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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024gray_easy-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet50_robust_l2_eps1	1.0
2	resnet_101_v2	1.0
3	BiT-S-R50x3	1.0
4	densenet-121	1.0
5	custom_model_cv_18_dagger_408	1.0
6	vit_base_patch14_reg4_dinov2:lvd142m	.973
7	resnet50-VITO-8deg-cc	.973
8	yudixie_resnet50_translation_rotation_0_240908	.973
9	resnet50-vitoimagevidnet8	.973
10	mobilenet_v2_1_3_224	.973
11	resnet_50_v1_spiking	.932
12	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.882
13	cv_18_dagger_408_pretrained	.882
14	alexnet	.882
15	resnet152_imagenet_full	.882
16	deit_small_imagenet_full_seed-0	.882
17	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.882
18	alexnet_workflow_trigger	.882
19	nasnet_large	.882
20	resnet50-moclr8deg	.882
21	BiT-S-R152x2	.882
22	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.845
23	cvt_cvt-21-384-in1k_4_LucyV4	.845
24	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.845
25	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.845
26	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.845
27	vit_large_patch14_clip_224:openai_ft_in1k	.845
28	vit_large_patch14_clip_336:laion2b_ft_in1k	.845
29	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.845
30	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.845
31	ViT_L_32_imagenet1k	.845
32	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.845
33	deit_large_imagenet_full_seed-0	.845
34	cvt_cvt-13-384-in1k_4_LucyV4	.845
35	compact_resnet50_V4	.845
36	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.845
37	deit_base_imagenet_full_seed-0	.845
38	vit_base_patch16_clip_224:openai_ft_in1k	.845
39	vit_large_patch14_clip_224:laion2b_ft_in1k	.845
40	resnet101_ecoset_full	.845
41	vit_relpos_base_patch16_clsgap_224:sw_in1k	.845
42	AdvProp_efficientnet-b7	.845
43	AdvProp_efficientnet-b8	.845
44	BiT-S-R152x4	.845
45	convnext_large:fb_in22k_ft_in1k	.845
46	vit_base_patch16_dinov3:lvd1689m	.800
47	compact_resnet50_IT	.800
48	compact_vgg19_V4	.800
49	nasnet_mobile	.800
50	BiT-S-R101x3	.800
51	AlexNet_SIN_fov12	.767
52	grcnn	.767
53	vonegrcnn_62e_nobn	.767
54	densenet-169	.767
55	convnext_xlarge:fb_in22k_ft_in1k	.725
56	yudixie_resnet18_translation_reg_0_240719	.725
57	alexnet_training_seed_01_fov4	.725
58	omnivore_swinS	.725
59	resnet_152_v1	.725
60	convnext_tiny:in12k_ft_in1k	.695
61	mobilenet_v2_1_0_128	.695
62	resnet152_ecoset_full	.695
63	AdvProp_efficientnet-b6	.695
64	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.658
65	yudixie_resnet50_random_0_240908	.630
66	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.630
67	resnet-50-robust	.630
68	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.630
69	vit_base_patch14_dinov2:lvd142m	.630
70	yudixie_resnet18_distance_translation_0_240719	.630
71	deit_small_imagenet_100_seed-0	.596
72	cvt_cvt-13-224-in1k_4_LucyV4	.596
73	resnet101_imagenet_full	.596
74	yolos_tiny	.572
75	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.572
76	imagenet_l2_3_0	.572
77	resnet50_robust_l2_eps3	.572
78	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.541
79	alexnet_fov12	.541
80	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.541
81	convnext_base:clip_laiona_augreg_ft_in1k_384	.541
82	pnasnet_large	.518
83	resnet50-barlow	.518
84	mobilenet_v2_1_4_224	.518
85	resnet-152_v2_pytorch	.518
86	focalnet_tiny_lrf_in1k	.518
87	mobilenet_v2_1-4_224_pytorch	.518
88	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.518
89	alexnet_training_seed_01	.490
90	compact_cornets_IT	.490
91	resnet50-vicregl0p75	.490
92	resnet50_imagenet_full	.470
93	cvt_cvt-21-224-in1k_4_LucyV4	.470
94	effnetb1_272x240	.470
95	resnet34_ecoset_full	.470
96	resnet50_ecoset_full	.470
97	BiT-S-R101x1	.470
98	resnet_101_v1	.470
99	BiT-S-R50x1	.470
100	resnext101_32x8d_wsl	.445
101	alexnet_training_seed_10	.445
102	alexnet_fov16	.445
103	compact_vgg19_IT	.445
104	blt_vs	.426
105	ReAlnet09	.426
106	ReAlnet10	.426
107	pnasnet_large_pytorch	.426
108	yudixie_resnet18_random_0_240719	.426
109	resnet50-vicreg	.426
110	AlexNet_SIN_fov16	.426
111	ReAlnet09_cornet	.426
112	ReAlnet10_cornet	.426
113	resnext101_32x32d_wsl	.403
114	resnext101_32x48d_wsl	.403
115	resnet_50_v1_spiking_l4	.387
116	mobilenet_v2_1_0_192	.387
117	ReAlnet07_ALL	.351
118	resnet_SIN_IN_FT_IN	.351
119	alexnet_training_seed_09	.332
120	alexnet_training_seed_07	.332
121	xception	.332
122	ReAlnet01_cornet_fov16	.332
123	ReAlnet07	.318
124	inception_v3	.318
125	convnext_small_imagenet_1_seed-0	.318
126	ReAlnet01_cornet_fov12	.318
127	ReAlnet07_cornet	.318
128	voneresnet-50-robust	.318
129	compact_resnet50_robust_V4	.301
130	AlexNet_SIN	.288
131	yudixie_resnet50_distance_rotation_0_240908	.288
132	yudixie_resnet18_rotation_reg_0_240719	.288
133	yudixie_resnet18_distance_reg_0_240719	.288
134	artResNet18_1	.288
135	antialiased-r50	.288
136	vonegrcnn_52e_full	.288
137	ReAlnet05	.261
138	ReAlnet05_cornet	.261
139	yudixie_resnet18_distance_translation_rotation_0_240719	.261
140	ReAlnet01_ALL	.261
141	resnet_50_v1_spiking_l3	.261
142	alexnet_training_seed_02	.247
143	omnivore_swinB	.247
144	densenet-201	.237
145	resnet50_imagenet_100_seed-0	.237
146	convnext_large_imagenet_full_seed-0	.237
147	resnet50_imagenet_1_seed-0	.237
148	efficientnet_b0	.237
149	ReAlnet10_ALL	.237
150	resnet34_imagenet_full	.215
151	efficientnet_b2_imagenet_full	.215
152	yudixie_resnet50_rotation_reg_0_240908	.215
153	vonegrcnn_47e	.215
154	alexnet_training_seed_01_fov16	.203
155	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.195
156	ReAlnet02_ALL	.195
157	convnext_femto_ols:d1_in1k	.195
158	resnet_50_v2	.195
159	alexnet_training_seed_05	.184
160	compact_resnet50_robust_IT	.184
161	mobilenet_v2_0_5_224	.177
162	mobilenet_v2_0_75_160	.177
163	efficientnet-b7	.177
164	AlexNet_SIN_fov4	.177
165	ReAlnet03_ALL	.177
166	alexnet_fov4	.160
167	convnext_small_imagenet_full_seed-0	.160
168	ReAlnet01_cornet	.160
169	resnet18_imagenet_full	.160
170	ReAlnet01_cornet_fov4	.160
171	grcnn_109	.160
172	ReAlnet01	.160
173	resnet_152_v2	.160
174	resnet50_primary_visual_cortex	.160
175	deit_small_imagenet_10_seed-0	.152
176	compact_cornets_V4	.152
177	alexnet_training_seed_03	.152
178	ReAlnet02_cornet	.145
179	ReAlnet02	.145
180	resnet50-vicregl0p9	.145
181	hmax	.138
182	ReAlnet04_cornet	.132
183	ReAlnet04	.132
184	alexnet_training_seed_08	.125
185	yudixie_resnet50_category_class_0_240908	.120
186	resnet_50_v1_spiking_l4a	.120
187	yudixie_resnet18_distance_rotation_0_240719	.120
188	deit_small_imagenet_1_seed-0	.120
189	ReAlnet04_ALL	.120
190	alexnet_training_seed_01_fov12	.113
191	convnext_small_imagenet_10_seed-0	.108
192	convnext_base_imagenet_full_seed-0	.108
193	resnet50-SIN_IN_IN	.108
194	Res2Net50_26w_4s	.108
195	ReAlnet05_ALL	.108
196	briaai_rmbg_1_4	.103
197	ReAlnet03	.098
198	ReAlnet03_cornet	.098
199	inception_v4	.098
200	yudixie_resnet18_translation_rotation_0_240719	.098
201	ReAlnet06_ALL	.098
202	resnet50_imagenet_10_seed-0	.089
203	mobilenet_v2_0_5_192	.089
204	yudixie_resnet50_translation_reg_0_240908	.089
205	yudixie_resnet18_object_class_0_240719	.089
206	AdvProp_efficientnet-b2	.089
207	AT_efficientnet-b2	.089
208	resnet50_tutorial	.081
209	resnet18_ecoset_full	.081
210	resnet-18_test_m	.081
211	alexnet_training_seed_04	.076
212	resnet50_ewconplus_wzc	.073
213	ReAlnet08	.073
214	resnet50-SIN_IN	.073
215	resnet50-SIN	.073
216	yudixie_resnet50_distance_translation_0_240908	.073
217	shufflenet_v2_x1_0	.073
218	ReAlnet08_cornet	.073
219	vgg_19	.073
220	ReAlnet08_ALL	.073
221	ReAlnet09_ALL	.073
222	convnext_tiny_imagenet_full_seed-0	.066
223	voneresnet-50	.066
224	yudixie_resnet50_distance_translation_rotation_0_240908	.066
225	yudixie_resnet50_imagenet1kpret_0_240908	.060
226	ReAlnet06	.060
227	yudixie_resnet50_object_class_0_240908	.060
228	VOneCORnet-S	.060
229	ReAlnet06_cornet	.060
230	vgg_16	.060
231	convnext_tiny_sup	.060
232	mobilenet_v2_0_75_224	.055
233	yudixie_resnet50_distance_reg_0_240908	.055
234	convnext_small_imagenet_100_seed-0	.055
235	yudixie_resnet18_imagenet1kpret_0_240719	.055
236	alexnet_training_seed_06	.052
237	mobilenet_v2_1_0_160	.050
238	yudixie_resnet18_category_class_0_240719	.050
239	voneresnet-50-non_stochastic	.050
240	efficientnet_b1_imagenet_full	.041
241	efficientnet_b0_imagenet_full	.041
242	inception_v1	.041
243	mobilenet_v2_1_0_224	.037
244	resnet_50_v1	.034
245	resnet50-sup	.034
246	AdvProp_efficientnet-b4	.028
247	mobilenet_v2_0_75_192	.025
248	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.019

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

0.91.

Note that scores are relative to this ceiling.

Data: Ferguson2024gray_easy

Metric: value_delta