	.215
183	ReAlnet02	.215
184	resnet50_ewconplus_wzc	.195
185	cvt_cvt-21-224-in1k_4_LucyV4	.195
186	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.195
187	resnet50-SIN_IN_IN	.195
188	yudixie_resnet50_rotation_reg_0_240908	.195
189	shufflenet_v2_x1_0	.195
190	compact_vgg19_IT	.195
191	omnivore_swinS	.195
192	AdvProp_efficientnet-b2	.195
193	convnext_tiny_sup	.195
194	AT_efficientnet-b2	.195
195	compact_resnet50_V4	.188
196	resnet_50_v1_spiking	.177
197	efficientnet_b0	.177
198	yudixie_resnet18_distance_translation_rotation_0_240719	.177
199	efficientnet-b7	.177
200	VOneCORnet-S	.177
201	ReAlnet03_ALL	.177
202	ReAlnet06_ALL	.177
203	resnet50-vicregl0p9	.177
204	resnet_152_v1	.177
205	resnet18_imagenet_full	.171
206	AlexNet_SIN	.160
207	cv_18_dagger_408_pretrained	.160
208	compact_vgg19_V4	.160
209	BiT-S-R101x3	.160
210	ReAlnet09_ALL	.160
211	resnet34_imagenet_full	.155
212	convnext_small_imagenet_100_seed-0	.145
213	ReAlnet05_ALL	.145
214	convnext_femto_ols:d1_in1k	.145
215	deit_small_imagenet_full_seed-0	.141
216	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.132
217	mobilenet_v2_1_0_192	.132
218	resnext101_32x48d_wsl	.132
219	resnet50-vitoimagevidnet8	.132
220	resnet50-VITO-8deg-cc	.132
221	cvt_cvt-13-384-in1k_4_LucyV4	.120
222	ReAlnet04_ALL	.120
223	resnet_50_v1_spiking_l4a	.109
224	focalnet_tiny_lrf_in1k	.109
225	resnet-18_test_m	.109
226	BiT-S-R152x4	.099
227	inception_v3	.089
228	ReAlnet10_ALL	.081
229	resnet50-vicregl0p75	.081
230	yolos_tiny	.074
231	yudixie_resnet50_distance_rotation_0_240908	.074
232	resnet101_ecoset_full	.071
233	AlexNet_SIN_fov12	.067
234	compact_resnet50_robust_IT	.067
235	densenet-169	.067
236	briaai_rmbg_1_4	.061
237	xception	.055
238	alexnet_fov4	.050
239	vonegrcnn_47e	.050
240	resnet_SIN_IN_FT_IN	.050
241	alexnet_training_seed_01_fov12	.044
242	inception_v1	.041
243	yudixie_resnet18_rotation_reg_0_240719	.037
244	BiT-S-R50x3	.037
245	ReAlnet08_ALL	.034
246	inception_v4	.028
247	alexnet	.028
248	BiT-S-R101x1	.013
249	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