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