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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	densenet-121	1.0
4	BiT-S-R50x3	1.0
5	custom_model_cv_18_dagger_408	1.0
6	mobilenet_v2_1_3_224	.973
7	yudixie_resnet50_translation_rotation_0_240908	.973
8	resnet50-vitoimagevidnet8	.973
9	resnet50-VITO-8deg-cc	.973
10	resnet_50_v1_spiking	.932
11	cv_18_dagger_408_pretrained	.882
12	nasnet_large	.882
13	resnet50-moclr8deg	.882
14	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.882
15	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.882
16	BiT-S-R152x2	.882
17	deit_small_imagenet_full_seed-0	.882
18	resnet152_imagenet_full	.882
19	alexnet	.882
20	vit_base_patch16_dinov3-lvd1689m	.845
21	vit_base_patch14_reg4_dinov2-lvd142m	.845
22	vit_base_patch14_dinov2-lvd142m	.845
23	compact_resnet50_V4	.845
24	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.845
25	vit_large_patch14_clip_336:laion2b_ft_in1k	.845
26	vit_large_patch14_clip_224:openai_ft_in1k	.845
27	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.845
28	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.845
29	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.845
30	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.845
31	vit_relpos_base_patch16_clsgap_224:sw_in1k	.845
32	vit_large_patch14_clip_224:laion2b_ft_in1k	.845
33	vit_base_patch16_clip_224:openai_ft_in1k	.845
34	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.845
35	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.845
36	convnext_large:fb_in22k_ft_in1k	.845
37	BiT-S-R152x4	.845
38	AdvProp_efficientnet-b8	.845
39	AdvProp_efficientnet-b7	.845
40	deit_large_imagenet_full_seed-0	.845
41	deit_base_imagenet_full_seed-0	.845
42	resnet101_ecoset_full	.845
43	cvt_cvt-21-384-in1k_4_LucyV4	.845
44	cvt_cvt-13-384-in1k_4_LucyV4	.845
45	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.845
46	ViT_L_32_imagenet1k	.845
47	compact_vgg19_V4	.800
48	compact_resnet50_IT	.800
49	nasnet_mobile	.800
50	BiT-S-R101x3	.800
51	AlexNet_SIN_fov12	.767
52	vonegrcnn_62e_nobn	.767
53	grcnn	.767
54	densenet-169	.767
55	alexnet_training_seed_01_fov4	.725
56	resnet_152_v1	.725
57	omnivore_swinS	.725
58	convnext_xlarge:fb_in22k_ft_in1k	.725
59	yudixie_resnet18_translation_reg_0_240719	.725
60	mobilenet_v2_1_0_128	.695
61	convnext_tiny:in12k_ft_in1k	.695
62	AdvProp_efficientnet-b6	.695
63	resnet152_ecoset_full	.695
64	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.658
65	yudixie_resnet50_random_0_240908	.630
66	yudixie_resnet18_distance_translation_0_240719	.630
67	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.630
68	resnet-50-robust	.630
69	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.630
70	deit_small_imagenet_100_seed-0	.596
71	cvt_cvt-13-224-in1k_4_LucyV4	.596
72	resnet101_imagenet_full	.596
73	yolos_tiny	.572
74	resnet50_robust_l2_eps3	.572
75	imagenet_l2_3_0	.572
76	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.572
77	alexnet_fov12	.541
78	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.541
79	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.541
80	convnext_base:clip_laiona_augreg_ft_in1k_384	.541
81	pnasnet_large_halfPrecision	.518
82	mobilenet_v2_1_4_224	.518
83	pnasnet_large	.518
84	resnet50-barlow	.518
85	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.518
86	mobilenet_v2_1-4_224_pytorch	.518
87	focalnet_tiny_lrf_in1k	.518
88	resnet-152_v2_pytorch	.518
89	compact_cornets_IT	.490
90	alexnet_training_seed_01	.490
91	resnet50-vicregl0p75	.490
92	resnet_101_v1	.470
93	BiT-S-R50x1	.470
94	BiT-S-R101x1	.470
95	cvt_cvt-21-224-in1k_4_LucyV4	.470
96	resnet50_ecoset_full	.470
97	resnet34_ecoset_full	.470
98	resnet50_imagenet_full	.470
99	effnetb1_272x240	.470
100	compact_vgg19_IT	.445
101	alexnet_fov16	.445
102	alexnet_training_seed_10	.445
103	resnext101_32x8d_wsl	.445
104	AlexNet_SIN_fov16	.426
105	ReAlnet10_cornet	.426
106	ReAlnet09_cornet	.426
107	ReAlnet10	.426
108	ReAlnet09	.426
109	resnet50-vicreg	.426
110	blt_vs	.426
111	yudixie_resnet18_random_0_240719	.426
112	pnasnet_large_pytorch	.426
113	resnext101_32x48d_wsl	.403
114	resnext101_32x32d_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	ReAlnet01_cornet_fov16	.332
120	xception	.332
121	alexnet_training_seed_07	.332
122	alexnet_training_seed_09	.332
123	ReAlnet01_cornet_fov12	.318
124	ReAlnet07_cornet	.318
125	inception_v3	.318
126	ReAlnet07	.318
127	voneresnet-50-robust	.318
128	convnext_small_imagenet_1_seed-0	.318
129	compact_resnet50_robust_V4	.301
130	AlexNet_SIN	.288
131	yudixie_resnet50_distance_rotation_0_240908	.288
132	vonegrcnn_52e_full	.288
133	antialiased-r50	.288
134	artResNet18_1	.288
135	yudixie_resnet18_distance_reg_0_240719	.288
136	yudixie_resnet18_rotation_reg_0_240719	.288
137	ReAlnet01_ALL	.261
138	resnet_50_v1_spiking_l3	.261
139	ReAlnet05_cornet	.261
140	ReAlnet05	.261
141	yudixie_resnet18_distance_translation_rotation_0_240719	.261
142	alexnet_training_seed_02	.247
143	omnivore_swinB	.247
144	ReAlnet10_ALL	.237
145	densenet-201	.237
146	efficientnet_b0	.237
147	convnext_large_imagenet_full_seed-0	.237
148	resnet50_imagenet_100_seed-0	.237
149	resnet50_imagenet_1_seed-0	.237
150	yudixie_resnet50_rotation_reg_0_240908	.215
151	vonegrcnn_47e	.215
152	efficientnet_b2_imagenet_full	.215
153	resnet34_imagenet_full	.215
154	aughost_fair_resnet18	.203
155	alexnet_training_seed_01_fov16	.203
156	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.195
157	ReAlnet02_ALL	.195
158	resnet_50_v2	.195
159	convnext_femto_ols:d1_in1k	.195
160	compact_resnet50_robust_IT	.184
161	alexnet_training_seed_05	.184
162	ReAlnet03_ALL	.177
163	AlexNet_SIN_fov4	.177
164	mobilenet_v2_0_75_160	.177
165	mobilenet_v2_0_5_224	.177
166	efficientnet-b7	.177
167	resnet50_primary_visual_cortex	.160
168	ReAlnet01_cornet_fov4	.160
169	alexnet_fov4	.160
170	ReAlnet01_cornet	.160
171	resnet_152_v2	.160
172	grcnn_109	.160
173	ReAlnet01	.160
174	convnext_small_imagenet_full_seed-0	.160
175	resnet18_imagenet_full	.160
176	compact_cornets_V4	.152
177	alexnet_training_seed_03	.152
178	deit_small_imagenet_10_seed-0	.152
179	ReAlnet02_cornet	.145
180	ReAlnet02	.145
181	resnet50-vicregl0p9	.145
182	hmax	.138
183	ReAlnet04_cornet	.132
184	ReAlnet04	.132
185	alexnet_training_seed_08	.125
186	ReAlnet04_ALL	.120
187	resnet_50_v1_spiking_l4a	.120
188	yudixie_resnet50_category_class_0_240908	.120
189	deit_small_imagenet_1_seed-0	.120
190	yudixie_resnet18_distance_rotation_0_240719	.120
191	alexnet_training_seed_01_fov12	.113
192	ReAlnet05_ALL	.108
193	Res2Net50_26w_4s	.108
194	resnet50-SIN_IN_IN	.108
195	convnext_small_imagenet_10_seed-0	.108
196	convnext_base_imagenet_full_seed-0	.108
197	briaai_rmbg_1_4	.103
198	ReAlnet06_ALL	.098
199	ReAlnet03_cornet	.098
200	inception_v4	.098
201	ReAlnet03	.098
202	yudixie_resnet18_translation_rotation_0_240719	.098
203	mobilenet_v2_0_5_192	.089
204	yudixie_resnet50_translation_reg_0_240908	.089
205	AT_efficientnet-b2	.089
206	AdvProp_efficientnet-b2	.089
207	yudixie_resnet18_object_class_0_240719	.089
208	resnet50_imagenet_10_seed-0	.089
209	resnet-18_test_m	.081
210	resnet18_ecoset_full	.081
211	resnet50_tutorial	.081
212	alexnet_training_seed_04	.076
213	ReAlnet09_ALL	.073
214	ReAlnet08_ALL	.073
215	resnet50_ewconplus_wzc	.073
216	ReAlnet08_cornet	.073
217	vgg_19	.073
218	yudixie_resnet50_distance_translation_0_240908	.073
219	ReAlnet08	.073
220	shufflenet_v2_x1_0	.073
221	resnet50-SIN	.073
222	resnet50-SIN_IN	.073
223	yudixie_resnet50_distance_translation_rotation_0_240908	.066
224	voneresnet-50	.066
225	convnext_tiny_imagenet_full_seed-0	.066
226	yudixie_resnet50_object_class_0_240908	.060
227	yudixie_resnet50_imagenet1kpret_0_240908	.060
228	ReAlnet06_cornet	.060
229	vgg_16	.060
230	ReAlnet06	.060
231	VOneCORnet-S	.060
232	convnext_tiny_sup	.060
233	yudixie_resnet50_distance_reg_0_240908	.055
234	mobilenet_v2_0_75_224	.055
235	convnext_small_imagenet_100_seed-0	.055
236	yudixie_resnet18_imagenet1kpret_0_240719	.055
237	alexnet_training_seed_06	.052
238	mobilenet_v2_1_0_160	.050
239	yudixie_resnet18_category_class_0_240719	.050
240	voneresnet-50-non_stochastic	.050
241	inception_v1	.041
242	efficientnet_b1_imagenet_full	.041
243	efficientnet_b0_imagenet_full	.041
244	mobilenet_v2_1_0_224	.037
245	resnet_50_v1	.034
246	resnet50-sup	.034
247	AdvProp_efficientnet-b4	.028
248	mobilenet_v2_0_75_192	.025
249	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